Conference Agenda

The EPSRC Centre for Doctoral Training (CDT) in Geospatial Systems was established in 2019 with a vision to establish an internationally recognised centre of excellence and an ambition to graduate 50 doctoral students across five annual cohort intakes. Since that time, the CDT has been delivered through a strategic partnership between Newcastle University and the University of Nottingham in the UK, together with c. 40 external partners from global academia, international industry and UK Government. The first doctoral students graduated from the CDT in July 2024, with the final students expected to complete their PhD studies in 2028. This paper provides an overview of the training structure and skills development initiatives implemented and offers critical reflections on the experiences and challenges encountered throughout the CDT’s lifetime to date (February 2026). While the content will be of particular interest to academics and stakeholders involved in any branch of geospatial doctoral training, many of the findings are transferable. As such, the insights presented may also be of value to the wider academic community, particularly those considering the establishment of similar cohort-based doctoral training models.

The CO3D mission, launched in July 2025, aims to reconstruct the Earth’s continental surface in 3D using pairs of synchronous satellite images, generating a Digital Surface Model (DSM) at 1 m Ground Sampling Distance (GSD). Assessing the quality of these DSMs requires an inter-DSM comparison tool, leading CNES to collaborate with the GlacioHack collective and join the governance of their open-source software xDEM.

Originally developed for glacier research, xDEM already offered valuable features for DEM analysis including coregistration, uncertainty analysis, geomorphological terrain attributes computation, etc. Recognizing its potential, CNES made the strategic choice to no longer maintain its own tool and instead contribute to xDEM. The main contributions include the ability to rapidly obtain statistics, scalability improvements through tiling, and the introduction of a command-line interface.

This collaboration has created a more robust tool that benefits both the CO3D mission and the broader scientific community. By combining resources and expertise, the project demonstrates how open-source development can drive innovation while reducing duplication of effort.

DINAMIS is a French national initiative designed to provide streamlined, cost-effective access to very high-resolution satellite imagery for research, public policy, and innovation. Coordinated by major public institutions—including CNES, IGN, INRAE, and several academic partners—DINAMIS acts as a single entry point for users who need high-quality Earth-observation data to support scientific studies, environmental monitoring, land-use analysis, and operational pu-blic-sector missions.

The platform facilitates access to a range of commercial satellite constellations, most notably Pléiades, Pléiades Neo, and SPOT 6/7, which offer imagery with spatial resolutions from sub-meter to a few meters. Users can request both archived scenes and new acquisitions, enabling them to obtain data tailored to their geographic area and temporal needs. DINAMIS also pro-vides standardized licensing conditions that simplify data sharing within research teams and public organizations.

A key objective of DINAMIS is to democratize the use of very high-resolution imagery by re-ducing financial barriers. Academic and public-interest projects often benefit from free or highly subsidized access, encouraging the development of innovative applications in fields such as agriculture, forestry, natural hazards, coastal management, and urban planning.

By centralizing requests, ensuring data quality, and supporting users throughout the process, DINAMIS strengthens France’s Earth-observation ecosystem and fosters collaboration between scientists, government agencies, and technology developers. Ultimately, DINAMIS contributes to a more informed understanding of the environment and helps public authorities make evi-dence-based decisions for sustainable territory management.

The “Green & Pink for Sustainable Education” (TNE-GPSEducation) project strengthens international cooperation between ten Italian universities and partner institutions worldwide, promoting multidisciplinary training in sustainability. The initiative integrates expertise in natural resource monitoring, socio-environmental resilience, innovative teaching, health, and gender equality. Partner countries—including Brazil, Argentina, Cambodia, Thailand, Palestine, Georgia, Morocco, China, and Vietnam—play strategic cultural and academic roles and are central to recent international efforts to foster joint education, research, and innovation. Through mobility and capacity-building actions, lecturers, staff, and students enhance their skills while acquiring transferable competencies usable across institutions. Italy’s broader cooperation policies, aligned with UN, EU, and CRUI–CUCS strategies, further support partnerships such as the MoUs signed by POLIMI with ENSMR and UIR in Morocco.

Within this framework, WP4 “Advanced Skills” represents the project’s core, merging socioeconomics, Earth Observation (EO), Nature-Based Solutions (NBS), and health. Five Long Life Learning Courses have been modularised to establish an EO Hub at ENSMR, serving as a regional network node. A Call for Applications invites professors and researchers to attend AS-LLLC programmes at POLIMI, covering EO techniques, BIM–XR workflows, NBS design, LULUCF-based EO monitoring, and decarbonisation methods.

The EO Hub Pathway links global-to-local scales through (a) the systematic use of global EO programmes; (b) LULUCF-aligned indicators and multi-decadal satellite analyses; (c) site-specific phenological monitoring for regenerative agriculture; (d) carbon-removal computation through NBS; and (e) XR/VR tools for immersive awareness raising. Together, these elements support adaptive strategies, MRV systems, regenerative practices, and innovative land-management approaches for regions facing degradation and climate challenges.

Exploration of socio-spatial pertinent dimensions of human population and its geo-spatial distribution in Nepal has been a foremost concern of planners and researchers for development. An input data matrix of 75 X 88 representing Nepal’s demographic, socio-economic, and environmental variables were used to investigate spatial pattern of latent fundamental characteristics and to examine their geo-spatial variability by integrated use of RS, GIS, GPS, Factor Analysis, and ANOVA. Six fundamental socio-spatial dimensions of human population explaining 74.0 percent of total variance were investigated. Demographic was the most prominent and significant dimension accounting for 27.0 percent of the total variance spatially clustered in Terai region indicating demographic pressure: old dependency and family size and also evident by Factorial Areas Analysis (FAA). Facility-Education Dimension was the second most dominant accounting for 19.62 percent of total variance spatially having insignificant geographic variability. Maize production and Ethnic Dimension was found as the third dominant dimension and was significantly concentrated in eastern mountain and hill districts, characterized as high dominancy in ethnic and language issue. Mother Tongue- Marriage age was the fourth accounting for 9.47 percent of total variance spatially clustered on EDR significantly spatial variability among development regions. Kathmandu district locating lower-left corner of both axes indicating the free from both pressure of old dependency and large family size. Family size- Wheat production was the least important dimension, significantly different and spatially distributed in Terai Region. The study demonstrates the usefulness of geospatial technology for demographic, and production planning, and sustainable regional policy in Nepal.

Black Carbon (BC) and Organic Carbon (OC) are among the most influential aerosol species affecting air quality, radiative forcing, and climate interactions, especially in regions dominated by coal-based industries. Understanding their temporal behaviour and associated deposition processes is critical for assessing pollution dynamics and guiding regional mitigation measures. Korba, located in Chhattisgarh, India, is widely known as the “Power Hub of India” due to its dense cluster of coal-fired thermal power plants, aluminium smelters, and mining activities, making it an ideal location to examine carbonaceous aerosol loading. The primary objective of this study is to quantify monthly variations in BC and OC and evaluate their atmospheric interactions and deposition characteristics during the study period. Methodology involved extracting BC and OC fractions, including hydrophilic (BCPI, OCPI) and hydrophobic (BCPO, OCPO) components, along with dry and wet deposition fluxes and meteorological drivers such as relative humidity, temperature, pressure, and precipitation. The results show that BC ranged from 3.97×10⁻⁹ to 1.00×10⁻⁸, while OC exhibited higher values between 7.68×10⁻⁹ and 2.24×10⁻⁸, indicating dominance of organic aerosols over black carbon. Dry deposition of BC was significantly high (up to 2.29×10⁹), whereas wet deposition remained several orders lower (≈1.75×10⁻¹² to 1.19×10⁻¹¹). Meteorological conditions, including RH (23–87%) and temperature (290–308 K), modulated concentrations and deposition behaviour. Overall, the study highlights substantial BC–OC loading driven by industrial and combustion sources in Korba. The conclusion emphasizes the need for cleaner combustion practices, while future work may integrate chemical transport modelling to identify precise source contributions.

The school of urban construction in Beijing City University (BCU) is committed to cultivating application-oriented talents who serve for urban planning, urban construction and urban management. The Geographic Information Science (GIS) program in our university began in 2019. It is carried out on the basis of the investigation of the national needs, the industry development, and the actual situation of our university and other universities in Beijing. Based on the above analysis, we have explored Unmanned Aerial Vehicle (UAV) remote sensing technology and LiDAR as two of the training orientations, focusing on the training of data acquisition and processing capabilities using UAV and LiDAR. We have carried out a lot of explorations and practice in curriculum structure and practical teaching. Student's professional ability is obviously improved. Their competitiveness is significantly enhanced.

This is an abstract for a paper on best pedagogical practices in engineering education. In particular, the paper will focus on a project-based course involving group work. Post pandemic, the course has been run twice. In both iterations there were serious breaches of academic integrity. This happened even though reasonable measures to prevent cheating had been put into place. The aim for future offerings of the course would be to preventatively tighten those measures and in the unfortunate scenario that cheating happens again to explore tools for its early detection.

Due to rapidly advancing technology and increasing societal needs, there is a significant demand for capacity building in the geospatial domain, which involves developing the skills, knowledge, and resources of individuals and organisations. The European association EuroSDR, a not-for-profit international network organisation linking National Mapping and Cadastral Agencies (NMCA) with research institutes and universities in Europe, recognised the challenges of skills development in the geospatial domain more than two decades ago. The EduServ annual training programme, organised under the EuroSDR umbrella, is a well-established and internationally recognised series of e-learning courses in photogrammetry, remote sensing, and geospatial information (GI) science. Since its inception in 2002, it has primarily aimed to transfer knowledge from EuroSDR research projects to the wider GI community. In recent years, interest in EduServ courses has increased significantly, and the topics have expanded to address new geospatial technologies and growing societal needs. This paper aims to share EuroSDR’s experience in distance education with the wider scientific community. Rather than limiting EuroSDR expertise to the European GI community, European mapping agencies can share their knowledge and experience with the international GI community.

The discussion on this topic is particularly relevant, as the changing geopolitical landscape has impacted the dissemination of geospatial data globally, as evidenced by reduced NASA funding for Earth and atmospheric studies, as well as the recent US government shutdown. The political alliances of countries also restrict the data availability during critical periods, such as war or disaster. This reminds countries to invest on sovereign geospatial dissemination portals to sustain research, innovation, and public discourse. At the same time, the emerging global conflicts open a new window of opportunity for India’s “Unified Geospatial Portal,” which is under development to become a predominant source not just for India but for the global community to leverage datasets generated by India's satellites, covering India and beyond.

This paper presents a tailored pedagogical approach to digital heritage documentation in contexts where heritage is under threat. It was developed during the July–August 2024 UNESCO/ICOMOS mission to Kyiv, Ukraine, within the UNESCO/Japan Funds-in-Trust project “Support for Ukraine in Culture and Education through UNESCO / Emergency response for World Heritage and cultural property: damage assessment and protection,” in relation to the UNESCO World Heritage property “Kyiv: Saint-Sophia Cathedral and Related Monastic Buildings, Kyiv-Pechersk Lavra.” The mission focused on the Metropolitan’s Residence and the Bell Tower of the Saint-Sophia Cathedral Complex. In parallel with the production of documentation for emergency preparedness and future conservation assessment, the mission implemented a dedicated capacity-building programme for the staff of the National Conservation Area “Sophia of Kyiv.” The paper discusses five interconnected components of this training programme: preparation before the mission, structure and content of the sessions, training activities and didactic material, learning outcomes and targeted competencies, and adaptive responses to a conflict-affected environment. The case study shows that integrating training within an active documentation workflow can strengthen both the immediate value of the records produced and the longer-term capacity of local professionals to support conservation, monitoring, and risk preparedness at World Heritage sites under threat.

Processing massive archives of satellite imagery has historically paralyzed traditional desktop-based remote sensing laboratories. The sheer volume of computationally heavy tasks-from bulk atmospheric correction to long-term radiometric calibration-frequently exceeds the hardware capacity of local campus networks and student laptops. To bypass these severe limitations, this study presents a dual-cloud pedagogical architecture that integrates Google Earth Engine (GEE) and Alibaba's AI Earth. This hybrid framework allows students to instantly access petabytes of analysis-ready data while maintaining low-latency processing for complex modelling via domestic servers. We operationalized this setup through four core practical modules: urbanization monitoring, urban heat island analysis, nighttime light assessment, and AI-driven road extraction. By entirely eliminating the overhead of raw data management and environment configuration, students can finally redirect their cognitive focus toward the actual physics and algorithmic logic of remote sensing—such as parameterizing radiative transfer equations and interpreting radiometric time-series. Furthermore, in light of AI Earth's recent policy shift in March 2026, which heavily restricts free access for educational usage, we critically evaluate the long-term sustainability of this curriculum. To maintain unhindered access to cloud-native geoprocessing, our future instructional designs will assess alternative localized platforms and open-source AI frameworks, ensuring the uninterrupted evolution of rigorous Earth observation education.

Web-based tools for synthetic spatial data generation offer flexibility and accessibility to students and educators alike. This abstract takes a brief overview of some of the existing and developing tools to this end.

The COVID-19 pandemic reshaped higher education and accelerated the shift toward blended learning (BL). In GIS education, however, most BL practices have emphasized technologies rather than pedagogical foundations. This study introduces a Complex Adaptive Blended Learning System for GIS education (CABLS-GIS) — a theory-driven framework that conceptualizes BL as an interdependent system comprising the learner, teacher, content, technology, learning support, and institutional environment. The framework was implemented in an introductory GIS course at the National University of Singapore through a flexible-mode BL design integrating face-to-face and online components. Survey results from undergraduate and graduate students revealed positive perceptions of the CABLS-GIS approach, particularly regarding learning flexibility, motivation, and conceptual understanding. The findings highlight how theoretically grounded BL design can enhance pedagogical coherence, technological integration, and educational resilience in the post-pandemic era. CABLS-GIS thus provides a holistic and adaptive model for advancing GIS education and serves as a foundation for developing future personalized and data-driven learning strategies.

