Keep Your Friends Close, and the Right Neighbours Closer: Disaster-Conditioned Kernel-Regularized Graph Attention for Building Damage Classification

• URL: http://arxiv.org/abs/2608.20548v1
• Date: Thu, 20 Aug 2026 20:20:10 GMT
• Title: Keep Your Friends Close, and the Right Neighbours Closer: Disaster-Conditioned Kernel-Regularized Graph Attention for Building Damage Classification
• Abstract summary: Using spatial context for damage classification remains surprisingly underexplored.<n>Floods, hurricanes, and wildfires can exhibit very different clustering behaviour.<n>We study this tension on xBD in a controlled post-localization, classification-only setup.
• Score: 0.9668407688201359
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Disaster damage is spatial: buildings rarely fail in isolation. Yet using spatial context for damage classification remains surprisingly underexplored, and many pipelines still rely primarily on per-building appearance cues even when the dominant uncertainty is spatially structured. Complicating matters, the right neighbourhood is not the same across events. Floods, hurricanes, and wildfires can exhibit very different clustering behaviour, making spatial reasoning valuable but easy to misuse - naive context aggregation can improve visual coherence while oversmoothing boundaries or propagating structured errors. We study this tension on xBD (the dataset used in the xView2 challenge) in a controlled post-localization, classification-only setup: each building is represented by a pre/post combined (PPC) patch cropped from the provided polygons, and spatial context is modelled with GPS-derived building graphs. Our approach keeps local evidence "close" by preserving strong spatial relationships in disaster damage patterns, while bringing only the right neighbours "closer" through a disaster-type-conditioned graph model that injects a learnable multi-scale spatial kernel prior into attention, allowing the effective neighbourhood scale to adapt across disaster types rather than being learned as a single global smoothing rule. To discourage coherence-by-smoothing, we add a residual de-correlation loss that penalizes positive Moran's~I in prediction residuals. We evaluate the method under event and dataset shift with a leave-one-event-out (LOEO) protocol on xBD and cross-dataset transfer from xBD to Ida-BD. The model improves macro-F1 and substantially reduces residual spatial autocorrelation under zero-shot event shift, indicating better use of spatial context rather than naive smoothing and enabling more reliable transfer to unseen events within known disaster types.

Related papers

• Inferring Urban Mobility Interactions from Aggregated Dynamics [45.73534370352232]
  Real-time urban governance depends not only on knowing where people are, but on how they move between places.<n>Here we show that this directional structure need not be observed to be known.<n>We infer future OD flows from area-level counts alone across twelve mobility datasets from cities in the United States and China.
  arXiv  Detail & Related papers  (2026-09-07T11:13:51Z)
• Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking [58.338848435042536]
  RGB-Event tracking improves localization by fusing RGB appearance textures and dense temporal motion cues from event sensors.<n>Real-world scenes suffer diverse structured signal degradations that hinder traditional multi-modal fusion.<n>We present a hierarchical perturbation and retrieval framework tailored for RGB-Event tracking with robustness against partial target missing and modal degradation.
  arXiv  Detail & Related papers  (2026-06-24T23:34:06Z)
• Task-Aware Environment Augmentation for Reliable Navigation via Shielded Conditional Diffusion [4.265905616031847]
  tbpSCoDA learns a conditional distribution over high-performing fiducial layouts from data.<n>We show that tbpSCoDA improves trajectory execution reliability and completion time over strong baselines.
  arXiv  Detail & Related papers  (2026-06-13T06:49:36Z)
• OcclusionFormer: Arranging Z-Order for Layout-Grounded Image Generation [49.49933361280724]
  We introduce OcclusionFormer, a novel framework that explicitly models Z-order priority by decoupling instances.<n>We also introduce a queried alignment loss that explicitly supervises individual instances and enhances semantic consistency.<n>The proposed method effectively reduces ambiguity in overlapping regions, enforces correct occlusion dependencies, and preserves structural integrity, leading to substantial accuracy gains across diverse scenes.
  arXiv  Detail & Related papers  (2026-05-20T16:10:44Z)
• Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graph [8.435985634889285]
  We propose a novel Graph Spatio-Temporal Point ( GSTPP) model for fine-grained event prediction.<n>It adopts an encoder-coder architecture that jointly models the state dynamics of spatially localized regions.<n>The proposed GSTPP model greatly improves the accuracy of fine-grained event prediction.
  arXiv  Detail & Related papers  (2025-01-15T08:38:07Z)
• LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal Data [14.726248469735971]
  Traffic accident forecasting is an important task for intelligent transportation management and emergency response systems.<n>Existing data-driven methods fail to handle the heterogeneous accident patterns over space at different scales.<n>We propose a novel Learning-Integrated Space Partition Framework (LISA) to simultaneously learn partitions while training models.
  arXiv  Detail & Related papers  (2024-12-19T19:52:19Z)
• Typhoon Intensity Prediction with Vision Transformer [51.84456610977905]
  We introduce "Typhoon Intensity Transformer" (Tint) to predict typhoon intensity accurately across space and time. Tint uses self-attention mechanisms with global receptive fields per layer. Experiments on a publicly available typhoon benchmark validate the efficacy of Tint.
  arXiv  Detail & Related papers  (2023-11-28T03:11:33Z)
• SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data [55.609946936979036]
  We propose a novel spatial event ranking approach named SpatialRank. We show that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents.
  arXiv  Detail & Related papers  (2023-09-30T06:20:21Z)
• SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregation [8.636014676778682]
  Open data is frequently released spatially aggregated, usually to comply with privacy policies. But coarse, heterogeneous aggregations complicate coherent learning and integration for downstream AI/ML systems. We propose an overarching model named Structurally-Aware Recurrent Network (SARN), which integrates structurally-aware spatial attention layers into the Gated Recurrent Unit (GRU) model. For scenarios with limited historical training data, we show that a model pre-trained on one city variable can be fine-tuned for another city variable using only a few hundred samples.
  arXiv  Detail & Related papers  (2023-06-09T21:01:29Z)
• Semi-signed neural fitting for surface reconstruction from unoriented point clouds [53.379712818791894]
  We propose SSN-Fitting to reconstruct a better signed distance field. SSN-Fitting consists of a semi-signed supervision and a loss-based region sampling strategy. We conduct experiments to demonstrate that SSN-Fitting achieves state-of-the-art performance under different settings.
  arXiv  Detail & Related papers  (2022-06-14T09:40:17Z)
• Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks [1.9087335681007478]
  In the aftermath of disasters, building damage maps are obtained using change detection to plan rescue operations. Current convolutional neural network approaches do not consider the similarities between neighboring buildings for predicting the damage. We present a novel graph-based building damage detection solution to capture these relationships.
  arXiv  Detail & Related papers  (2022-01-25T15:25:21Z)
• RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery [83.49145695899388]
  RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end. RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
  arXiv  Detail & Related papers  (2020-04-15T19:52:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.