Conference Agenda

This study statistically analyzes fire events in the Atlantic Forest, seeking to understand their particularities in relation to the national scenario. The biome, historically pressured by deforestation, fragmentation, and anthropogenic activities, also suffers from agricultural, livestock, and accidental fires, which increase its vulnerability. The research used data from Censipam's Fire Panel, obtained by MODIS and VIIRS orbital sensors, considering records from 2020 onwards and specific sections for the Atlantic Forest. Variables such as area, severity, persistence, speed of expansion, number of outbreaks, Fire Radiative Power (FRP), and detections were analyzed. The results indicate that, compared to the national pattern, fires in the Atlantic Forest are less intense and shorter in duration, a phenomenon associated with higher humidity, landscape fragmentation, and management conditions. It is concluded that the dynamics of fire in the biome differ significantly from the national average, reinforcing the importance of regional monitoring and firefighting strategies aimed at preserving its ecological integrity.

The MODIS burned area product is widely used to extract ignition locations and delineate individual fires for wildfire probabilistic loss modeling. However, limited studies have systematically evaluated the accuracy of these derived fire events through detailed spatial and temporal comparisons with reference datasets. This study addresses this gap by developing a robust framework to assess the accuracy of MODIS-derived individual fires across the United States. In this study, the MODIS Collection 6 MCD64 burned area product was used to extract ignition locations and individual fire events using the Fire Events Delineation (FIRED) algorithm. A comprehensive evaluation framework was then implemented to assess the delineated fire events against the Monitoring Trends in Burn Severity (MTBS) reference dataset, accounting for both spatial overlap and temporal consistency. The results show that the proposed approach achieved an average Intersection over Union (IoU) score of 0.54, an F-score of 0.701, an overall accuracy of 0.77, a precision of 0.90, and a recall of 0.57. These metrics represent averages across the period 2001–2020. Collectively, the results highlight the strengths and limitations of the event detection system and provide a quantitative assessment of its performance. This comprehensive evaluation offers valuable insights into the reliability of MODIS-derived individual fire events and improves understanding of their suitability for wildfire probabilistic loss modeling and related applications.

Wildfires cause economic, social, and environmental consequences, as they affect ecosystems, public safety, biodiversity and natural resources. They pose challenges to various world regions, particularly Mediterranean areas such as Spain. Numerous fire prediction and detection systems were introduced to detect and predict fires as well as prevent their risks and damage. Statistical methods and classical machine learning models were often employed to estimate and predict fire risk, showing their efficiency in generating fire risk maps. However, they fail to accurately capture complex temporal and spatial characteristics related to fire ignition. To address this challenge, a novel Convolutional Neural Network (CNN) model, namely CFMap, was introduced for predicting and generating detailed wildfire risk maps covering Spain regions. Comprehensive analyses were performed using data between 2008 and 2024, including fire history, geographical location information, land usage features, human activity indices, topography data, meteorological features, and vegetation indices from Spain regions, collected from the IberFire dataset. CFMap showed a superior performance with an accuracy of 0.8028 ± 0.0440, an AUC (Area Under the Curve) of 0.9354 ± 0.0088, and an F1-score of 0.7787 ± 0.0623, outperforming classical machine learning methods (XGBoost, LightGBM, and RandomForest) and deep learning models including ResNet and a simple CNN. These results demonstrate its reliability in predicting fire events and generating monthly fire risk maps for different Spain regions. Consequently, it helps to identify high fire risk zones, improve fire management strategies, and efficiently deploy firefighting resources, thereby reducing the potential risk and impact of fires.

In the immediate aftermath of a hurricane, the rapid, accurate assessment of building damage is paramount for effective emergency response and the allocation of resources. Traditional methods of damage assessment, which rely on ground-based surveys, are often slow, hazardous, and subjective. While the advent of remote sensing (RS), through Unmanned Aerial Vehicles (UAVs) and the application of Convolutional Neural Networks (CNNs), has significantly advanced the automation of this process, these models operate on a pixel-level or object-level basis, failing to capture the inherent spatial relationships and contextual information within a disaster zone. Damage patterns are not spatially random; they exhibit strong spatial autocorrelation, a principle encapsulated by Tobler's First Law of Geography. This paper introduces a novel approach that leverages Graph Attention Networks (GATs) to explicitly model spatial dependencies when evaluating building damage. By representing damaged buildings and their surroundings as nodes and edges in a graph, our model can learn and weigh the influence of neighboring structures and the local environment when assessing their damage level. This spatially-aware methodology moves beyond simple image classification to a more holistic scene understanding. We evaluate the method on DoriaNET, a geo-referenced UAV dataset collected after Hurricane Dorian (2019) that provides masked building patches, GPS centroids, structural metadata, and ordinal FEMA/HAZUS-style damage labels. By incorporating spatial context via a graph-based framework, our GAT model achieves superior performance in building damage classification compared to state-of-the-art CNN-based approaches, producing more coherent and accurate damage maps better suited to real-world disaster management scenarios.