Remote sensing imagery is a crucial resource for evaluating flood-affected areas following inundation events. The integration of optical satellite data and UAV-based drone surveillance enables the development of precise flood extent maps. This research determined inundated areas by applying spectral water indices and classification methods to both Landsat and Sentinel-2 imagery, supplemented by UAV-based damage assessment. To delineate flooded regions, the study utilized the Normalized Difference Water Index (NDWI), the Modified NDWI (MNDWI), and the Water Ratio Index (WRI). Additionally, land use and land cover analysis were conducted using supervised classification with the maximum likelihood algorithm, enabling effective identification and comparison of flood extents across the indices. The flood coverage was estimated at approximately 107 km² via Landsat, 111 km² through MNDWI, and 115 km² using NDWI. By leveraging classification insights from each index, a targeted correction process was implemented to address misclassifications and enhance delineation accuracy. Notably, both MNDWI and NDWI yielded accuracy rates surpassing 90%, reinforcing the reliability of the results. The proposed remote sensing techniques offer a reliable and innovative approach for detecting flood-affected areas, contributing significantly to timely disaster response and targeted relief efforts.

The author aims to introduce some concepts and practical tools, which were usefully applied in the curriculum development influenced by the Bologna process and successfully used in the quality improvement practice.

The first part of the paper is dealing with the definition of education/training needs and involvement of stakeholder’s curriculum planning. One of the most important outcomes from these activities is the definition of skills and competences; and stakeholder management plan. The curriculum is a crucial component of any education/training activities, it is a road map to knowledge, and it builds knowledge topology. The implementation of new curricula often needs capacity building for faculty delivering education or training. Faculty of Geoinformatics (GEO) at Tribhuvan University of Kathmandu, Nepal participated or managed in many relevant international projects. The author will share some good educational practices.

The second part is focusing on curriculum and learning material development methods. The competency matrix will be introduced as a tool used to document and compare the required competencies for graduates. It is used in a gap analysis for determining where critical overlaps between courses are or which skills/competencies are not taught deeply enough.

Quality is omnipresent, ubiquitous – like the cloud of computers. Understanding and evaluating the quality of education requires a comprehensive picture of the unique and complex characters of the system that produced them. The third part of the paper is dealing briefly with quality impro issues.

Photogrammetry has been known for a little less than a century as the art and science of making precise measurements from optical images. In the last few decades, photogrammetry was taught with traditional approaches focusing on using images captured by metric cameras. Recently, new sensors have been adopted in the surveying and mapping communities. Employers are now looking for graduates with the knowledge and skills required to extract accurate and reliable data from these sensors. Therefore, novel approaches are needed to blend essential principles and cutting-edge technologies in the photogrammetric courses. This article outlines the design and implementation of a new syllabus for a photogrammetry class, the experience delivering the material, and student feedback. The new curriculum introduces students to non-metric camera calibration; laser scanning; and satellite image rectification. sUAV flight planning and data processing were the core of the redevelopment; hence, the article focus on blending sUAV in the curriculum. Topics are taught in lectures and then practiced in labs. Comments received from students and academic and industry experts supported the new design and recommended it as part of renovating new surveying programs.

The geometric monitoring of historic masonry towers is a central topic in heritage preservation, where structural safety must be ensured despite complex geometries, heterogeneous materials and deformation processes that evolve over time. This contribution presents an integrated surveying workflow developed by the DICAM Geomatics Laboratory and tested on the Garisenda Tower in Bologna, one of the most emblematic slender structures in Italy. The tower, built in the early 12th century and today inclined by more than 3 m, represents a challenging case study due to its ongoing deformation, dense urban context and the impossibility of establishing forced-centering stations.

The proposed methodology combines the high-precision capabilities of a Leica TS30 total station with the geometric completeness of a Leica RTC360 terrestrial laser scanner. The total station defines a stable local reference system and ensures accurate vertical alignment of the scanning instrument, while the TLS provides detailed three-dimensional reconstruction of the tower’s surfaces. The resulting 3D model enabled the computation of out-of-plumb parameters, wall flatness and local deformation patterns. Validation against TS30 control points confirmed the metric reliability of the integrated approach.

Cultural heritage is increasingly reinterpreted and experienced through digital and immersive environments, including Extended Reality (XR) and Augmented Reality (AR) technologies. While these engage visitors in novel ways, the trend raises questions about what constitutes an “authentic” digital experience. This study examines perceptions of authenticity in an AR experience at the Twin Pagodas, a small-scale heritage site in Suzhou, China.

Building on a framework that distinguishes between objective authenticity (the accuracy of content), constructive authenticity (the interpretive meaning conveyed through stories), and subjective authenticity (the personal and emotional experience), the study explores how these dimensions interrelate and are mediated during digital engagement. Data were collected from 108 participants (ages 8–67, Chinese and international visitors) via pre- and post-experience surveys and 20 semi-structured interviews. Participants rated statements capturing each authenticity dimension, and Pearson correlation analysis examined relationships among them. Ethical approval was obtained prior to data collection.

Findings indicate that authenticity in mobile AR heritage experiences operates across multiple interacting layers. Cognitive judgments about historical accuracy shape interpretive meaning-making, while affective engagement forms a relatively independent experiential dimension. This pattern aligns with existing scholarship that emphasizes the interpretive and experiential nature of authenticity in heritage contexts, while providing empirical evidence from a mobile AR implementation at a modest urban heritage site. Limitations include the focus on a single site and AR design, indicating the need for further research across diverse contexts to strengthen generalizability.

Digital archiving of cultural heritage using 3D scanned point cloud data requires effective edge-highlighting visualization to analyze fine structures. However, conventional methods often produce edges that are too thick, obscuring fine details.

This study proposes a method for adaptively optimizing the scale (range) used for local statistical analysis. This allows for the extraction of both sharp and rounded soft edges with high visibility. The core idea is to automatically determine the optimal scale for the analysis.

First, an eigenvalue-based feature value is calculated at multiple scales. Next, the scale that yields the minimum sample variance of this feature value across the entire point cloud is found and selected as the optimal scale.

Using this optimal scale, edge regions are extracted using another feature value. Opacity gradation is applied to emphasize soft edges as well. When this method was applied to a complex cultural heritage relief, fine structures such as ship hulls and human figures, which were indistinct with conventional methods, were clearly visible in the visualization results of the proposed method.

Coastal bathymetry and water-clarity assessment using multispectral remote sensing is essential for understanding nearshore dynamics, sediment transport, and environmental variability. Optical indices such as the Normalized Difference Water Index (NDWI) and satellite-derived depth models provide a rapid means of monitoring these changes. This study focuses on the coastal region of Goa, located along the central western coast of India, an area influenced by strong monsoonal cycles, tidal fluctuations, and high sediment exchange from estuarine systems and open-sea interactions. The objective of this work is to evaluate monthly and annual variations in NDWI and satellite-derived bathymetric depth from 2022 to 2024 and to assess their seasonal and statistical relationships. Sentinel-2 imagery was processed to generate monthly median composites, from which NDWI and bathymetry were extracted; monthly mean NDWI and median depth values were calculated to represent surface water conditions and subsurface optical penetration, respectively. Results show clear seasonal contrasts, with NDWI values ranging from –0.02 to 0.33 and depth values varying between –8.5 m (deep, clear water) and +8.4 m (high turbidity). Annual mean NDWI remained relatively stable (~0.15), whereas median depth became progressively shallower from –2.01 m in 2022 to –0.52 m in 2024, indicating declining optical water clarity. Seasonal correlations between NDWI and depth shifted from strongly positive in winter (r = 0.70) to strongly negative during the pre-monsoon period (r = –0.83), reflecting the influence of sediment resuspension and monsoonal turbidity. Future work may integrate turbidity, wave climate, and machine-learning models for enhanced depth estimation.

The concept of Levels of Detail (LoDs) plays a critical role in 3D semantic modelling by balancing geometric and semantic complexity with application needs. In our earlier work, we proposed a four-level LoD framework tailored to Buddhist statues, ranging from symbolic representation to detailed geometry, aiming to fulfil the needs for about 60 applications. However, when implementing this concept to applications in the cultural heritage domain, it is suggested to introduce an intermediate LoD between LoD2 and LoD3 because some applications need geometries coarser than the LoD3 but more detail than LoD2. In this paper, we present the analysis of these requirements and propose a new LoD for the 3D modelling of Buddhist statues. To verify the updated concept, we conducted a questionnaire among experts in geomatics and archaeology. Feedback from 170 participants confirmed that the five-level LoD concept is more appropriate and the revised framework provides a more comprehensive alignment with tasks in archaeology, conservation, museum exhibition, and risk management, and demonstrates strong potential for standardization within CityGML ADE.

In recent years, digital archives using VR technology have been actively created, but most are intended for viewing culutual properties, with few designed for analysis. In this study, we create a VR system for understanding the 3D structure of cultural properties, using the 3D point cloud data of Tamaki Shrine, a World Heritage site in Nara Prefecture, Japan, as an example. As a feature enhancement method, we performed feature enhancement using principal component analysis. Furthermore, by applying it to a transparent VR environment, we aimed to improve the visibility of 3D structures.

Multispectral data improves instance segmentation in digital agriculture by combining geometric and spectral information to distinguish complex natural features. While geometric information captures structural details, it often falls short when dealing with complex natural features that exhibit high spectral similarity, rather than due to limitations inherent to geometric representation itself. This work presents a feasibility analysis of instance segmentation using a spectral point cloud. A combination of spectral bands is selected based on class separability and proximity to a normal distribution as estimated by the Shapiro–Wilk test. The aim is to identify the minimum number of bands required to produce optimum results. For the normality analysis, Euclidean magnitude normalisation was applied, and it was also used alongside standard scaling to support the Multilayer Perceptron (MLP) for classification and segmentation. To refine the MLP predictions and consolidate instance labels, a graph-based post-processing step was applied, linking each point to its nearest neighbours and using a majority-voting scheme, resulting in spatially coherent clusters and refining the MLP predictions. The results demonstrate that multispectral data can reliably segment individual objects, with ten spectral bands being sufficient to achieve highly satisfactory segmentation and accurately delineate natural features such as leaves and tree trunks. Further increasing the number of bands improved spectral definition even more, with 14 bands achieving the highest performance across all metrics (mIoU: 96.59%; AP50: 96.14%). These findings highlight the strong potential of multispectral point clouds for precise and scalable object-level segmentation in agricultural environments.

This study presents a UAV-based framework for early detection of Bacterial Leaf Blight (BLB) in rice using multi-temporal and multi-modal data. Conducted at the International Rice Research Institute (IRRI) during the 2023 wet season, the experiment integrated multispectral, thermal, and RGB imagery with crop physiological measurements from both healthy and artificially inoculated fields. Spectral (NDVI, NDRE), thermal (canopy temperature), and textural features were extracted and analyzed using a Random Forest classifier to identify early indicators of BLB infection. Results demonstrated that combining spectral and thermal data enhances early disease detection before visible symptoms appear, supporting precision agriculture and sustainable rice disease management.

National mapping agencies (NMAs) provide authoritative and authoritative geospatial data for their respective countries. All geospatial agencies face significant challenges, including rapid technological advancements, societal expectations, and environmental pressures.

To produce high-quality geospatial information that meets user needs, NMAs combine image data acquisition from various sensors, field data collection, and manual interpretation and processing.

The use of geospatial artificial intelligence (GeoAI) offers opportunities to optimize workflows and reduce manual workload.

This article presents preliminary results from a study on the applications of GeoAI in the activities of National Mapping Agencies, along with key challenges and ethical considerations.

Detecting early-stage vegetation stress at the individual tree scale is a pivotal remote sensing application. The ``green shoulder'' band at 530 nm serves as a key signal for early stress detection due to its sensitivity to carotenoid changes. However, existing remote sensing systems often struggle to simultaneously capture fine-scale canopy structures and stress-sensitive spectral data, making heterogeneous fusion a promising topic. Unlike mainstream supervised methods that rely on prescribed degradation models and high-quality samples, an unsupervised blind fusion framework based on Implicit Neural Representation and low-rank decomposition is proposed in this paper. Guided by orthorectified aerial images, the framework performs per-band super-resolution on PlanetScope SuperDove data to achieve a 0.16-meter resolution. It employs Sinusoidal Representation Networks to learn a continuous joint implicit representation of spatio-spectral information, effectively modeling the non-linear relationship between canopy structure and spectral response.To mitigate high-dimensional feature redundancy during heterogeneous data fusion, low-rank decomposition is integrated to reduce computation overhead. Experimental results show that the proposed method can fuse heterogeneous images effectively, providing a solid solution with practical guidance for subsequent early stress monitoring at the individual tree level.

Urban LiDAR point clouds provide rich geometric information but pose significant challenges for automated interpretation due to their scale, noise, and semantic complexity. Traditional convolutional and graph-based networks (e.g., PointNet++, RandLA-Net) have made significant strides by focusing on local geometric feature learning. However, they often lack the ability to incorporate high-level, global semantic context. This limitation leads to persistent errors in object boundary delineation and category confusion, particularly for semantically or geometrically similar classes (e.g., 'road' vs. 'sidewalk',or 'low-wall' vs. 'curb').Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in contextual understanding, reasoning, and knowledge retrieval. Inspired by these developments, and motivated by the growing trend of cross-modal alignment in vision-language models, we propose an LLM-enhanced segmentation framework that integrates linguistic priors into the 3D perception pipeline. Our key contribution is the use of contextual prompts—textual descriptions generated or retrieved by an LLM based on 3D scene content—to guide the segmentation network. These prompts provide disambiguating cues, enabling the model to better distinguish between challenging classes and to recognize objects that are rare in the training data.The main contributions of this work are:1.A novel framework that synergistically combines a geometric point cloud encoder with an LLM-based contextual prompter for semantic segmentation.2.A methodology for generating and fusing contextual prompts from point cloud data, bridging the gap between geometric perception and linguistic reasoning.3.Extensive experiments demonstrating superior performance over state-of-the-art methods, particularly on semantically ambiguous and long-tailed object categories.

This contribution presents a new multi-sensor dataset of the urban road network of Turin, designed to support research in Digital Twins, AI-based road monitoring, and semantic 3D modelling. The dataset integrates mobile mapping (MMS), aerial LiDAR, imagery, and BIM/IFC models into a unified spatial and semantic framework. It includes detailed point cloud classifications, pavement defect annotations, and metadata to ensure full reproducibility. By combining geometric precision with semantic labelling, the dataset enables applications such as automated defect detection, semantic segmentation, 3D reconstruction, and predictive maintenance. Compared to existing benchmarks, it offers a unique focus on road surface condition and DT interoperability. The contribution outlines the methodology used to structure, validate, and document the dataset, positioning it as a valuable resource for both academic research and operational urban infrastructure management.

