Conference Agenda

Recreational fishing supports economies, wellbeing, and connection to the marine environment but can pressure fish stocks. Traditional monitoring in New Zealand is costly, sporadic, and self-reported. This study evaluates integrating mobile phone data (MPD) and satellite-based object detection (YOLO on Sentinel-2 and sub-meter imagery) to improve monitoring. MPD provides temporal coverage but is biased, while satellite imagery offers spatial validation but provides only snapshots. Combining these datasets mitigates biases and gaps, enabling more accurate, representative estimates of fishing activity. This is the first study to integrate these approaches, demonstrating the potential of hybrid methods for scalable, cost-effective recreational fisheries monitoring.

The aggregation of building footprints is a key task of cartographic generalization, which is an important topic in geoinformation science. It has been approached from various angles, ranging from heuristics and optimization algorithms to machine learning. Given a set of input polygons that represent the building footprints, the task is to generate a set of polygons that provide a coarser representation of the input. The problem has applications in the visualization of settlement areas in small-scale maps, as well as settlement classification and analysis. A popular solution approach is to construct a subdivision of the plane and then build a solution by selecting faces from the subdivision. Often, a triangulation is used for the subdivision. However, this can cause the orientations of the boundary edges in the solution to differ drastically from the input polygons, which leads to a loss of information about the underlying settlement structure. We explore an alternative method that constructs the subdivision by extending the input building edges, thereby automatically preserving their orientations. To make the approach scalable to large instances without substantially decreasing the solution quality, we propose different methods of reducing the complexity of the subdivision. Our experimental evaluation on real-world data shows that our method is able to aggregate towns containing up to approximately 10 000 building footprints while preserving input edge orientations much better than state-of-the-art methods.

Timely updates of lane-level dynamic information are crucial for intelligent vehicle path planning and driving safety. Most existing crowdsourced map update methods lack sufficient analysis of the reliability and uncertainty of perception results, making it difficult to ensure the accuracy of map updates. We propose a novel method for updating lane-level dynamic information in HD maps based on crowdsourced data. First, a hybrid modelling multi-object detection method is used to reliably perceived lane markings and traffic cones. To address the issues of false detection and missed detection in single-vehicle perception, a multi-vehicle probabilistic fusion algorithm is proposed, which explicitly models perceptual uncertainty to effectively mitigate the impact of missed and false detections, enabling accurate, robust, and real-time detection of dynamic information such as temporary lane closures.To validate the effectiveness and accuracy of the proposed method, we conducted experiments in the Intelligent and Connected Vehicle Demonstration Zone in Wuhan.Experiments comparing single-vehicle and multi-vehicle fusion results demonstrate the effectiveness of the proposed method in enhancing detection performance.

This study uses machine learning and geospatial science to investigate how climate change may affect the foraging and habitat suitability of little penguins Eudyptula minor in Australia and New Zealand. An innovative modeling approach was followed here to identify favorable climatic conditions for the species across both regions. The model trained on Australian occurrence data was projected to New Zealand, and vice versa, to assess cross-regional habitat suitability and potential range shifts under changing climate conditions. This is to further evaluate adaptive potential and determine whether transoceanic relocation would be feasible in the event of local extinction. The study evaluated habitat suitability using the ML model and climate variables from the WorldClim dataset. The findings showed that the healthy habitat of little penguins is significantly shaped by temperature-related bioclimatic variables, especially temperature annual range. According to the models, the habitat suitability of little penguins varies between the two nations, with Australia offering the little penguins of New Zealand less hospitable conditions. But the New Zealand is predicted to offer relatively better habitat to Australia-based little penguins. This study offers vital information for conservation strategies by highlighting the possible changes in penguin populations brought on by climate change. A promising tool for comprehending how the climate affects marine ecosystems is provided by this study.

This paper presents an advanced computational framework developed to support decision-making for the placement of the underground Einstein Telescope, a third-generation gravitational-wave observatory. The system aims to automate the search for an optimal location through a multi-criteria analysis approach. Because the ET is extremely sensitive to environmental noise sources—including seismic, thermal, and anthropogenic vibrations—its design prioritises underground construction. This strategy, also adopted for the Japanese KAGRA detector and in contrast to surface-based observatories such as LIGO and Virgo, minimises interference from surface activities while ensuring subsurface stability.