This work presents an innovative image-based method for measuring wind speed and direction in urban environment using video

footage. Wind dynamics are traditionally investigated at multiple spatial scales, including pollutant dispersion at the canopy level

(Allwine, 2000), architectural design and outdoor comfort at the building scale (Allard, 2012; Holst, 2011) and the convection heat

transfer coefficient ℎ [Wm-²K-1] used to define the boundary conditions of numerical simulations (Oke, 2017). In 1997, Gary Settles

showed that image measurement could provide non-invasive and high-resolution measurements of fluid motion. This paper presents a

method for extracting anemometric data from images at the urban scale. We process freely accessible videos from the internet in which

air masses are identified at the canopy level. Motion extraction technique is used to isolate elements of the video that are in motion.

This information is fed into an optical flow algorithm that estimates an apparent velocity in [pixels/frame]. To convert the data to

[km/h], the view’s perspective is considered to ensure the conversion is accurate across the entire image. Distance mapping is

performed by projecting the image onto a 3D model of the scene, and the camera's recording parameters are estimated by simulating

the illumination of the scene. The anemometric data obtained are evaluated in relation to meteorological data recorded at a nearby

weather station. Innovative and simple to implement, this approach provides estimates of wind speeds and directions that are both

reliable and directly usable for architectural design and climate studies.

Rapid urbanization and the loss of vegetative cover in Indian cities have raised serious concerns about environmental sustainability and public health. This study focuses on analyzing and forecasting Urban Heat Island (UHI) patterns in Jaipur, India, by examining both Surface UHI (SUHI) and Atmospheric UHI (AUHI). Using Google Earth Engine, the research integrates diverse spatio-temporal datasets—including Landsat-derived indices (such as LULC, NDVI, NDWI, NDBI, NDMI, albedo, and emissivity), geospatial features (building density, sky view factor, and population density), and meteorological data (air temperature, humidity, wind speed, and solar radiation) from 2000 to 2024—to train a Random Forest Regression model. The model demonstrated strong performance (R² = 0.806; RMSE = 0.059), surpassing linear and generalized additive models by effectively capturing complex, non-linear relationships. It also helped identify high-risk areas like Transport Nagar and Budhsinghpura. Projections for 2030 and 2035 indicate increasing heat stress, particularly in Jaipur’s expanding urban periphery. This GIS-integrated machine learning framework presents a replicable approach for UHI prediction in other fast-growing Indian cities.

Persistent Scatterer Interferometry (PSI) is a consolidated active remote sensing technique to measure and monitor land deformation. The technique has experienced an intense development in the last 25 years. PSI techniques use large stacks of SAR images that cover a given observation period. The outcome of any PSI processing is a cloud of geocoded measurement points that contain the estimated deformation time series over the observation period. If the analysed area is wide, the corresponding point cloud can be huge. In these cases, the potential users often experience problem in analysing such huge point clouds, and this can limit the PSI exploitation. In this paper we present a set of products that address specific application needs or that offer higher-level products with respect to the standard PSI products, which can facilitate the interpretation and exploitation of the PSI results.

Interferometric SAR (InSAR) is a well-established remote sensing technique to measure and monitor land deformation. We focus in this paper on Persistent Scatterer Interferometry (PSI) techniques based on large stacks of SAR images. Several PSI approached have been proposed in the last three decades, see for a review Crosetto et al. (2016). In this paper, we describe an approach the is based on the direct integration of the interferometric phases (back-to-back approach).