Structural inspection is crucial for comprehensive risk management, especially given the accelerated deterioration caused by factors such as climate change and obsolescence. The accurate determination of the percentage of surface damage is fundamental for optimizing maintenance decision-making and the administration of resources for infrastructure preservation. This work presents a methodological exploration to assess the superficial condition of a concrete pedestrian bridge located over an urban river. The study focuses on determining the structural conditions by calculating the percentage of surface damage to evaluate maintenance needs. For data acquisition, Light Detection and Ranging (LiDAR) technology is employed using a Terrestrial Laser Scanner (TLS) Trimble X7 laser scanner, generating a 3D point cloud that models the bridge surface with precise spatial coordinates. The methodology utilizes the reflective intensity of the laser pulses to obtain quantitative information about the surface. This approach allows for the precise identification, demarcation, and quantification of deteriorated areas. The application of this methodology facilitates a non-invasive and detailed diagnosis of the surface condition, providing quantitative and visual information that can enhance the maintenance planning of critical infrastructure such as pedestrian bridges.

Understanding public experiences in urban greenspace is essential for supporting more human-centric design and management. While traditional survey methods are often time- and labor-intensive, user-generated content (UGC) offers a rapid and scalable alternative for capturing public experiential insights. However, extracting detailed user experience information from this data remains methodologically challenging. This study proposes a novel multimodal analytical framework based on online review data and natural language processing techniques, combining LoRA fine-tuned RoBERTa model with CLIP vision-language model to analyze multidimensional ecosystem service experience patterns in urban greenspace from user-generated text and image reviews. Results demonstrate that the proposed approach achieves more robust extraction and analysis of user experience insights compared to conventional deep learning and lexicon-based methods, exhibiting greater capacity to process contextually embedded experiential information. The multimodal framework enables more comprehensive capture of user experiences than either text or image data alone, with particular gains on dimensions that are difficult to represent through a single modality. Applying the analytical framework to Amsterdam and Rotterdam as case studies, statistical and spatial analysis reveals heterogeneity in user urban greenspace experiences and identifies key experiential bundles alongside their associated synergies and trade-offs. This study offers a novel approach to quantifying urban greenspace experiences from a user perspective, and provides insights for evidence-based urban greening practices.

This paper describes the development of a 3D mesh product by Ordnance Survey, Britain's National Mapping Agency. The work originated in the research team and was then taken up by a multi-disciplinary cross-business team which used product development techniques and extensive customer interviews to determine the feasibility (could it be made) and viability (would it generate sufficient revenue) of a potential 3D mesh product. The 3D mesh, generated from nadir aerial imagery already captured for topographic map update, was introduced as a beta product and is currently being tested by potential users.

Photorealistic 3D reconstruction fundamentally requires recovering the intrinsic optical properties of object surfaces. Traditional multi-view photogrammetry, based on Structure-from-Motion (SfM) and Multi-View Stereo (MVS), effectively reconstructs geometry and texture but assumes Lambertian reflectance, failing on non-Lambertian materials with specular highlights and subsurface scattering. While recent implicit representations like NeRF and its extensions have advanced novel view synthesis, their effectiveness is constrained by the inherent coupling of geometric, material, and luminous properties. To overcome these issues, we propose a differentiable rendering method for photorealisitic material reconstruction in close-range photogrammetry, enabling physically accurate forward and inverse rendering of PBR parameters. Experimental results demonstrate that our method achieves high-fidelity reconstruction of object geometry and multi-channel SVBRDF/BSSRDF materials, robustly recovers HDR environment maps under complex indoor and outdoor illumination, can effectively removes indirect illumination artifacts through Monte Carlo ray tracing, and produces editable assets that enable realistic relighting and material editing.

The emergence of vision–language models (VLMs) has enabled joint multimodal understanding beyond traditional visual-only approaches. However, transferring VLMs from natural images to remote sensing (RS) segmentation remains challenging due to limited category diversity and significant domain gaps. We propose a training-free framework that decouples visual and textual inputs and performs multi-scale visual–language alignment for RS segmentation. At the global–local decoupling module, we separate text into local class nouns and global modifiers, while images are partitioned into class-agnostic mask proposals via unsupervised mask generation. At visual–textual alignment module, we introduce a context-aware cropping strategy and a knowledge-guided prompt engineering method to enhance text representations, enabling mask classification for open-vocabulary semantic segmentation (OVSS). A Cross-Scale Grad-CAM module refines activation maps using contextual cues from global modifiers, facilitating accurate and interpretable alignment for referring expression segmentation (RES). Evaluations on the benchmarks demonstrate strong performance, highlighting the potential of training-free VLM transfer to the RS domain.

Retrogressive thaw slumps (RTSs) are key indicators of permafrost degradation in Arctic regions. Yet, their detection remains challenging due to spectral similarity with surrounding terrain and the limited generalization of conventional deep learning approaches. This study presents a Geo-Foundation framework that integrates pretrained Clay embeddings with high-resolution PlanetScope multispectral imagery, spectral indices, and ArcticDEM data for RTS detection in the Northwest Territory (NWT), Canada. The proposed dual-branch architecture combines high-level geospatial representations with physically meaningful environmental features to improve segmentation performance. The model achieved an F1-score of 0.83 and a mean Intersection-over-Union (mIoU) of 0.75 on the validation dataset. Analysis of patch size indicates that intermediate spatial context provides optimal performance, while feature importance results highlight the dominant role of vegetation-sensitive spectral bands and indices. Qualitative evaluation further confirms accurate boundary delineation and spatial consistency across diverse terrain conditions. The results demonstrate that Geo-Foundation models enhance detection accuracy, reduce dependence on large labeled datasets, and improve generalization across heterogeneous Arctic landscapes. This approach provides a scalable and efficient solution for monitoring permafrost-related disturbances under a changing climate.

Forests provide essential ecosystem services beyond timber, yet locating high-yield areas for non-wood forest products such as bilberries (Vaccinium myrtillus) remains a challenge for both recreational and commercial pickers. By integrating Airborne Laser Scanning (ALS), Geographical Information System (GIS) data, and crowdsourced terrestrial imagery analyzed via deep learning (YOLO), we developed a predictive system optimized for identifying high-yield hotspots. We demonstrate that YOLO detection remains highly accurate, but plant height significantly contributes to berry omission. However, this limitation can be mitigated by selecting the maximum berry count from multi-angle terrestrial images. Using a Random Forest classifier across a 36-km² study area in Nuuksio, Finland, we achieved a precision of 58% for the highest yield category. This represents a 20-fold increase in the probability of encountering a high-yield area compared to random searching. Extensive user testing over two years validated the practical utility of the system, showing a 22.5% increase in harvested yield and a 36.5% reduction in time required to locate hotspots. Furthermore, 97% of users reported that the platform provided an accurate big picture of bilberry yield. These results highlight the potential of combining crowdsourced citizen science with advanced LiDAR metrics to create digital twins of forest ecosystems that enhance human interaction with nature and optimize the sustainable harvest of wild food resources.

Cultural heritage (CH) digitization currently suffers from fragmented multi-source heterogeneous data, insufficient knowledge organization, and limited semantic expression in 3D CH models. Existing knowledge graphs and HBIM in CH field lack unified semantic representation and effective GIS integration, thus restricting intelligent knowledge services. To overcome these issues, a knowledge service approach integrating knowledge graph and semantic 3D models is proposed, enabling the transformation from data process to knowledge-driven services. An extension model for CH (CHADE) is developed using the CityGML ADE mechanism to support the construction of semantically enriched 3D geospatial scenes. Meanwhile, A domain ontology (CHOnto) based on CIDOC CRM is constructed to formalize CH knowledge, and multi-source heterogeneous data are organized into a Cultural Heritage Knowledge Graph (CHKG). By establishing semantic connections between knowledge graph and 3D models, the proposed method achieves integrated representation of geometry, spatial context, and domain knowledge. A prototype system (3DCHKS) is implemented and validated through multiple heritage scenarios. Results demonstrate that the approach enhances semantic connectivity, knowledge organization, and scenario-based representation, supporting intuitive visualization and intelligent application. Although limitations remain in generalizability and knowledge extraction robustness, this study provides a novel framework for integrated CH knowledge services and lays a foundation for scalable, knowledge-driven heritage applications.

Aerosols play a critical role in the development of boundary layer and build-up of air pollution in urban environments. Their presence in the atmosphere is calculated and represented by Aerosol Optical Depth (AOD). Satellite sensors observe aerosol quantities and different algorithms are applied to retrieve AOD at varied spatial and temporal resolutions. In air quality monitoring, satellite-based AOD products are useful in modelling particulate matter (PM). This study evaluates AOD products observed by Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS) and Advanced Himawari Imager of Himawari-8 in predicting inland daytime PM2.5 for test sites in Japan and South Korea. Prediction models are constructed using eXtreme Gradient Boosting (XGBoost) regression with input variables from observation datasets matched on PM2.5 station locations. In addition to AOD, seventeen (17) predictor variables were considered to account topographic and meteorological parameters that can influence the formation and transport of PM2.5 near the ground surface. Overall results show that prediction model using MODIS MAIAC AOD generate relatively higher accuracy for daily estimates considering both spatial coverage and prediction skill metrics. For future work, model improvements will be done by exploring additional predictor variables to reduce overfitting and additional statistical tests to generate more accurate estimates of PM2.5.

Geospatial embeddings represent a promising paradigm for encoding geospatial information into compact and learnable representations that support scalable downstream tasks in remote sensing. Among recent developments, Google’s AlphaEarth embeddings are a dataset of 64-dimensional embeddings, made available globally at 10 m resolution, derived from multimodal inputs, including multispectral and SAR imagery, elevation, gravity and text data. In this study, we explore the feasibility of inferring surface height from AlphaEarth embeddings within a deep learning framework. The analysis focuses on an 8000 km² area in Nouvelle-Aquitaine, France, where a 5 m resolution Digital Surface Model (DSM) is available. A U-Net architecture with a ResNet34 encoder was trained to predict surface heights from the 64 embedding channels using a spatial cross-validation strategy to ensure independence between training and testing subsets. For computational efficiency in this preliminary experiment, both the embeddings (input) and DSM (target) were resampled to 100 m. Results indicate promising agreement between predicted and reference heights, achieving an R² of 0.83 and a Pearson correlation of 0.93 on the test set. However, a systematic bias was observed. These findings highlight the potential of AlphaEarth embeddings to capture height-related features, despite being trained on a broader geospatial domain. Future work will address bias investigation, increase inference spatial resolution, and expand the analysis across diverse geographical regions. Additionally, comparisons with alternative embedding datasets, such as Tessera, will be conducted to better evaluate the strengths and limitations of embedding-based surface height estimation.

With the rapid advancement of large language models, natural-language-based understanding of satellite task requests is becoming increasingly important for improving the accessibility of remote-sensing services. However, satellite commands issued by non-expert users are often conversational, ambiguous, and terminologically inconsistent, leading to a substantial gap between free-form expressions and structured task representations. To address this challenge, we propose a hierarchy-aware framework for intent recognition and task-oriented text generation from non-expert satellite instructions. Specifically, we design a hierarchical annotation scheme that models intent levels, parameter structures, inter-element relations, and execution complexity, and we further construct a hierarchical sequence representation for learning. We then introduce a boundary-aware sample organization method based on semantic similarity and structural proximity, together with a retrieval-augmented multi-type negative-sample reorganization strategy to enhance robustness. Finally, we adopt Qwen3-8B with LoRA for parameter-efficient domain adaptation and unified generation of top-level intents and task-oriented outputs. Experiments on a manually curated dataset of 4,025 non-expert satellite instructions show that the proposed method consistently outperforms multiple baselines on both intent classification and task-oriented generation, demonstrating a resource-efficient and scalable solution for natural-language satellite task interfaces.

China’s Low-Altitude Economy (LAE) requires robust airspace surveillance for the safe integration of Vertical Take-off and Landing (VTOL) aircraft and Unmanned Aerial Systems (UAS). Traditional radar Automatic Target Recognition (ATR) approaches—both micro-Doppler-based and tracking-based—depend on track accumulation, introducing Detection Response Times (DRT) exceeding 3–5 seconds that are incompatible with real-time low-altitude operations. This paper proposes a tracking-free ATR methodology that restructures the conventional serial “Detection–Tracking–Recognition” chain into a parallel “Integrated Detection and Recognition” (IDR) architecture. The classifier operates independently of the tracker, extracting target attributes from single-dwell echoes within one Coherent Processing Interval (CPI), achieving a DRT below 100 milliseconds—more than an order-of-magnitude improvement over existing systems. The methodology is validated through field trials using a X-band radar, demonstrating reliable identification of VTOL at ranges exceeding 12 km. We further clarify the precise definition of DRT and argue for NATO ATR hierarchy level T3 (Recognition) or above as the minimum performance standard for low-altitude radar sensors.

The Amazon rainforest faces threats from anthropogenic disturbances, which also increase greenhouse gas emissions and contribute to global climate change. In 2004, a system to detect disturbance for the Brazilian Legal Amazon (BLA) was created to mitigate forest loss. The system, Detection of Deforestation in Real Time (Deter), from the National Institute for Space Research (INPE), alerts to seven types of anthropogenic forest disturbances through the visual interpretation of optical satellite imagery from CBERS-4, CBERS-4A and Amazônia-1. Many near-real-time systems currently generate alerts using automated algorithms, primarily leveraging SAR sensors to compensate for the absence of cloud-free images over tropical forests. Deter uses spatial patterns to identify types of disturbances, minimising commission errors, while most algorithms prioritise the temporal dimension for early-stage detections. Discrepancies in space and time across systems and disturbance types, such as omissions, delays, and mismatches, are linked to the selection of sensor technologies, forest masks, and algorithm strategies. Forest disturbances detected between 2020 and 2024 for the entire Brazilian Amazon Biome were extracted from the systems: Deter, Prodes, MapBiomas, SAD, RADD, GLAD, LUCA and TropiSCO. Based on this dataset, we conducted an exploratory analysis revealing agreement and disagreement between detection systems regarding five classes of disturbances (clear-cut, selective logging degradation, fire scars, mining and windthrow). The results emphasise the importance of systems that consider the trade-off between spatial and temporal context to detect different disturbance types, similar to Deter, but using automated near-real-time alert approaches.

