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.