The Persistent Scatterer Interferometry (PSI) is a multi-temporal InSAR approach that allows to monitor displacement time series of the Earth's surface. The method identifies and analyzes Persistent Scatterers (PSs) which are phase stable scattering points which dominate the backscatter of their resolution cell. Standard PSI techniques only identify and analyze PSs which are coherent throughout the whole considered SAR time series. However, PSs can fade, appear or be occluded during the time series, forming so called Temporary PSs (TPSs), which should be integrated into the PSI to establish optimal measurement point networks. Previous research has proposed methods to integrate such TPSs into the PSI, however these were exclusively evaluated for construction-related TPSs. In this work, we evaluate the performance of a TPS integration method in handling recurringly occured PSs, and compare the performance of individual components of the algorithm against alternative methods. We evaluate the methods using simulated TPSs with temporal and spatial baseline settings taken from real Sentinel-1 data stacks. Furthermore, we present and discuss the application of the methods to a Sentinel-1 data stack acquired over the Oetztal Alps, which are seasonally covered by snow. We show that the integration of ROPSs significantly increases the measurement pixel density at many locations across the study area, compared to results from the European Ground Motion Service. Even if most of the ROPS did not have identified coherent segments in each covered summer with the current analysis algorithm, their integration leads to a significant information gain compared to standard PSI approaches.

Deformation monitoring in open‑pit mining and tailings‑dam operations is critical for operational safety, yet conventional in situ geodetic techniques provide only sparse, point‑based measurements. InSAR offers many displacement measurements, but its operational uptake is limited by complex workflows and the difficulty of interpreting analysis‑ready products such as EGMS. In cold regions, seasonal data gaps can introduce phase‑unwrapping artefacts that appear as winter‑only displacement offsets of approximately half the Sentinel‑1 wavelength. We propose a semi‑automated workflow to post‑process EGMS displacement time series, including pre‑filtering to identify and remove points affected by phase‑unwrapping errors and subsequent time‑series clustering in either a reduced‑dimensional representation or the full feature space. Cluster selection is automated using heuristic criteria and a custom metric based on temporal homogeneity and consistency. The findings show that the semi‑automatically detected clusters are plausible with regards to a visual interpretation of the EGMS data. The workflow supports improved interpretation of EGMS time series and avoids hard‑coded thresholds or reliance on velocity‑based estimates.

The Inglewood Oil Field, located in the Los Angeles Basin, California, is a major urban hydrocarbon production site with a documented history of ground deformation linked to oil extraction. To assess ongoing deformation and validate previous monitoring results, Interferometric Synthetic Aperture Radar (InSAR) analysis was conducted using Sentinel-1 SAR data processed through the Alaska Satellite Facility’s HyP3 platform and the Miami InSAR Time-series software in Python (MintPy). The study analysed ascending and descending datasets acquired between 2020 and 2025 to derive high-resolution deformation time series and velocity maps. Results reveal a localized deformation pattern characterized by low-magnitude vertical motion, with maximum uplift and subsidence rates of approximately +0.8 cm/yr and –1.6 cm/yr, respectively. Minor horizontal displacements (±1.0 cm/yr) suggest limited lateral strain associated with reservoir compaction and stress redistribution. Compared with previous assessments conducted up to 2024, the current findings indicate a marked reduction in deformation magnitude, implying progressive stabilization of reservoir pressure and improved subsurface management. These results demonstrate the effectiveness of InSAR for long-term monitoring of urban oilfields, providing critical insights into the behaviour and contributing to risk mitigation in densely populated environments.

Ground deformation caused by excessive groundwater extraction has become a major environmental concern in agricultural regions worldwide. Interferometric Synthetic Aperture Radar (InSAR) enables large-scale monitoring of ground deformation. However, its performance often decreases in low-coherence areas affected by vegetation growth and irrigation. In this study, we conducted a comparative evaluation of three multi-temporal SBAS-InSAR processing frameworks, MintPy, LiCSBAS, and SARvey, to assess their consistency in monitoring ground deformation across Golestan Province, Iran, using Sentinel-1 data acquired between 2014 and 2024. The analysis included deformation velocity fields, cross-sectional profiles, and time-series displacements, which were compared with temperature and precipitation variations. All three frameworks identified a pronounced deformation zone in the Gorgan Plain, with maximum line-of-sight deformation rates up to 13 cm/year. Quantitative comparisons showed strong correlations among the frameworks (r = 0.80 to 0.89), confirming their mutual reliability even under low coherence conditions. The time-series analysis revealed clear seasonal deformation patterns, with summer subsidence and winter uplift closely related to hydroclimatic fluctuations. Overall, this study demonstrates that multi-temporal SBAS-InSAR approaches can provide consistent and physically meaningful deformation estimates in challenging agricultural environments, offering valuable insights for subsidence monitoring and water resource management.