In archaeology, the long-standing problem of low efficiency and high experience-dependence in manual matching of numerous unearthed pottery shards has been a challenge. This paper presents and develops an intelligent matching and annotation tool for pottery shard images, integrating advanced computer vision technologies. Using 35,159 pottery shard images from Pit H690 at the Daxinzhuang Site in Shandong as the dataset, a comprehensive “segmentation-feature extraction-cross-verification-screening” technical process is established. The core steps are as follows: First, the natural-language-based visual segmentation model Lang SAM is employed to precisely segment individual pottery shards from the original images, obtaining clean front and back images. Second, the self-supervised visual feature model DINO v2 is used to extract deep visual feature vectors of the shards, calculate image similarities for the front and back sides respectively, and generate a Top-N candidate matching list for each shard. Finally, cross-verification is carried out by taking the intersection of the front and back candidate lists, and the final screening is conducted with archaeological metadata. This research demonstrates the great application potential of AI in archaeological fragment assembly, offering an automated, interpretable, and efficient solution for handling massive cultural relic fragments.

Effective maritime surveillance and management of small-scale fisheries remains challenging in coastal waters because small vessels are not systematically tracked and are weakly represented in medium-resolution satellite imagery. Within the AI4COPSEC Horizon Europe framework, this study investigates an object-detection workflow for small-vessel monitoring along the Adriatic coasts of Marche and Puglia, Italy. A multisource dataset was prepared in which Sentinel-2 and PlanetScope optical imagery were manually annotated to enrich an existing SAR and optical imagery training dataset and support a two-stage training strategy. The first stage used a larger, more heterogeneous dataset for robust feature learning, while the second refined the model on a smaller, higher-quality subset to improve domain adaptation and detection performance. The resulting dataset comprised 4,202 image tiles (pretraining) and 706 image tiles (fine-tuning), with 16,096 and 1,716 vessel annotations, respectively, all belonging to a single target class.

Detection experiments were conducted with several YOLOv26 configurations trained under a consistent protocol to assess the trade-off between model complexity, accuracy and computational efficiency. Among the standard variants, YOLOv26-M achieved the most balanced performance, with a Precision of 0.813, Recall of 0.846, F1-score of 0.829, Accuracy of 0.719 and mAP50-95 of 0.306. Pruned and lightweight alternatives showed competitive efficiency-oriented behaviour. Results indicate that, in small-target coastal environments, scaling up model size does not necessarily yield proportional gains, whereas task-oriented architectural design improves the balance between detection quality and computational cost. The workflow provides a practical benchmark for AI-enabled maritime monitoring and supports the advancement of Copernicus-oriented coastal surveillance applications.

The growing use of artificial intelligence (AI) and data-driven methods in architectural heritage research requires structured and reusable semantic units to support consistent modelling, annotation, and knowledge alignment. In this context, Historic Building Information Modelling (HBIM) can serve as a semantic anchor by linking surveyed geometry with object-based representations and non-geometric information. However, current HBIM workflows remain semantically fragmented: point cloud segmentation often relies on project-specific labels, object modelling adopts inconsistent decomposition and naming logics, and semantic enrichment is frequently implemented through custom parameters without a shared component-level framework. Although Industry Foundation Classes (IFC) provide the most widely adopted canonical structure for interoperability, their standard entities are often too coarse to represent heritage-specific subcomponents. To address this gap, this study proposes an IFC-compatible semantic framework for component-level representation in HBIM. The framework combines a canonical IFC-aligned layer with a heritage extension layer and introduces a mapping strategy for representing semantically meaningful subcomponents without modifying the core IFC schema. A Serliana arch on the church of SS. Paolo e Barnaba in Milan is used as a case study to illustrate the implementation of the proposed approach. The study establishes a preliminary semantic foundation for component-level heritage representation in HBIM, providing both a conceptual basis for structuring heritage subcomponents and an operational basis for their IFC-compatible implementation. This foundation may also support future developments in ontology alignment and cross-modal AI applications, where stable semantic anchors are required for data integration and annotation.

Thousand-Buddha niches on the walls of grotto temples are core carriers of China's Buddhist cultural heritage. Their high-precision digital extraction is a key prerequisite for virtual restoration of cultural relics, stylistic lineage research, and digital display. Currently, close-range photogrammetry is mostly used for digital acquisition of small and medium-sized grotto temples to obtain point clouds. This technology, through non-contact multi-view image collection and matching, can not only retain the fine morphological features of niches but also comply with the core requirement of "non-destructiveness" in cultural relic protection, making it the mainstream method for grotto temple point cloud collection. However, the segmentation of thousand-Buddha niche point clouds still faces two core challenges: first, the sample scarcity bottleneck in cultural relic scenes. Manual annotation of niches requires professional archaeological knowledge, which is time-consuming and labor-intensive, resulting in limited sample size that is difficult to support the full training of deep learning models; second, the segmentation adaptation problem of target characteristics. Niches are densely distributed with similar shapes, and point clouds from close-range photogrammetry are prone to local noise due to lighting differences. Traditional segmentation methods are prone to boundary blurring, misclassification, and missing segmentation. Pure transfer learning without combining the characteristics of cultural relic scenes leads to insufficient segmentation accuracy.

Remote sensing image super-resolution aims to recover fine structural and textural details from degraded low-resolution observations. However, conventional methods and early deep learning models often produce over-smoothed results and struggle to reconstruct realistic high-frequency content. Stable Diffusion-based (SD-based) methods offer a promising alternative by using strong generative priors to synthesize more natural, detail-rich super-resolved images. Although many SD-based super-resolution methods have been proposed in computer vision, their use in remote sensing imagery remains limited, and systematic comparative evaluation in this domain is still lacking, leaving insufficient empirical guidance for method development. Therefore, this paper compares four representative SD-based super-resolution methods, namely Stable Super-Resolution (StableSR), Semantics-Aware Super-Resolution (SeeSR), Different Blind Image Restoration (DiffBIR), and Pixel-Aware Stable Diffusion (PASD), on the WHU-Mix remote sensing dataset. The evaluation uses seven metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), Frechet Inception Distance (FID), CLIP Image Quality Assessment (CLIP-IQA), Multi-Scale Image Quality Transformer (MUSIQ), and Multi-Dimension Attention Network for No-Reference Image Quality Assessment (MANIQA). Quantitative results show that StableSR achieves the highest PSNR of 23.16 dB, PASD obtains the best SSIM of 0.81 and lowest LPIPS of 0.45, SeeSR achieves the best MUSIQ of 64.57 and MANIQA of 0.46, and DiffBIR achieves the best FID of 110.58 and CLIP-IQA of 0.68 but with weaker full-reference fidelity. These findings indicate that current SD-based methods favor different aspects, including fidelity preservation, perceptual quality, and generative realism, and should be selected according to the target remote sensing application.

Monocular depth estimation (MDE) infers depth from a single image, offering significant advantages in computational efficiency and memory consumption compared to conventional Multi-View Stereo (MVS) methods. However, most MDE methods suffer from poor multi-view geometric consistency, which limits their application to photogrammetric 3D reconstruction. To address this issue, this paper employs sparse point clouds of Structure-from-Motion (SfM) as extra geometric constraints and proposes a framework that achieves photogrammetric 3D reconstruction using off-the-shelf learning-based MDE models without the need for additional fine-tuning. Specifically, when SfM priors are available during inference, globally geometrically consistent depth maps can be directly predicted. Otherwise, the estimated monocular depths are aligned to a consistent scale using SfM results via a post-correction step. The resulting depth maps are then fused using a truncated signed distance function (TSDF) to generate dense 3D reconstructions. Experiments on photogrammetric datasets demonstrate that the proposed framework effectively improves geometric consistency across depth maps and enables high-quality scene reconstruction. In addition, we systematically analyze the impact of key parameters in depth inference and fusion, including depth map resolution, voxel size, denoising steps, and ensemble size, on reconstruction performance, and further explore the potential of MDE for photogrammetric 3D reconstruction.

Recognising individual trees has important applications in forest ecology and management. Conventional individual tree segmentation methods tend to favour dominant trees with pronounced canopy surface features but have limited capability in detecting subdominant trees that are partially occluded or have smaller crowns. To mitigate this issue, we propose a morphology-based method for individual tree segmentation. First, a treetop extraction method is developed based on morphological criteria. Candidate treetops are initially detected using local maximum filtering, followed by classification and validation through vertical profile analysis integrated with crown morphological characteristics. Subsequently, the extracted treetops serve as seed points to guide individual tree crown delineation within a Min cut/Max flow graph cut framework, leveraging the spatial relationships among points. Our method enhances the detection of subdominant trees, with detection rates climbing to 90–95%, and achieves an average F score of 0.8 for crown delineation, which outperforms the other methods by 0.24 points. By integrating treetop information with local crown features, the proposed method improves the detection and segmentation accuracy of subdominant trees in complex forest environments, supporting overstory structure analysis and individual tree inventory in intricate forests.

This paper presents an automated approach for quantifying corrosion surface areas in steel transmission towers by integrating Unmanned Aerial Vehicle (UAV) photogrammetry and deep convolutional neural networks (DCNNs). Traditional visual inspections for corrosion pose significant challenges to structural safety and maintenance planning due to their complexity, subjective nature, high costs, and safety risks associated with inspecting tall structures. The proposed methodology utilizes a DeepLabv3+ model for the semantic segmentation of corroded areas. The network was trained and validated using a robust dataset of 999 field photographs collected from on-field tower inspections. A comparative analysis of DCNN backbones identified MobileNetV2 as the optimal choice, offering a superior balance between accuracy and computational efficiency. After fine-tuning, the network achieved an acceptable validation accuracy of 90.8% and a validation loss of 0.23. A major contribution of this study is the integration of these deep learning algorithms with metrically accurate photogrammetric products. The trained network was applied to orthomosaics derived from the 3D reconstruction of the South-East tower at the Torino Eremo broadcasting center. Unlike traditional image segmentation which lacks spatial reference, the photogrammetric approach enables the quantification and localization of the corrosion extent in exact physical dimensions. The high accuracy of the orthomosaic was confirmed against ground-truth measurements, achieving a root mean square error of 0.87 mm.

This automated, deep learning-based framework streamlines the detection process, provides reliable and quantitative data for assessing structural integrity, and represents a significant advancement over manual inspections, enhancing the overall efficiency, safety, and accuracy of infrastructure maintenance

Accurate identification of urban building functions is crucial for smart city planning and sustainable development. AlphaEarth Foundations introduce a new paradigm in remote sensing by providing semantically rich, pre-trained embeddings that integrate multi-sensor, spatiotemporal, and contextual information. In this study, we propose a novel fusion of 64-dimensional AlphaEarth embeddings and OpenStreetMap (OSM) derived building spatial indicators. We use the city of Toulouse as the study area, with the French official OCS GE database providing the ground truth labels. A random forest classification model was constructed, and the classification performance of single-source versus multi-source feature fusion was systematically compared. Results demonstrate that the multi-source feature fusion model achieves optimal classification performance, with an overall accuracy of 72.1\%, significantly surpassing models relying solely on embedding features (68.7\%) or spatial features (53.3\%).

The findings demonstrate the effectiveness and superiority of integrating AlphaEarth embeddings and OSM-derived building spatial indicators for automated urban building function identification, and provide a reliable technical approach for achieving large-scale and high-precision urban functional mapping.

We present an LLM-driven framework for retrieving remote sensing data from cloud-based geospatial catalogues using natural language queries. The system converts user intent into structured API calls, enabling efficient access to satellite imagery and environmental datasets. The architecture integrates three agents: Guardrail for safety and policy enforcement, General-QA for intent interpretation, and Recommender-Analyst for schema-aware API call generation. This coordinated design ensures reliable, semantically aligned interaction with external data services. The modular framework is portable across platforms through API schema substitution and supports applications in environmental monitoring, disaster response, and climate analysis. It establishes a scalable interface between user intent and geospatial infrastructure, enabling streamlined and automated Earth observation workflows. Preliminary experiments under adversarial multi-turn settings show that prompt-level safety instructions improve robustness, although rare high-impact failures persist in API manipulation scenarios and highlight the need for adaptive, system-level defenses that balance safety, usability, and cost efficiency, which motivates the use of our intercept-level Guardrail agent.

This paper introduces the second version of an end-to-end framework, which is the EDGE-Solar Farm Observation System (EDGE-SFOS v2.0). This system was developed for real-time solar farm defect detection with Edge generative detectors using drone images.

This study addresses the critical issue of systematic biases in gridded meteorological datasets, which can lead to inaccurate agricultural predictions and flawed decision-making. The primary objective is to develop a unified, high-accuracy meteorological dataset for Manitoba to support agricultural applications. The study focuses on the 2005–2024 period and on key variables commonly used in agriculture, including minimum temperature, maximum temperature, precipitation, and solar radiation. The methodology involves two main stages. First, four widely used national and international gridded datasets, ERA5-Land, Daymet, CHIRPS, and ANUSPLIN, will be benchmarked by comparing gridded values extracted at the locations of more than 120 Manitoba weather stations with the corresponding station observations. Second, the best-performing dataset for each variable will be selected for bias adjustment. Traditional statistical methods, such as Linear Scaling and Quantile Mapping, will be compared with machine-learning and deep-learning approaches, including Linear Regression, Random Forest, XGBoost, DNN, LSTM, and 1D-CNN. The study is expected to provide a quantified assessment of dataset reliability for Manitoba and to produce an improved bias-adjusted meteorological dataset for regional applications. The resulting dataset is intended to support more accurate agro-climatic assessments, regional yield estimation, and crop modelling, while also offering a scalable framework for similar agricultural regions.

Accurate mapping of urban tree canopy is essential for quantifying ecosystem services and assessing the impact of green infrastructure on wellbeing and public health. This study evaluates and compares three Geospatial Artificial Intelligence (GeoAI) frameworks for the automated detection and segmentation of tree cover. The frameworks are YOLO, Detectree, and TreeEyed Utilizing high-resolution aerial imagery (0.2 m and 0.5 m ground sampling distance), the research tests different deep-learning paradigms, including object detection and semantic segmentation. The results indicate that while object-based models like YOLO align closely with statistical baselines (30.83% vs 30.11%), pixel-based models such as Detectree may underestimate fragmented urban vegetation. The study highlights the effectiveness of the TreeEyed QGIS plugin for urban applications and emphasizes the necessity of local LiDAR-derived data for model validation. Further studies would benefit from ad-hoc training with correct co-registration and consistent coordinate reference systems across layers.

Maritime vehicles face significant positioning challenges under adverse weather conditions where visual and laser SLAM systems suffer from severe degradation. Millimeter-wave radar offers inherent robustness to weather interference, yet single-band radar cannot simultaneously achieve accurate translation and robust attitude estimation.This paper proposes a complementary fusion framework for multi-band radar odometry.This system leverages W-band radar (CFEAR) for reliable translation estimation and combines it with X-band radar (LodeStar) to improve rotational estimation robustness. The main innovations are as follows:(1) A complementary fusion framework exploiting the complementary characteristics of W-band and X-band radar; (2) A quality-aware adaptive weighting mechanism dynamically computing fusion weights based on sensor data quality assessment; (3) A consistency gating mechanism monitoring inter-sensor agreement and activating protective measures during sensor degradation.Experiments on the MOANA maritime dataset demonstrate that the proposed method achieves stable and reliable local motion estimation, reaching an RTE RMSE of 1.67 m on the Near-Port sequence.

