Abstract. 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.
Hasan et al. (Wed,) studied this question.