Urban rooftop solar planning relies on solar radiation inputs, yet estimates vary across models and measurement methods. This study compares radiation estimates from ArcGIS Solar Analyst, NASA solar radiation values, in-situ observations from research-grade and personal weather stations, and SDT-based Solar Radiation Modelling. We derive hourly global horizontal irradiance (GHI) values from these solar radiation data centres, model building-level estimates, harmonise all sources through temporal alignment, and then evaluate the values. The comparison reveals the hourly modelling of solar radiation models and common solar radiation centres, highlighting where an urban-adjusted local sensor provides lower solar radiation values because of the limited representation of the built and urban environment. Results show that utilising gridded or terrain-based models over urban-adjusted solar radiation values overrepresent due to the uncaptured localised shadings, roof placement effects, and increasing systemic errors for downstream rooftop PV terrain-based assessments. The cross-validated workflow of sensor-based city-scale solar radiation modelling is reproducible and scalable, offering local governments a more nuanced understanding of their solar capacity, and paves the way for carbon emission budget management.

Deep learning, in particular, has driven hundreds of new studies in remote sensing each year. However, ensuring the reliability of these models requires robust uncertainty quantification, an aspect that remains insufficiently explored. Current remote sensing deep learning models typically yield single, deterministic predictions, such as a class label for each pixel or a single biomass value for a given location or region. While commonly used metrics such as RMSE or classification accuracy summarize overall model performance, they fail to convey the reliability of individual predictions, leaving users without guidance on how much confidence to place in each output. Uncertainty estimation addresses this critical gap by quantifying the variability or confidence associated with model predictions. This enables practitioners to interpret not only what the model predicts but also how confident it is in those predictions, providing a more nuanced understanding that is essential for informed decision-making.

We address aleatoric uncertainty using Sentinel-1 and Sentinel-2 time series, proposing two approaches: (i) Gaussian UC, which predicts mean and standard deviation, and (ii) Quantile UC, which estimates the 10th, 50th, and 90th quantiles to capture asymmetric errors. We evaluate these approaches on three representative EO tasks: building height, canopy height, and aboveground biomass estimation. Our results (ID and OOD) show that both models achieve accuracy comparable to deterministic benchmarks while providing well-calibrated, interpretable, and operationally useful confidence estimates. Notably, both models outperform existing global canopy height products on evaluated sites, including the recent 1 m canopy height maps produced by vision transformers.

Recent advances in remote sensing have shifted the focus from the analysis of individual image scenes to the understanding of complex earth systems. This means that the analysis of dynamic evolutions replaces previous static examinations for fixed time points. Furthermore, interdisciplinary research and the integration of heterogeneous data sources are characterizing this transformation process. Digital Elevation Models (DEMs) are predestined for supporting this process by supplementing orthophotos and map data. Promising applications include city planning, landslide analysis, and flood risk assessment where spatio-temporal change detection is a central concept to be applied. Concerning map data, the OpenStreetMap project, based on the idea of Volunteered Geographic Information, has revolutionized the effective production and update of digital maps. However, OSM data does not include elevation information and often contains incorrect geometric information. In this paper, we introduce a self-training framework for validating OSM building footprints with the aid of high-resolution DEMs. The framework supports building segmentation with a self-supervised approach to improve the representation of OSM building footprints. The availability of Digital Elevation Models is used to check the quality of OSM data. The applicability of the approach is demonstrated by a case study conducted in Karlsruhe, Germany. The promising results are described in detail. With our approach, change detection of OSM data can also be carried out using different temporal versions of DEM and OSM data.

Accurate and reliable road extraction from LiDAR data remains a major challenge when spectral cues are limited or spatial heterogeneity increases model uncertainty. This study introduces a comparative, entropy-driven framework for evaluating the performance and reliability of road asset detection using three supervised machine learning algorithms—XGBoost, Random Forest (RF), and Support Vector Machine (SVM). Using a high-density aerial point cloud, a reproducible computational pipeline was implemented, to help practitioners in real-world scenarios for selecting the most robust and reliable machine learning methods for large-scale road assets mapping. Beyond traditional accuracy metrics (Overall Accuracy, F1-score, and Kappa coefficient), uncertainty-based evaluation of the outputs has been conducted using KPIs of entropy and sensitivity to training sets to quantify model reliability and spatial instability. Results reveal that the inclusion of RGB significantly reduces entropy across all models. XGBoost achieved the lowest mean entropy (0.084–0.143) and the most consistent probabilistic behaviour, reflecting confident and well-calibrated model. SVM, while statistically the most accurate (OA and Kappa > 0.97), exhibited higher local entropy (≈ 0.23–0.26), implying precise yet less certain classification. RF demonstrated the highest entropy (≈ 0.65–0.70) and the greatest variability, underscoring its sensitivity to feature noise. Under the WOR configuration, mean entropy rose markedly—most for RF_WOR (≈ 0.93) and moderately for SVM_WOR (≈ 0.39)—while XGBoost retained low uncertainty.