In recent years, Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have emerged as advanced methods for photogrammetry-based 3D reconstruction. Since its introduction in 2020, NeRF has gained significant attention due to its capability to generate high-fidelity reconstructions from multi-view imagery. More recently, 3D Gaussian Splatting (3DGS), introduced in 2023, has proposed an alternative explicit scene representation based on a collection of anisotropic Gaussian primitives optimized directly in 3D space. This representation allows efficient rendering and scalable modelling of complex scenes while maintaining high visual quality. This paper analyses the performance of different 3DGS methods when dealing with complex geometry and less-cooperative surfaces compared to standard SfM IM procedures. Included in the comparison is also the Mesh-In-the-Loop Gaussian Splatting for Detailed and Efficient Surface Reconstruction (MILo), a novel meshing method using Gaussian splats. Three Gaussian splatting methods as implemented in the Postshot commercial software were also tested. Our experiments show that MILo shows very promising results in terms of detail reconstruction, while standard Gaussian splatting excels in visualisation but is still plagued by a high rate of noise especially when converted into a geometric point cloud form.

In this paper, we present the design and pilot implementation of Slovenia’s national geospatial Digital Twin (DT), coordinated by the Surveying and Mapping Authority of the Republic of Slovenia (GURS). Geospatial digital twins are enriched digital replicas of real world environments, dynamic models capturing past, present, and projected states to support geospatial decision making, location based services, and scenario simulations.

To demonstrate how the Slovenian Geospatial DT can be applied in practice, a prototype for modelling and managing flood hazards was developed. A flood-hazard prototype demonstrates the approach using the August 2023 event. The flood-modelling framework integrates very high-resolution (VHR) geospatial datasets with in situ environmental observations to ensure detailed spatial representation and analytical consistency. It combines ALS-derived terrain models with hydrological time series, meteorological forecasts, and satellite-based water detection from sources such as Sentinel-1/-2 and PlanetScope, enabling three-dimensional simulation and visualisation of flood dynamics.

The results show how a geospatial DT can transform authoritative datasets into operational intelligence for emergency management, spatial planning and climate-risk scenarios. Beyond floods, the architecture generalises to landslides, drought monitoring, infrastructure condition assessment and biodiversity applications.

Post-construction vegetation monitoring along linear infrastructures is increasingly required to support evidence-based restoration assessment, yet conventional ground surveys remain spatially sparse and difficult to scale over narrow, heterogeneous corridors. This limitation is particularly critical in recently replanted pipeline clearings, where plant-level restoration outcomes must be inferred under operational constraints and where satellite-based monitoring cannot reliably resolve early post-restoration signals at the scale of individual saplings. This study addresses the problem by developing a UAV data-fusion workflow that integrates UAV laser scanning (ULS), UAV multispectral imagery (UAV-MS), and ultra-high-resolution UAV-RGB observations for sapling-level vitality assessment. The workflow was tested in two restored pipeline corridor sites in the central Apennines (Italy), Ponte Baffoni (4.6 ha) and Ca' Romano (1.4 ha), surveyed in May 2025. ULS data were used to detect and geolocate individual saplings, UAV-MS data were used to extract vegetation-index metrics (NDVI, GNDVI, NDRE), and UAV-RGB imagery supported plot-level expert validation. A PCA-based soft-labelling strategy generated proxy vitality labels, which were then used to train a Random Forest classifier to derive corridor-scale probabilistic maps of sapling vitality, subsequently expressed as ALIVE, DEAD, and UNCERTAIN classes. Random Forest classification achieved balanced accuracies of 0.78 and 0.83, respectively. The resulting corridor-scale maps suggested mortality rates of 48.9% in Ponte Baffoni and 40.0% in Ca' Romano. These results suggest that multi-sensor UAV fusion can provide spatially explicit, sapling-level indicators of restoration performance, complementing field surveys and supporting operational post-construction assessment in narrow restoration corridors.

Automatic building reconstruction is a strategic component for creating urban Digital Twins (DTs), enabling the generation of accurate and interoperable Level of Detail 2 (LOD2) models. These models provide an essential standard for applications such as Geographic Information Systems (GIS), energy and hydraulic simulations, and urban planning. To address these needs, the MEDUSA (MEDiterraneo: Uso Sostenibile dell’Ambiente) project, promoted by the University of Salerno and funded by the Italian Space Agency (ASI), developed an innovative pipeline. The method was optimized to model areas with complex geometries and articulated roofs, utilizing the Amalfi Coast as a test area. The developed workflow is based on the City3D algorithm, integrating LiDAR (Light Detection And Ranging) data with building footprints derived from the Regional Topographic Database (RTDB). The process involves point cloud segmentation to isolate buildings and the generation of a Triangulated Irregular Network (TIN) mesh. Roof contours are identified using edge detection operators, simplified into polylines, and regularized using geometric constraints like parallelism and orthogonality to ensure LOD2 compliance. Finally, polygons are vertically extruded and optimized through the PolyFit framework, ensuring closed and topologically correct polygonal models. To overcome computational challenges and LiDAR data variability, significant improvements were introduced, including process parallelization, alignment with the Digital Terrain Model (DTM), and batch management of GeoJSON files. These enhancements successfully increased the pipeline's robustness and efficiency. The enriched pipeline produces high-quality LOD2 models, laying a solid foundation for next-generation urban modeling capable of meeting the scalability and interoperability requirements of future smart cities.

The identification and systematic recording of industrial buildings pose significant challenges for modern monument preservation. In particular, system halls have shaped the industrial landscape since the 19th century but often elude complete documentation because of their widespread distribution. These buildings serve as vital witnesses to technical innovations and economic transformation; however, assessing their architectural value requires a comprehensive inventory to determine the rarity or preservation state of specific building types.

Deep learning (DL) approaches are commonly used for the automatic recording of these buildings in aerial photographs, where the primary obstacle is the scarcity of curated training datasets. We overcome this by employing generative AI, specifically Stable Diffusion (SD), to produce synthetic data. By fine-tuning the SD model with Low-Rank Adaptation (LoRA), we successfully replicate the appearance and textures of various hall types. To resolve the spatial incoherence and geometric inaccuracies inherent in standard text-to-image generation, we integrated ControlNet. This allows for precise structural grounding using semantic masks, where specific colors represent building types, and polygon shapes define their exact locations. The resulting model generates accurate synthetic samples that maintain both spectral authenticity and an accurate spatial layout. Their usability was assessed by training a building detection model on both the real and synthetic datasets, achieving 71.9 and 66.7 mIoU, respectively. Moreover, introducing a few real samples for validation during training increased the mIoU to 82.7. The detection results demonstrate that the synthetic dataset is a reliable source for training, yielding robust generalization.

The PRISMA hyperspectral narrow-band data covering part of the Jind district during the Kharif Season 2024 were acquired to discriminate between two rice varieties, namely High-Yielding Variety (HYV) and Aromatic Basmati. In this study, hyperspectral bands were selected from the 240 hyperspectral bands of PRISMA data using Selective Principal Component Analysis (SPCA), which is specifically useful for crop classification. The subset of 9 PRISMA hyperspectral bands corresponding to the Sentinel-2 MSI bands was selected for rice crop classification and variety discrimination. The main difference between PCA and SPCA is that SPCA chooses only a subset of bands depending on the desired objectives of the study. The first three principal components (PCs) explained over 98 % of the variance of all spectral bands. The scatter plots of PC-1 and PC-2 indicated that there is a clear distinction between HYV and Basmati rice varieties.

In the present analysis, narrow-band hyperspectral red-edge group indices, such as Ratio Vegetation Index (RVIs), Green Normalised Difference Vegetation Index (GNDVI), and Chlorophyll Green Index (Clgreen), were generated to study their effectiveness for rice variety discrimination. The Spectral Angle Mapper (SAM) algorithm was used for supervised classification, and the results were validated using the time series S1 and S2 classified data. The results of validation indicated that using single-date hyperspectral data with 30 m spatial resolution, it was possible to discriminate between Basmati and HYV rice; however, it was not possible to discriminate between traditional and evolved Basmati rice varieties.

Real-time visualization of cadastral information through augmented reality (AR) has emerged as a significant challenge for public authorities in recent years. This paper addresses the potential, usage, and challenges of AR in the public sector. The prototype developed for this study demonstrates the visualization of geospatial data from ALKIS (Amtliches Liegenschaftskatasterinformationssystem, engl. Authoritative Real Estate Cadastre Information System), visualizing the boundaries and points of parcels in AR. Field tests conducted within this study assess the accuracy and usability of the AR visualization. As part of the study, existing AR libraries and frameworks were evaluated to select the most suitable platform for the prototype. The research underlines the potential of AR for geospatial applications, although it points out current precision limitations in the absence of external GNSS (Global Navigation Satellite System) receivers. The outcomes demonstrate the capabilities of AR visualization in a geospatial context and provide concrete approaches for optimizing future applications and research initiatives.

Modelling old buildings according to BIM standards is challenging, as historical architecture often features complex geometries and subject-specific information that is difficult to classify. This applies also to historic timber roof structures. The geometric complexity of historic timber structures makes them laborious and time-consuming to model using standard 3D software. In the case of aged heritage wooden beams, a lot of additional information should be parameterised. This information is derived from optical analysis as well as timber geometry and surface features, what is usually omitted in Open BIM.

In this paper we demonstrate a pipeline of data transfer from smartphone-based interface analysing automatically wood strength factor to BIM. This prototype interface allowing wood knottiness estimation for assessment of unknown strength values by aged heritage timbers as well as information connection to BIM framework.

The exponential growth of remote sensing data in recent years has underscored the need for intelligent, fast, and user-friendly analytical tools. Despite advancements in platforms such as Google Earth Engine and ENVI, the computation of spectral indices still demands specialized expertise, considerable time, and complex parameter tuning. This study aims to reduce the complexity of spatial data analysis and enhance its accessibility for non-expert users by developing an intelligent system capable of transforming simple natural language commands into automated remote-sensing index calculations. The main innovation lies in integrating Large Language Models (LLMs) with geospatial processing to establish a lightweight, multilingual, and fully automated framework capable of identifying index types and selecting appropriate spectral bands from Landsat data. The system was implemented using the Bloomz-560m language model in combination with open-source image-processing engines and deployed as a web-based interface. Experimental results over Tehran demonstrated that the model outputs were highly consistent with those generated by Google Earth Engine and ENVI, achieving an RMSE of 0.016 and a correlation coefficient of R² = 0.957. The total processing time was under 45 seconds, with the entire workflow executed automatically without user intervention. By simplifying the analytical process and significantly reducing computation time, this framework represents a crucial step toward democratizing remote sensing and spatial analysis. It can be effectively applied to urban surface heat island (SUHI) monitoring, water resource management, and precision agriculture applications.

Global Earth Observation provides coarse LCLU maps, classifying complex urban areas as a single Built-Up class. This limits urban modeling and product validation. Conversely, local SLAM offers fine-grained semantic detail but suffers from large-scale drift and lacks a global coordinate system.

We introduce Urban-Graph, a novel AI fusion framework to bridge this gap. Our system centers on a semantic scene graph to manage multi-scale information. It fuses three data sources: satellite imagery as a global prior, vehicle-based SLAM for local semantic detail, and fixed roadside infrastructure for high-precision GNSS anchors. A factor graph optimizer integrates these local, global, and anchor constraints.

This process generates a large-scale, globally-consistent, and geospatially-anchored semantic map. This resulting graph serves a dual purpose. It provides a drift-free map for local systems and functions as a scalable, high-fidelity ground-truth product to automate the fine-grained validation and decomposition of coarse urban LCLU classes.

High-resolution 3D documentation of cultural heritage sites is essential for their preservation. While terrestrial laser scanning (TLS) remains the gold standard, it is often cost-intensive compared to photogrammetry. This study evaluates three image-based reconstruction techniques, Multi-View Stereo (MVS), Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), by applying them to a complex scene featuring a chapel and its surrounding vegetation, sensed from an uncrewed aerial vehicle (UAV). A hybrid TLS/MVS model provides a high-accuracy reference. Using identical interior and exterior camera parameters of the 105 UAV-acquired images, we generate dense point clouds with all methods and assess geometric accuracy and completeness using the M3C2 algorithm. Results show that MVS achieves superior accuracy (standard deviation of all M3C2 distances: MVS = 0.11 m, NeRF = 0.15 m), whereas NeRF attains up to 20% higher completeness, particularly in low-texture and vegetation-occluded regions. The 3DGS point cloud was deemed too sparse and was therefore not used for further analysis. The study highlights the potential of NeRFs to recover partially occluded or sparsely textured geometries that are challenging for MVS and suggests a complementary use of both approaches for cost-efficient documentation of cultural heritage.

This study presents a machine learning-driven approach to forecast forest fire risk in California’s high-risk regions, aiming to predict fire-prone areas one month in advance. By integrating static topographical features with dynamic vegetation indices—such as NDVI, NDWI, GPP, and LAI—and their derivative components like trend and Exponential Moving Average (EMA), the model captures critical indicators of vegetation health and moisture. Among several algorithms tested, Logistic Regression (LR) consistently outperformed others, achieving a validation AUC of 0.90 when combining static and dynamic features. A 12-month historical time window proved most effective, enabling the model to learn seasonal and long-term vegetation patterns. Validation on independent datasets showed promising results for 2021 (AUC 0.84), though performance dropped in 2024 (AUC 0.64), likely due to satellite data shifts and ecological changes. These findings underscore the importance of long-term vegetation monitoring and robust feature engineering for accurate fire risk prediction. The study offers a practical tool for early warning systems, while highlighting the need to address data variability and environmental dynamics for sustained performance.

This work showcases mapping a natural disaster using social media posts of users (tweets) during the ongoing event. We have finetuned an encoder-decoder model and created a model that detects toponyms from tweets very efficiently. Toponyms are then resolved to geographical coordinates and features so that temporal heatmaps can be created effectively mapping the natural disasters through social media posts.

Encoder-decoder models are generally used for machine translation or summarization tasks in NLP. We show that through finetuning with proper data, a lightweight encoder-decoder model deployed locally can generate comparable results to prompting web-deployed large language models.