The proposed methodology integrates Geographic Information System (GIS) data, incorporating a Digital Surface Model (DSM) to spatially represent relevant factors. The dominant site-selection criteria were identified and weighted according to their scientific and strategic importance in collaboration with the ET scientific community. An interactive parametric model was developed to interface directly with the GIS data, enabling evaluation of key factors and providing real-time analytical feedback on placement scenarios. Using an evolutionary algorithm combined with a composite fitness function, the system balances competing objectives and delivers optimised solutions, offering a robust decision-support tool for the early planning stages of the Einstein Telescope project.

Although the Sardinia site is currently considered a preliminary case study, the methodology is generalisable and applicable to other candidate sites to host ET

In fractured rock slopes, the geometric configuration and spatial arrangement of unstable rock blocks are fundamentally governed by the intersection of multiple joint sets. The mechanical weakening along these joints markedly reduces the integral strength of the rock mass and establishes potential kinematic release boundaries. This study establishes an in-situ hazardous-rock detection and characterization framework utilizing high resolution three-dimensional point cloud acquired under realistic topographic conditions. This method first examines the spatial interaction between joints and slope morphology, and incorporates explicit kinematic criteria to automatically identify structural combinations capable of different failures. Consequently, the spatial positions and distribution patterns of potentially unstable blocks are delineated within the point cloud. Subsequently, point cloud differencing is employed to achieve volumetric extraction and statistical classification of block sizes, enabling quantitative characterization of block volume and elevation across the source areas. Representative blocks are then selected as initial release elements, with their actual geometrical and volumetric attributes incorporated into rockfall simulations. This allows for the computation of key kinematic parameters including rockfall frequency, bounce height, velocity, and kinetic energy. Overall, the presented approach delivers a scalable pathway for rapid detection, quantitative assessment, and hazard evaluation of structurally controlled rockfalls in complex mountainous terrain. The results provide technical support and decision insights for the safe operation and disaster-resilient planning of transportation infrastructure.

With the rapid advancement of satellite, aerial, and UAV platforms, the daily volume of remote sensing data collected over megacities has grown exponentially. However, only a limited portion of this data can be transformed into usable products in time. Current production workflows remain lengthy and poorly automated, which fails to meet the increasing demand for high-precision and high-timeliness remote sensing products in city management, environmental monitoring, and emergency response.

To address this gap, this study proposes the construction of an standardized, efficient and reusable foundational database system consisting of three key components: image control point database, interpretation sample database, and spectral information database.

The image control point database establishes a unified geometric reference for multi-source data; The interpretation sample database provides large-scale, high-quality labeled data for deep learning-based image analysis; and the spectral database offers standardized spectral features for accurate classification and parameter inversion.

Together, the three databases form a collaborative mechanism that links geometric accuracy, semantic understanding, and spectral consistency, thereby building a complete chain from analysis-ready data (ARD) production to rapid information extraction. Using Shanghai as a case study, this paper presents the design, construction, and collaborate applications of the three databases, demonstrating their effectiveness in supporting refined and sustainable megacity governance.

Disaster Risk Management benefits from innovative techniques including AI and Multi Sensor Fusion. The Fireguard Approach uses such technologies to improve the Wildfire Management works in Saxony, Eastern Germany by supporting standing efforts in Early Warning, Disaster Response and Monitoring. Unmanned Aerial Systems (UAS) play a vital role in providing real-time information via a 5G network to a central information management system that delivers geospatial information to response teams. This study highlights the potential of combining UAS, AI, geospatial solutions and existing data for real-time wildfire monitoring and risk assessment systems. The preliminary study successfully shows the potential of the provided solution to enhance Wildfire early detection, response and monitoring to address immediate and long-term needs of response teams.