Spatial entropy maps further highlighted that uncertainty concentrates along road edges with RGB data but expands diffusely under WOR conditions, emphasizing the critical role of spectral–spatial synergy in constraining ambiguity. entropy-based evaluation provided insights beyond conventional accuracy metrics, revealing paradoxes between correctness and confidence.

Efficient processing of large-scale 3D point clouds acquired from Terrestrial or Airborne Laser Scanning (TLS/ALS), presents a significant computational challenge. While transformer-based architectures excel at modeling the global context crucial for interpreting these complex scenes, their quadratic computational complexity makes them infeasible for direct application on massive point sets. To address this scalability bottleneck, we propose the Spatio-Sequential Point Transformer (S2PT), a novel hierarchical architecture for efficient and effective large-scale point cloud processing. Our approach begins by serializing the point cloud into an ordered sequence, which enables the use of attention with linear complexity. This not only circumvents the quadratic bottleneck of standard transformers but also establishes a global receptive field at every layer. To compensate for potential information loss during serialization, we further introduce a novel Spatio-sequential Positional Encoding (S2PE) that synergistically combines 3D local geometric features with 1D sequential order information, enhancing the model’s spatial awareness. Experiments on multiple benchmarks demonstrate that S2PT achieves performance comparable to state-of-the-art methods while being significantly more efficient during training and inference, offering a promising path towards scalable representation learning for large-scale 3D scenes.

Accurate semantic segmentation of 3D point clouds is a fundamental task in photogrammetry, robotics, and large-scale scene understanding. Despite recent advances in point-based architectures such as PointNeXt, segmentation performance remains limited near semantic boundaries, where local neighborhoods often contain points from multiple classes, leading to feature ambiguity and oversmoothing.

In this paper, we propose a lightweight boundary-aware learning framework that explicitly models boundary regions during training. Boundary supervision is automatically derived from local semantic label disagreement, eliminating the need for additional annotations. An auxiliary boundary prediction head is introduced to learn boundary-sensitive features, which are subsequently integrated into the segmentation process through a late-stage feature fusion mechanism. In addition, a boundary-aware loss formulation emphasizes boundary regions during optimization, encouraging improved feature discrimination at class transitions.

Experimental results on the S3DIS dataset using the standard 6-fold cross-validation protocol demonstrate consistent improvements over the PointNeXt baseline. The proposed method achieves gains of 3.22% in mean Intersection over Union (mIoU) and 2.85% in mean class accuracy (mACC), with notably improved segmentation quality at object boundaries. Importantly, these improvements are obtained without modifying the backbone architecture or increasing inference complexity.

The results indicate that incorporating boundary-aware supervision provides an effective and efficient strategy for improving segmentation performance in challenging regions.

China is actively pursuing synergistic governance to address air pollution and carbon mitigation issues. This study, focusing on concentration as a key feature, assesses the synergy performance in the Yangtze River Delta Urban Agglomeration (YRDUA), revealing fluctuating trends with only seven cities showing improvement. To further understand the influences from local drivers, we employed an explainable spatial machine learning approach, integrating Geographical Weighted Regression (GWR), Random Forest (RF), and Shapley Additive Explanation (SHAP) to capture nonlinear, threshold, and interaction effects among explanatory variables. The analysis identifies longitude, SO2 emissions from industrial sources, wind speed, latitude, and the proportion of GDP from tertiary sector as the top five influencing factors, emphasizing the importance of geographical position, local air pollution emission, and meteorological condition. Most drivers exhibit nonlinear impacts and interactions with clear thresholds. Such as, wind speeds, exceeding 9.3 m/s negatively impact synergy. Furthermore, spatial heterogeneity of drivers' influence is evident across cities and regions. Specifically, cities along the eastern coast benefit from geographical advantages that enhance synergy in air quality improvement and carbon mitigation. Meteorological conditions, especially wind speed, significantly influence synergy, with notable differences between northern and southern coastal cities. These findings underscore the need for locally tailored governance strategies that leverage each city's unique geographical and socioeconomic attributes to enhance synergistic governance effectiveness. This research contributes to understanding the complex interplay of local drivers influencing synergistic governance in the YRDUA, providing valuable insights for policymakers aiming to improve air quality and promote sustainable development in rapidly urbanizing regions.