High-resolution mapping of urban land cover and urban green infrastructure (IVU) is essential for medium-sized cities, where global datasets often fail to capture fine-scale patterns. This study presents a local-scale land-cover classification for Alta Gracia, Argentina. The approach integrates medium- and high-resolution imagery with object-based segmentation (SNIC) and Random Forest classification. Six vegetation indices (NDVI, EVI, SAVI, GNDVI, MSAVI, VARI) were used to enhance class separability, while PlanetScope mosaics and local orthophotos improve spatial detail. Accuracy was assessed using overall accuracy, Cohen’s Kappa, and F1 Score.

The resulting land-cover map was used to delineate and quantify urban green infrastructure. Green-cover areas were summarized across city-defined sectors. Results were compared with regional layers from IDECOR and the global Dynamic World product, showing that global datasets underestimate fine-scale vegetation and fail to capture small or fragmented patches. The high-resolution local map substantially improves spatial accuracy and IVU delineation, serving as a baseline for urban planning, green-space management, and climate-resilient strategies.

This study demonstrates the value of combining multi-source imagery, object-based methods, and machine-learning classification to refine local land-cover mapping and IVU assessment. The methodology is reproducible using open-source tools (Google Earth Engine, QGIS, and R) and transferable to other medium-sized Latin American cities with limited data availability. Future work will integrate LiDAR-derived canopy metrics and citizen science to validate and enhance local products.

This contribution links local mapping with broader land-cover/use frameworks, supporting the ISPRS ThS21 global–local dialogue and providing actionable evidence for sustainable urban development.

This work presents a hybrid camera calibration approach that combines the strengths of standard photogrammetric camera calibration with data-driven lens distortions correction. Conventional calibration methods, such as those based on Zhang’s model, estimate lens distortions by fitting polynomial functions to the calibration images coordinates. While these methods are well established, they may struggle to fully describe complex or setup-dependent distortions, particularly near image borders or under varying environmental conditions.

To address this, a learning-based model is introduced to directly predict the distortion coefficients from calibration images. The network is trained using real data, allowing it to capture lens- or condition-specific variations that conventional calibration may overlook. The predicted coefficients maintain the same format as those used in standard photogrammetric models, ensuring compatibility with existing calibration toolchains such as OpenCV or MATLAB.

The proposed approach, therefore, aims to automate the estimation of distortion parameters while preserving the interpretability and mathematical foundations of traditional models. Although the primary focus is on camera calibration, the method offers further advantages for optical metrology systems such as fringe projection, where accurate and consistent distortion compensation is essential for depth measurement reliability.

Digital twins play a crucial role in autonomous driving applications and transportation system simulations. The need for large scale and dynamic information has increased interest in generating urban digital twins from remote sensing data.

Aerial high resolution imagery of urban areas serves as the one of the most important data sources for this task. Advances in deep learning and machine learning allow more accurate and automated extraction of urban elements. In recent years, we have developed and integrated advanced deep learning models to extract various land cover types surrounding road networks, including buildings, roads, and vegetation. Furthermore, we have conducted proof of concept studies aimed at detecting and delineating linear landmarks from aerial imagery, including curbstones and road borders. These developments contribute to the creation of more accurate and detailed urban digital twins, which are essential for advanced urban analytics and intelligent transportation systems.

Results from the deep learning models are presented for the Schwarzer Berg district in Brunswick, Germany, which is a test region for the development of mobility services and technologies at the German Aerospace Center (DLR). The AI models are trained using benchmark datasets from other urban regions, indicating that the proposed approaches can be readily transferred and evaluated in other European cities.

In this work, we demonstrate our early results in geocoding oceangraphic research articles across Canada. Through the use of AI, we extracted locations out of the abstracts of research articles and then assigned a latitude and longitude to those works based off of the locations extracted. The geocoded works are then displayed. Our work allows a user to identify locates across Canada that are being actively researched and find research specialists of those locations. We intend to develop this tool further by collaborating with journalists.

Land use and land cover (LULC) classification plays a crucial role in addressing numerous real-world challenges. Hence, we proposed methodological advances in LULC classification from a data-centric artificial intelligence perspective, which prioritizes data quality as a key factor in improving machine learning performance. The main contributions include evaluations of novel approaches for: (i) constructing an accurately labeled dataset based on agreement among existing reliable maps; (ii) curating remote sensing data to improve accuracy, consistency, unbiasedness, relevance, diversity, and completeness; (iii) generating training samples that capture the spatial, temporal, and spectral dimensions of remote sensing data; and (iv) developing a deep learning model designed to leverage multidimensional features. The study evaluates a sample generation method grounded in reference map agreement and multidimensional feature extraction, along with a deep learning model that leverages these features, attaining high accuracy across all LULC classes and providing a robust basis for large-scale, data-centric LULC mapping.

Global forested areas are decreasing at a rapid rate, leading to environmental instability, altered climate patterns, and a decline in ecosystem services. In the present study, the Western Ghat (WG) region is one of the major forest resources in the Indian southern peninsula; it regulates/balances the weather conditions with the unique features of high-rise mountains and tall trees. This mountain chain is recognised as one of the world’s eight ‘hottest hotspots’ of biological diversity. These mountains cover an area of approximately 140,000 km² along a 1,600 km long stretch, traversing the states of Kerala, Tamil Nadu, Karnataka, Maharashtra, Goa, and Gujarat. This region is one of the richest biodiverse hotspots and biosphere reserves identified by UNESCO. The WG region is of immense global importance for the conservation of biological diversity and endemism. This region encompasses a number of protection regimes, ranging from Tiger Reserves, National Parks, Wildlife Sanctuaries, and Reserved Forests. The forests of the WG include some of the world's best representatives of non-equatorial tropical evergreen forests. Around 325 globally threatened species (IUCN Red List) occur in the Western Ghats, of which 129 are classified as vulnerable, 145 as endangered, and 51 as critically endangered.

This research introduces an innovative platform designed to enhance citizen engagement in urban planning and management by integrating emerging technologies such as Artificial Intelligence (AI), Large Language Models (LLMs), and chatbots. Traditional Public Participation Geographic Information Systems (PPGIS) often face challenges in effectively capturing and analyzing citizen input. This platform addresses these limitations by enabling users to articulate urban issues or ideas in natural language, which are then processed through AI-driven Natural Language Processing (NLP) techniques to identify key elements such as location, issue type, and intensity. Furthermore, the platform facilitates interactive dialogues, allowing citizens to inquire about perspectives from other community members, thereby fostering a dynamic exchange of views. In the absence of an initial user base, a dataset comprising 2,000 tweets related to Montreal's public transportation was curated. An LLM was fine-tuned using this data, equipping the model to respond to queries concerning Montreal's public transportation system. The findings demonstrate the feasibility of leveraging AI and LLMs to create a responsive and interactive platform that not only streamlines data collection but also enriches the participatory planning process. This approach has the potential to transform urban governance by making it more inclusive and data driven.

Remote Sensing Image-Text Retrieval (RSITR) aims to retrieve target textual descriptions from the gallery images, and vice versa. RSITR faces the key challenge of establishing accurate alignment between two heterogeneous modalities. Existing methods typically assume that image-text pairs are semantically aligned, where each textual description corresponds to a single image. However, this assumption does not always hold because factual errors in textual descriptions lead to incorrectly-correlated relationships. Moreover, some samples exhibit weakly-correlated relationships, i.e., an image corresponds to multiple similar texts. These incorrectly- and weakly-correlated relationships hinder effective cross-modal alignment. To address these challenges, we propose the Robust Dual Embedding Alignment (RDEA) network, which improves the robustness of cross-modal alignment by jointly learning both instance-level and feature-level correspondence between image and text modalities. Firstly, we propose an Incorrectly-Correlated Feature Rectification (ICFR) module, which employs a dynamic margin-guided mechanism to adaptively balance original and auxiliary descriptions generated by a large language model, guiding the model to learn correct image-text correspondences at the instance-level. Secondly, a Weakly-Correlated Feature Decoupling (WCFD) module constructs modality-specific intermediate features via learnable distributions, which decouple overlapping semantics across modalities. These intermediate features enable the model to distinguish semantically similar texts, thereby establishing more discriminative and accurate image-text correspondences at the feature-level. We conduct extensive experiments on benchmark datasets, demonstrating that our approach outperforms state-of-the-art methods.

This contribution presents an operational framework that advances BIM–SAR fusion into a commercial, API-based Digital Twin service for building deformation monitoring. Building on the BIMSAR research project, the system integrates multi-frequency MTInSAR results from Sentinel-1, TerraSAR-X, and PALSAR-2 with IFC-based BIM models to provide semantically structured deformation indicators for individual building components. Persistent and distributed scatterer analyses generate millimetre-scale deformation time series, which are stored in a harmonized database and exposed through a RESTful API that supports standardized queries for deformation values, risk metrics, and metadata. A pilot implementation in Ahlen, Germany, demonstrates the service’s interoperability with existing digital twin platforms and validates the workflow using previously established BIMSAR datasets. Developed jointly by EFTAS Remote Sensing and EnviroTrust, the system showcases the successful transition of research-driven BIM–SAR fusion methods into an operational, cloud-ready monitoring service supporting resilient building and infrastructure management.

Accurate tree species classification is fundamental to forest ecology, biodiversity monitoring, and sustainable resource management. However, large-scale species-level labeling in remote sensing remains challenging due to the need for expert annotation and the limited generalization of supervised models. This study introduces TreeCLIP, an unsupervised framework that adapts the CLIP vision–language model for ecological analysis through multi-view feature fusion.

TreeCLIP renders each individual tree point cloud into multiple orthogonal 2D projections that capture its geometric and morphological characteristics. CLIP’s pre-trained image encoder extracts visual embeddings from each view, which are then L2-normalized and fused into a unified multi-view representation. By applying clustering methods such as K-means and DBSCAN, TreeCLIP achieves species-level grouping without any manually defined textual prompts or labeled training data.

Experiments on multi-platform airborne laser scanning datasets from German forest stands demonstrate that TreeCLIP surpasses traditional machine learning approaches (e.g., Random Forest, SVM) and achieves accuracy comparable to supervised deep models. The results highlight CLIP’s capacity to generalize across domains and reveal the potential of foundation models for fine-grained ecological recognition.

TreeCLIP provides a scalable, annotation-efficient framework for large-scale forest inventory and vegetation monitoring, bridging the gap between general-purpose vision–language models and domain-specific ecological applications.

Construction sites are complex, dynamic environments that demand accurate, real-time monitoring for progress and safety management. Traditional on-site supervision and image-based UAV monitoring often fall short in providing detailed and timely 3D information. Recent digital twin technologies offer virtual replicas of construction sites, but existing 3D reconstruction methods—typically relying on LiDAR or depth cameras—remain limited by high hardware costs, heavy energy consumption, and extensive manual annotation requirements.

This study investigates the feasibility of applying 3D Gaussian Splatting (3DGS) for 3D scene reconstruction and segmentation in digital twin–based construction monitoring. Leveraging only visual inputs, 3DGS enables high-fidelity modeling while avoiding costly hardware. Combined with foundation models such as the Segment Anything Model (SAM), it supports unsupervised or weakly supervised segmentation adaptable to continuously evolving site conditions. Moreover, integrating 3DGS with large vision–language models allows for interactive segmentation through clicks or natural language prompts, advancing toward intelligent and adaptive digital twins.

We evaluate several Gaussian-based segmentation algorithms on construction-related datasets, assessing their effectiveness in capturing structural details and object semantics. Results show that 3DGS-based methods achieve promising segmentation quality for simple geometric objects but face challenges in complex, cluttered environments. These findings highlight both the potential and current limitations of 3DGS in realizing fully automated, adaptive digital twins for smart construction management.

EarthDaily FM is a foundation model purpose-built for high-frequency change detection and short- to medium-range forecasting across global Earth Observation (EO) time series. It is designed around the forthcoming EarthDaily Constellation (EDC)—a systematic, near-daily mission with 22 VNIR, SWIR, and LWIR bands engineered for AI-ready analytics, high geolocation and radiometric accuracy, CEOS ARD compliance, and spectral compatibility with Sentinel-2 and Landsat. This design enables a single self-supervised model to fuse years of historical S2/Landsat data with new daily EDC observations, closing the temporal gap that constrains existing EO foundation models focused on static scene understanding.

Preliminary experiments using open and proxy datasets demonstrate the model’s capability for diverse forecasting tasks, including harvest date prediction, crop yield estimation, and soil moisture retrieval. Using VENµS imagery as a proxy for EDC’s cadence and 5-m resolution, the model achieves low median errors in harvest date prediction at 50–60-day lead times, while multimodal training with meteorological and radar inputs improves soil moisture estimation. The impact of incorporating EarthDaily Constellation data on forecasting accuracy and model generalization will be demonstrated as new observations become available.

EarthDaily FM represents a practical step toward operational, time-aware EO modeling—integrating optical, radar, and weather data to support forecasting in agriculture, water resources, and environmental resilience.

We propose an innovative method to improve the reliability of Planet Fusion surface reflectance during periods of extended cloud cover. Planet Fusion offers daily, 3 m, cloud-free data (RGB-NIR) by radiometrically harmonizing all available PlanetScope imagery using the CESTEM algorithm, which employs MODIS/VIIRS and FORCE data for correction, and then uses a spatially and temporally driven gap-filling algorithm to ensure spatial completeness. A critical weakness arises during prolonged cloudiness, where the certainty of Planet Fusion's gap-filled pixels diminishes.

The proposed research directly addresses this weakness by incorporating Sentinel-1 synthetic aperture radar and AMSR-2 brightness temperature data. Both Sentinel-1 and AMSR-2 operate in the microwave spectrum, guaranteeing data acquisition regardless of weather or light conditions. By fusing these multi-sensor, multi-modal datasets into the Planet Fusion workflow, we are able to improve the accuracy of gap-filled pixels during months-long periods of persistent cloud cover. This work not only seeks to increase the reliability of the Planet Fusion product, but also advances the field of multi-modal data fusion, highlighting its necessity for uninterrupted, observation-driven monitoring of land surface change from space.

This work presents the development of a web‑based interface designed to support both on‑site and remote exploration of environmental sensor deployments.

