Fine-grained segmentation of communication-tower components in UAV imagery is essential for automated inspection, yet task-specific models are hard to develop due to limited instance-level annotations. Zero-shot segmentation models offer a promising alternative, but in cluttered scenes, visually similar background structures interfere with component localization, causing missed instances and false positives. We propose a model-agnostic saliency-depth foreground-conditioning strategy combining appearance-based saliency with monocular relative depth to construct a coarse tower prior and suppress irrelevant content. We integrate this module with Grounded-SAM and SAM 3, yielding SD-Grounded-SAM and SD-SAM 3. SD-Grounded-SAM further applies geometric and depth-aware box refinement before mask generation, while SD-SAM 3 relies on SAM 3's internal setup. On TOW-300, a dataset of 340 communication-tower UAV images, our strategy improves both baselines: SD-SAM 3 achieves the strongest instance-segmentation performance, while SD-Grounded-SAM produces fewer false positives. Ablations confirm complementary gains from saliency, depth, and box refinement, improving robustness in cluttered scenes.
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.
Fuad Hasan, C. Yeum, Ali Lesani et al.· ISPRS Annals of the Photogra...· 1 citation