This paper presents a multi-agent geospatial application that enables users to interact with spatial data through natural language, called MapEcho Copilot. The system integrates large language model (LLM) reasoning, semantic embedding search, and spatial analytics within a unified architecture. A vector embedding database is constructed to index diverse open-source geospatial datasets. Upon receiving a user query such as “show all tennis courts in downtown of Toronto” or “find habitats of grizzly bears in Canada” the system performs semantic retrieval to identify relevant datasets, followed by geospatial filtering and reasoning through specialized agents via an interactive and friendly interface. The multi-agent framework coordinates between semantic understanding, data retrieval, and spatial computation layers to deliver map-based responses in real time. The Results demonstrate the system’s ability to process both semantic and geospatial queries with high accuracy and interpretability, providing an intuitive bridge between natural language and spatial intelligence.

Point clouds have proven over the years to be a suitable spatial representation of scenes and objects at varying scales and levels of complexity, making them widely used across several scientific domains and applications. Advancements in sensor technology, computer vision, and data science have produced high‑resolution point clouds and advanced analytical approaches, leading to broader adoption for spatial information extraction to support decision making. However, traditional point cloud management systems for organizing and distributing data throughout the point cloud lifecycle often create significant duplication at each stage. This causes data fragmentation as multiple copies and versions are scattered across different processing steps, workgroups, and storage locations, further limiting cross‑domain applications.

In this paper, we propose a unified point cloud data management (PCDM) approach that supports the principles of findability, accessibility, interoperability, and reusability (FAIR) across domains at scale. The proposed approach aims to support diverse point cloud retrieval for cross-domain analysis by leveraging a single, reusable PCDM system built on a shared data model. Our approach improves on existing frameworks and provides a foundation for point cloud data management and data spaces.

This contribution presents a rigorous mathematical and computational examination of confidence ellipses applied to bivariate spatial distributions, with a specific focus on their implementation in the open-source LFTools plugin for QGIS. Confidence ellipses are widely used in geography, environmental sciences, public health, criminology, and spatial statistics to summarize central tendency, dispersion, and directional trends of point-based datasets. Although conceptually well established, their practical reliability depends on correct numerical implementation and statistical consistency—an aspect rarely evaluated in detail.

The study first revisits the formal mathematical foundations of confidence ellipses, including covariance-matrix geometry, eigen-decomposition, and Chi-Square-based scaling for different confidence levels. It then analyses the computational workflow adopted in LFTools and validates its correctness using 100,000 simulated Gaussian random points, demonstrating near-perfect adherence (<0.05% deviation) to theoretical confidence intervals.

To assess performance on real-world data, the method is applied to the Groundwater Well Database for Brazil (GWDBrazil), comprising more than 350,000 groundwater wells. Confidence ellipses at the national and regional levels reveal strong anisotropy, clustered patterns, and non-Gaussian spatial structures, confirming both the robustness of the tool and the complexity inherent to real geospatial phenomena.

Results indicate that the LFTools implementation is mathematically sound, statistically reliable, and suitable for scientific applications. The study highlights the relevance of reproducible open-source tools and outlines future directions involving spatial–temporal extensions, non-parametric approaches, and multi-scale territorial analysis, applicable in Brazil and worldwide.

Through theoretical analysis and empirical research, this study thoroughly examines the theoretical foundations and practical applications of zero-defect sampling inspection schemes, revealing significant differences between inspecting large lots as a whole versus splitting them into sub-lots in terms of consumer's risk control. The findings indicate that although the zero-defect sampling scheme (Ac=0) adopted in the GB/T 24356-2023 standard shifts quality control from "post-production spot checks" toward "in-process prevention", it exhibits notable deficiencies in controlling consumer's risk, resulting in an unacceptably high level of risk for consumers. Empirical analysis demonstrates that, for large lots with relatively poor quality, e.g., when the product's defect rate is 10%, the inspection plan (100, 10, 0) still carries a 33.3% probability of erroneously accepting the lot, which significantly exceeds the risk level typically acceptable to consumers and thus imposes excessive quality risk on them. Furthermore, the study reveals that inspecting small lots or subdividing large lots benefits producers, highlighting an imbalance in the current standard's risk allocation mechanism. These insights provide more reliable theoretical support and practical guidance for quality management of surveying and mapping products.