The growing accessibility and standardisation of IoT technologies have led to their adoption across diverse fields, including environmental studies, urban planning, architecture, agriculture, archaeology, and museum studies, yet shared challenges persist around planning, deployment, interpretation, and communication of sensor data. When multiple disciplines operate within the same test environment, their activities can affect one another, highlighting the need for interfaces that reduce disciplinary barriers and rely on spatially grounded visualisation rather than domain‑specific terminology. The system builds on principles of Situated Analytics, enabling data to be interpreted directly within its spatial or contextual setting while also supporting remote interaction through proxy representations of real‑world environments.

In this contribution, three modelling techniques, dense point cloud, 3D Tiles, and Gaussian Splatting, were generated from drone images and integrated into a Babylon.js platform. A WebAR application, developed with 8th Wall, allowed sensor locations to be placed in situ, with data visualised through a shared information layer using MQTT to stream live or simulated readings.

The results indicate promising developments for cross‑disciplinary knowledge exchange through accessible, device‑agnostic web tools. Ongoing work explores the improvements to point‑cloud handling, AR localisation accuracy, and the long‑term collection of historical environmental data.

To address the common deterioration of ancient Chinese murals—including pigment loss, texture blurring, and color fading—this paper proposes a deep learning-based approach integrating multi-scale attention and texture enhancement modules for high-fidelity virtual restoration. The model employs a multi-scale attention mechanism to maintain structural continuity and a dedicated texture enhancement module to recover fine details often lost in conventional methods. The restoration process consists of three stages: multi-scale feature extraction using partial convolutions, feature reconstruction that transfers statistical properties from intact regions, and a texture refinement module for detail completion. Evaluated on the Dunhuang mural dataset, the method outperforms existing techniques in PSNR, SSIM, and FID scores, producing visually coherent and stylistically consistent results. This approach offers a scalable and adaptable solution for digital conservation, supporting customizable restoration levels tailored to various degrees of damage.

Climate change and population growth intensify the demand for precise agriculture mapping to enhance food security. Such mapping tasks require robust modeling of multi-scale spatiotemporal patterns from fine field textures to landscape context, and from short-term phenology to full growing-season dynamics. Existing methods often process spatial and temporal features separately, limiting their ability to capture essential agricultural dynamics. While transformer-based remote sensing foundation models (RSFMs) offer unified spatiotemporal modeling ability, most of them remain suboptimal: they either use fixed windows that ignore multi-scale crop characteristics or neglect temporal information entirely. To address these gaps, we propose AgriFM, a multi-source, multi-temporal foundation model for agriculture mapping. AgriFM introduces a synchronized spatiotemporal downsampling strategy within a Video Swin Transformer backbone, enabling efficient handling of long and variable-length satellite time series while preserving multi-scale spatial and phenological information. It is pre-trained on a globally representative dataset comprising over 25 million samples from MODIS, Landsat-8/9, and Sentinel-2 with land cover fractions as pre-training supervision. AgriFM further integrates a versatile decoder specifically designed to dynamically fuse multi-source features from different stages of backbone and accommodate varying temporal lengths, thereby supporting consistent and scalable agriculture mapping across diverse satellite sources and task requirements. It supports diverse tasks including agricultural land mapping, field boundary delineation, agricultural land use / land cover mapping, and specific crop mapping (e.g., winter wheat and paddy rice) with difference data sources. Comprehensive evaluations show that AgriFM consistently outperforms the general-purpose RSFMs across multiple agriculture mapping tasks.

An AI-driven framework for structural identification and stock analysis of bamboo scaffold systems to enable lifecycle management for firms, regulators, and workers. The method addresses irregular geometry, dense packing, and occlusions through three components. First, Node-guided Pole Fitting detects bamboo nodes and poles; the Bamboo of Building dataset trains a neural network to generate a Node Candidate Set. Within each node’s bounding box, Line Segment Detector (LSD) extracts linear features; representative segments are clustered, connected, and curve-fitted to model a pole. Second, multi-view 3D reconstruction maps the scaffold; cross-image matching projects poles into a unified space, refining NCS into Real Node Set and Fake Node Set for reliable topology. Third, a digital model estimates member lengths/diameters to quantify stock and potential CO2 reductions.

Agricultural drought is one of the most damaging natural hazards, causing ecological disruption, economic losses, and reduced crop yields. Recent extreme droughts in Central Europe, particularly after 2018, have underscored the need for reliable, spatially explicit drought monitoring. Traditional ground-based indices often fail to capture crop-specific physiological responses, while commonly used remote-sensing indicators, such as NDVI, are limited by soil background effects and saturation in dense vegetation. Sun-Induced Chlorophyll Fluorescence (SIF) directly reflects plant photosynthesis and responds sensitively to water and heat stress, making it a promising alternative for drought assessment.

Despite its potential, SIF-based drought monitoring remains largely unexplored in Germany. Most studies focus on specific regions or individual crops and rely on other remote-sensing indices rather than SIF. To fill this gap, this study evaluates whether multi-temporal SIF data can detect agricultural drought signals across Germany and how consistently these signals relate to crop yield anomalies. Using the Soil–Climate Regions (SCRs) of Germany as an ecologically meaningful spatial framework, we examine spatial correlations between SIF and yield across SCRs, and compare time-series SIF anomalies with average yield anomalies.

This research highlights the potential of SIF as an early and robust indicator of agricultural drought, offering insights for improved drought monitoring and crop management strategies in Germany.

Vision-based displacement measurement offers a promising pathway toward UAV-enabled structural monitoring, where contact-free, lightweight, and rapidly deployable sensing is essential. However, existing vision approaches typically estimate only 2D motion or require model training, artificial markers, or complex calibration, which hinders their applicability on real structures.

To address these limitations, this paper presents a new training-, marker-, and calibration-free vision-based framework designed with future UAV deployment in mind for structural 3D displacement measurement. Leveraging the reasoning capability of a state-of-the-art vision foundation model, the proposed method achieves millimeter-level 3D displacement accuracy without any scene-specific training, calibration, or fine-tuning.

To support rigorous evaluation, we establish a compact multi-modal dataset collected from two full-scale bridges, including synchronized stereo videos, accelerometer measurements, and an evaluation protocol. Experiments on real bridges demonstrate that the proposed framework delivers accurate, robust, and practical in-situ 3D displacement measurement under uncontrolled field conditions.

The system is inherently suited for airborne visual sensing, and integrating the framework with UAV-based data acquisition constitutes the next step of this research.

Invasive pests such as the desert locust are both detrimental to people and the environment. The desert locust is documented as one of the most destructive polyphagous plant pests. This study, about the integration of crowd-sourced field dataset and Google Earth Engine (GEE) satellite data, demonstrates how community-based initiatives and freely available cloud-based earth observation resources can be used to provide innovative, evidence-based and data-driven decision support insights that are of critical use to government agencies in desert locust crisis management. The study integrated 160,810 desert locust field survey records collected from January 2020 to December 2021, with vegetation and water indices time-series computed from Sentinel 2 bands B2, B3, B4, B8 and B11 on GEE. The results indicate that the peak of desert locust mature adult (67) and hopper (75) incidents coincided with the highest spectral index values in June 2020. However, the peak of desert locust immature adult (70) incidents in February 2021 coincided with low spectral index values. This means that spectral indices can be used to identify suitable breeding areas for desert locusts, but may not reliably identify all the areas where the pest might be present. Among the assessed indices, the Modified soil adjustment vegetation index (MSAVI) produced the best prediction with a β=0.703, t=6.983 and p=<0.001. The study concludes that, because Hotspot 1 denotes arid and semi-arid lands (ASAL), MSAVI would be the most suitable for monitoring desert locusts in this area, as the index accounts for soil brightness in the deserts.

This paper reviews the evolution, achievements, and future direction of remote sensing across the Great Lakes Basin (GLB), emphasizing the unique binational collaboration between the United States and Canada. Beginning with post–World War II aerial photography, remote sensing in the region rapidly expanded through pioneering work in forestry, water quality mapping, and early satellite-based observation. The formation of the Great Lakes Alliance for Remote Sensing (GLARS) marked a major step toward coordinated, cross-border environmental intelligence. Enabled in part by the Great Lakes Restoration Initiative (GLRI), GLARS brought together federal agencies, universities, and private partners to deliver high-resolution, multi-temporal products supporting natural resource management.

Key achievements include production of 2-meter digital surface models for the entire basin using petascale computing; integrated optical and SAR approaches for dynamic wetland mapping; multi-year RADARSAT-2 monitoring of seasonal wetland saturation; InSAR applications for water-level change detection; and successful classification of invasive species such as Phragmites australis using multi-sensor datasets.

Looking ahead, the paper identifies priorities such as harnessing new SAR missions (RCM, NISAR), expanding daily high-resolution multispectral monitoring, building fully automated analysis pipelines, and formalizing binational data-sharing systems. Continued integration of AI, cloud computing, and stakeholder-driven design is essential for climate-resilient management of the world’s largest freshwater system.

This work details the development of a comprehensive national assessment application for rooftop solar photovoltaic (PV) potential in Israel, designed to support the national target of 30% renewable electricity generation by 2030. Faced with limited land and increasing electricity demand, Israel's policy prioritizes PV installations on existing building rooftops.

The technological approach integrates solar radiation modeling, Deep Learning (AI) obstacle segmentation, GIS, and governmental data. The system utilizes advanced models incorporating DSM data, shading, and meteorological variables to calculate solar radiation. Crucially, multiple Convolutional Neural Network (CNN) models (U-net, Mask RCNN) were trained on high-resolution aerial imagery to accurately segment and deduct rooftop obstacles, such as existing PV systems, solar collectors, and vegetation, achieving over 95% IoU.

The final assessment feeds into a two-pronged system:

A Public Application allowing citizens and businesses to receive address-specific estimates of usable roof area, expected electricity production, and economic return on investment.

A National Management System and Dashboard for policymakers and local authorities, enabling spatial examination, progress monitoring, and data-driven strategy formulation (e.g., targeted encouragement campaigns).

This multi-level system, combining remote sensing, machine learning, and governmental data, provides an adaptable, data-driven framework for facilitating the renewable energy transition across all stakeholder levels.

Low-altitude remote sensing using unmanned aerial vehicles (UAVs) has become a crucial method for large-scale 3D reconstruction in various applications, including urban planning, environmental monitoring, and disaster management. However, due to issues such as proportion blurring, projection distortion, and failed loop closure, obtaining precise and dense 3D point cloud maps from monocular RGB cameras remains challenging. Recent advances in feed-forward 3D scene reconstruction, such as VGGT (Visual Geometry Grounded Transformer), which generates dense point clouds and camera poses from uncalibrated RGB images, offer potentially promising solutions. VGGT-SLAM extends this capability to large-scale scenes by aligning local submaps optimized on the SL(4) manifold, which addresses projective ambiguity that similarity transformations (Sim(3)) cannot resolve. The enhanced large-scale reconstruction capability of VGGT-SLAM is precisely what is needed for 3D reconstruction of remote sensing datasets. This study investigates the feasibility of applying VGGT-SLAM to UDD (Urban drone datasets) and highlights its limitations in real-world scenarios.

Accurate LiDAR–camera extrinsic calibration is crucial for reliable multi-sensor fusion in robotics, autonomous navigation, and UAV photogrammetry. This study presents a robust two stage LiDAR–camera calibration framework that integrates geometric and monocular depth assisted information constraints within a unified joint optimization scheme.

In the initial stage, geometric features from both LiDAR and camera views are extracted and aligned via Singular Value Decomposition (SVD) to provide stable initialization. The refined stage introduces a hybrid optimization that combines spatial distance constraints with a Normalized Mutual Information Distance (NID) term between LiDAR-measured depth and monocular depth estimation (MDE) results. The deep learning–based MDE provides dense and metrically consistent depth maps, effectively bridging the modality gap between 3D point clouds and 2D images. This dual-constraint formulation enhances calibration robustness against LiDAR sparsity and texture deficiencies. Experimental evaluations using a circular calibration target demonstrate mean rotational errors below 0.3° and translational errors under 3 cm, surpassing traditional FastCalib methods. Qualitative visualizations further confirm precise alignment between LiDAR projections and image contours. The proposed framework eliminates the need for precise calibration targets and manual initialization, achieving automatic, high-accuracy extrinsic calibration adaptable to complex outdoor environments

Extreme rainfall from Hurricane Helene (September 2024) triggered widespread landslides across the southern Appalachian region, highlighting the need for rapid landslide susceptibility assessments that capture both landslide initiation and downstream runout. Traditional susceptibility models often focus solely on initiation zones, limiting their ability to identify which slopes will generate destructive landslides or where material will travel. This study addresses that gap by (1) integrating Geographic Information System (GIS)-based machine learning susceptibility modeling using ArcGIS Pro: Maximum Entropy (MaxEnt) and Random Forest-Based and Boosted Classification and Regression (FBBC) and (2) the U.S. Geological Survey (USGS) Grfin (Growth, Flow, and Inundation) runout toolbox. The study focuses on the Nolichucky River Gorge in eastern Tennessee and western North Carolina, where intense rainfall (4-20 in;10.1-50.8 cm) triggered numerous shallow landslides. Results provide a framework for emergency response along TN-107 and US-19W corridors, infrastructure vulnerability assessments, and hazard planning in Unicoi and Carter counties.

The Surveying and Mapping Authority of the Republic of Slovenia (GURS) carried out nationwide airborne laser scanning project (CLSS) between 2023 and 2025, with a minimum spatial resolution of ten points per square metre across the entire territory of Slovenia. In 2025, the project for automated building extraction from the acquired LiDAR data was initiated, with the objective of systematically processing approximately one third of Slovenia’s territory per year. The automatically extracted building data (2.5D building footprints and 3D building models) will serve as a fundamental topographic dataset, a key source for detecting and monitoring changes in the Real Estate Cadastre, and a foundational dataset for property valuation at scale. Moreover, this initiative represents a pivotal step towards the establishment of a geospatial digital twin of Slovenia. The production workflow is based on an integrated processing method that combines a classified LiDAR point cloud (GKOT) and True Orthophoto imagery (POF) from CLSS. The quality evaluation is conducted in accordance with the international standard ISO 19157 — Geographic Information — Data Quality.

Climate change is expected to significantly alter environmental conditions in southern Sweden, increasing the risk of natural hazards such as wildfires. This study assesses wildfire susceptibility in forest areas of Scania under projected climate conditions corresponding to the Representative Concentration Pathways RCP8.5 scenario. Using Geographic Information Systems (GIS) and a fuzzy multicriteria decision analysis (MCDA), climatic variables (temperature, precipitation, wind speed) and forest type data were integrated to generate a continuous fire risk map. Forest types were reclassified based on fire susceptibility, and fuzzy membership functions were applied to climatic variables, with a fuzzy gamma overlay (γ = 0.6) used to combine criteria. Results indicate that several coastal and fragmented forest areas exhibit high wildfire risk, while northern inland regions show relatively lower susceptibility. The fuzzy approach enables a nuanced representation of risk gradients, providing valuable spatial information for climate adaptation and hazard mitigation planning. Despite limitations in input data and parameter quantification, the produced map highlights priority areas for monitoring and management under future climate scenarios.

The debut of the Constellation Optique en 3D (CO3D) in July 2025 represented a significant advancement in Earth observation. This state-of-the-art satellite mission captures the earth in breathtaking three dimensions by using four satellites in a novel out-of-phase tandem arrangement that mimics mammalian vision. CO3D produces high-resolution Digital Elevation Models (DEMs) at one-meter grid spacing with previously unheard-of accuracy—one-meter relative height precision and four-meter absolute height precision.

Synchronous stereo imaging enables tracking of moving objects even in the dark, and each CO3D satellite provides 0.50-meter resolution images in the red, green, blue, and near-infrared bands. This innovative technology, which offers cutting-edge capabilities for coastal monitoring, disaster response, urban planning, and climate research, helps the scientific, defense, and civil communities equally.

Applications for CO3D are numerous, ranging from improving post-disaster evaluations and urban resilience to tracking glaciers and coastal erosion. CO3D enables governments, businesses, and researchers to tackle important issues with unmatched accuracy by offering almost worldwide 3D data. Welcome to the era of CO3D, the future of Earth observation.

The oil slick look-alike challenge occurs when natural ocean phenomena reduce synthetic aperture radar (SAR) return in the same backscatter range as mineral oil. We revisit this challenge through the lens of geospatial foundation models (FMs), large neural networks which are a current frontier in automatic, deep learning-based mapping methods. In their benchmark evaluations, FMs promote state-of-the-art performance across a wide range of downstream tasks including segmentation. In contrast, our findings suggest that, in their current state, FMs do not outperform other neural network backbones for segmentation in an unconventional remote sensing modality such as SAR imaging of oceans. Surprisingly, backbones that were partly pretrained on SAR data do not show improved segmentation over those pretrained on natural images (here ImageNet). Rather than improving model backbones for segmentation, we argue that the breakthrough made by FMs may well lie elsewhere, such as in data management and pruning techniques. We make available the dataset used in our experiments, consisting of Sentinel-1 IW images annotated for semantic segmentation of oil slicks.

This study will evaluate whether the analysis-ready, global, cloud-free, annual, 10 m resolution embedding field layers of the Google AlphaEarth and Tessera foundation models can be used to improve estimation and prediction of biophysical variables such as live fuel moisture content, as well as contributing to an understanding of the global transferability of developed models to different regions.

Quantifying biodiversity intactness—a central indicator of ecosystem health and resilience—remains difficult across Africa due to scarce standardized baseline data and limited biodiversity monitoring. Traditional indicators based on vertebrates or vegetation provide only partial insights, as they respond more slowly to environmental change and have limited spatial coverage.

This study presents a novel, continent-wide framework that integrates multi-sensor Earth Observation (EO) data (Sentinel-2, GEDI, and TerraClimate) with extensive in situ insect occurrence records to derive an insect-based biodiversity intactness index (IBI). Insects, which dominate terrestrial biodiversity and respond rapidly to microclimatic and habitat changes, are used as sensitive ecological proxies for ecosystem condition. Their ubiquity and fine-scale environmental sensitivity make them particularly suited to detect patterns of habitat degradation and recovery that other taxa may overlook.

By coupling EO-derived indicators of vegetation structure, productivity, and climatic variability with insect diversity models, the framework provides spatially explicit, continuous estimates of ecosystem integrity across Africa. The resulting IBI fills a major information gap in biodiversity monitoring by offering a harmonized, scalable, and policy-relevant assessment tool.

The approach directly supports reporting needs under the Kunming–Montreal Global Biodiversity Framework (GBF) and African Union ecosystem restoration goals. It demonstrates how EO and biodiversity data integration can operationalize continent-wide monitoring of ecosystem condition—helping countries to track progress toward conservation and sustainable land-use targets through an ecologically grounded, insect-based lens.

Tree-species classification from multispectral remote sensing has advanced rapidly with the improved spatial and spectral capabilities of sensors such as Sentinel-2, enabling accurate discrimination of forest taxa across large areas. This paper deals with two approaches for tree species classification at the national scale using multi-temporal S2 imagery. We compare a machine learning algorithm (LightGBM) and a deep learning transformer-based model (ForestFormer) to classify dominant tree species in Slovenia based on seasonal characteristics. The resulting classifications are validated against National Forest Inventory datasets, provided by the Slovenian Forestry Institute.

This work explores the practicalities of emulating a closed-source Earth embedding AI model from a large set of its pre-computed outputs. It also demonstrates how behaviour of a multi-modal multi-temporal embedding dataset can be probed using individual observational inputs. The framework is tested using Major TOM Core datasets with Sentinel-2 and Sentinel-1 data and an existing global dataset of AlphaEarth Foundations embeddings.

In an ever-changing global context, accurate and up-to-date land use and land cover (LULC) information becomes critical to understanding the dynamics of the Earth surface and managing natural resources. Nowadays, a common workflow for LULC classification involves training a supervised machine learning model using satellite image time series (SITS) and a collection of ground truth (GT) samples. Unfortunately, GT data are not always available across years due to costly and time consuming field campaigns or restrictions on field access. For this reason, the possibility of transferring a model learned on a particular year (with GT data available) to another mapping year (without GT data) has received traction, recently. To cope with such temporal transfer scenario, unsupervised domain adaptation (UDA) has been considered in order to address possible data distribution shifts originating from different acquisition conditions affecting mapping years. In recent years, self-supervised learning has emerged as a promising paradigm to mitigate the reliance on large amounts of GT data through the learning of general purpose and robust feature representations, enabling the development of geospatial foundation models (GFM) in Earth observation. GFM, trained on large volume of multi-modal geospatial data can provide embeddings that encode rich spatio-temporal, spectral, and semantic information. A notable example is AlphaEarth satellite embedding, released lately on a global scale and annual basis for the seven past years. In this study, we propose to evaluate its potential for temporal transfer scenarios in LULC classification, using a multi-year open dataset collected in Burkina Faso, West Africa.

One of the primary objectives of Earth observation is to capture the complex dynamics of the Earth system using satellite image time series. This process encompasses tasks such as reconstructing continuous cloud-free image sequences, identifying changes in land cover types, and forecasting future surface evolution. However, existing methods typically require specialized models tailored to different tasks, lacking unified modeling of spatiotemporal features across multiple time series tasks. In this paper, we propose a Unified Time Series Generative Model (UniTS), a general framework applicable to various time series tasks, including time series reconstruction, time series cloud removal, time series semantic change detection, and time series forecasting. Based on the flow matching generative paradigm, UniTS constructs a deterministic evolution path from noise to targets under the guidance of task-specific conditions, achieving unified modeling of spatiotemporal representations for multiple tasks. Furthermore, we construct two high-quality multimodal time series datasets, TS-S12 and TS-S12CR, filling the gap of benchmark datasets for time series cloud removal and forecasting tasks. Extensive experiments demonstrate that UniTS exhibits exceptional generative and comprehension capabilities in both low-level and high-level time series tasks. More details can be found on the project page: https://yuxiangzhang-bit.github.io/UniTS-website/

Advancements in remote sensing technologies and spatial data analytics have continued to transform how we map and monitor landscapes, including urban and agroforestry systems. Land use and land cover (LULC) analysis provides useful insights for sustainable land management, especially for agricultural stakeholders. Cocoa production is an agricultural system that benefits greatly from appropriate land use management. The system provides economic stability for millions of households worldwide through job creation, livelihoods, and raw materials for confectionery industries. However, its sustainability faces growing threats from environmental and socioeconomic challenges, such as climate change, land use conflicts, and extensive deforestation. One serious threat to cocoa production, particularly in West Africa (which supplies over 70% of the world's cocoa), is the widespread occurrence of the cocoa swollen shoot virus, among other pests and diseases that substantially decrease annual yields.

Therefore, accurate and current maps of cocoa farms are required for managing deforestation, supporting disease monitoring, and guiding climate-resilient agricultural strategies in the region. Previous efforts in mapping cocoa landscapes with remote sensing have not achieved the desired results, partly due to their spectral similarity to forests and shrublands, especially where they are part of agroforestry systems. This study aims to overcome this challenge by developing a robust methodology for detecting full-sun cocoa plantations using high-resolution satellite imagery and machine learning techniques for sustainable land utilisation.

This research addresses the challenges in classifying patterns of textile cultural relics by developing a multi-modal feature fusion approach. Current methods struggle with fine-grained classification and cultural-period analysis due to fragmented data and insufficient feature integration. The proposed framework integrates high-resolution images, historical documents, and spectral data through Vision Transformers and BERT models, enhanced by a Feature Enhancement Fusion Module. Validation on Han and Tang dynasty textiles demonstrates 3-5% accuracy improvement in fine-grained classification while maintaining model size under 300MB. This research establishes a new paradigm for digital heritage preservation, enabling precise pattern recognition and cultural evolution analysis with practical applications in museums and digital curation.

This study examines the impact of various personal laser scanning (PLS) schemes on the accuracy of individual tree attribute estimation in lowland pedunculate oak (Quercus robur L.) forests in central Croatia. Using a FARO Orbis PLS system, three scanning schemes were tested on sample plots with different densities: (i) a walking scheme with a planned trajectory, (ii) a static flash-scanning scheme with multiple fixed positions, and (iii) a combined scheme integrating walking and static scans. For each plot, multi-scan terrestrial laser scanning (TLS) was first conducted and used as a reference for diameter at breast height (DBH) and tree height (H). All PLS point clouds were processed using a consistent workflow, which included filtering, normalisation, individual-tree segmentation, and attribute estimation, and then compared against TLS-derived values. Preliminary results indicate that, although the static scheme yields denser point clouds and higher measurement precision, it does not consistently improve DBH and H accuracy compared to the walking scheme and can even increase errors in denser plots. The combined scheme performs similarly to the walking scheme. These findings indicate that well-designed walking-based PLS schees can provide accurate, operationally efficient estimates of individual-tree attributes in structurally complex deciduous stands, supporting wider adoption of PLS in forest inventory practice.

Despite its popularity, IMU preintegration is often perceived as requiring a dedicated implementation that is separate from conventional IMU propagation. In practice, however, many codebases already contain a reliable propagation module, often tied to a particular state or error-state definition. This raises two practical questions. First, does adopting IMU preintegration require reimplementing the IMU model from scratch? Second, how can one validate that a preintegration implementation, especially its bias Jacobians and covariance, is correct?

This note shows that IMU preintegration and IMU propagation can be viewed as two equivalent realizations of the same underlying computation. We first describe both in a way that is not tied to a particular perturbation convention. We then show that the preintegrated measurement, its Jacobian with respect to the initial IMU bias, and its covariance can all be obtained by wrapping an existing IMU propagation routine. Conversely, a preintegration module can be used to recover state-transition matrices and propagated covariances. This view also clarifies how to adapt preintegration across different error-state definitions without re-deriving bias Jacobians and residual covariances from scratch.

We validate the analysis by converting an RK4-based IMU propagation implementation to and from the GTSAM preintegration modules. In experiments with random IMU sequences, the recovered Jacobians, covariances, and transition matrices closely match those produced by GTSAM's tangent and manifold preintegration. These results suggest that a robust propagation implementation can serve both as a simple path to preintegration and as a practical reference for validating preintegration code.

Accurate information on tree structure is fundamental for forest management, biomass estimation, and carbon accounting. Personal Laser Scanning (PLS) has recently emerged as an efficient method for capturing detailed three-dimensional representations of trees under operational field conditions. At the same time, Quantitative Structure Models (QSMs) have become an important tool for deriving structural attributes such as diameter at breast height (DBH), tree height, and total tree volume directly from point cloud data. Despite increasing use of these approaches, systematic comparisons of different QSM reconstruction methods applied to PLS data remain limited.

This study evaluates two QSM workflows, PyTLidar and AdQSM, using PLS point clouds collected for pedunculate oak and European beech trees in leaf-off conditions. Data were acquired with the FARO Orbis system using both continuous mobile scanning and stationary flash scans, enabling the creation of mobile-only, flash-only, and combined point cloud variants. After preprocessing and single-tree extraction, each tree cloud was reconstructed separately with both QSM approaches. Key structural attributes were derived from each reconstruction to assess how the methods differ in estimating tree volume.

The comparison employs statistical measures that quantify natural variability among trees relative to variability introduced by each workflow. This allows the study to identify situations in which the two QSM methods produce consistent results and where their outputs diverge. The findings will support improved understanding of QSM behaviour when applied to PLS data and contribute to ongoing efforts to strengthen digital tree modelling for forest monitoring and ecological applications.

LIDAR point clouds combined with canopy-light extinction software can provide 2D simulations of shortwave radiation to identify crucial microclimates that control the overall water balance in savanna ecosystems. However, the point size necessary to accurately depict the wide range of tree species and forms that temperate savannas contain is largely unknown. To determine the optimal point size, hemispherical canopy imagery and field measured insolation will be compared to synthetical hemispherical imagery derived from LIDAR point clouds at different point sizes. The optimal point size will be validated against FLApy predictions and Hobo MX2022 measured illumination across 20 sample plots. The index of agreement between observed and predicted values will quantify systemic biases. Accurate point size is needed to assess tree removal scenarios and equip ecologists with the tools needed to understand the long-term implications for tree removal choices and how to best restore the tree canopy for long-term savanna resilience.

As Earth’s climate changes, it is impacting disasters and extreme weather events across the planet. Record-breaking heat waves, drenching rainfalls, extreme wildfires, and widespread flooding during hurricanes are all becoming more frequent and more intense. Rapid and efficient response to disaster events is essential for urban climate resilience and sustainability. A key challenge in disaster response is to correctly and quickly identify diaster locations for timely decision-making and resources allocation. In this paper, we propose a Probabilistic Cross-view Geolocalization approach, called ProbGLC, exploring new pathways towards generative location awareness for rapid disaster response. Herein, we combine the probabilistic and deterministic geolocalization models into a unified framework to simultaneously ensure model explainability and state-of-the-art geolocalization performance. Designed for rapid diaster response, the ProbGLC is able to address cross-view geolocalization across multiple diaster events as well as to offer unique features of model explainability and uncertainty quantification.