Localized Debris Detection in Post-Disaster Aerial Imagery Using YOLO-SDD

Rapid Post-earthquake Damage Detection Using UAV Imagery and YOLO: A Case Study from the Kahramanmaras Earthquake

Rapid Debris-Volume Estimation from Post-Hurricane Aerial Imagery

Hurricane debris removal is planned, contracted, and federally reimbursed on the basis of volume estimates, yet operational practice still relies on parametric forecasts with 41-90% documented over-estimation or on truck-load tallies that arrive only after hauling begins. We present DebrisHeightNet, a segmentation-conditioned monocular debris-height network that estimates spatially explicit debris volume from a single pass of post-event aerial RGB imagery, the kind of survey routinely flown within days of a hurricane landfall. We train only a lightweight 1.08 M-parameter head on top of two frozen vision foundation models. This head regresses height from a Depth Anything V2 backbone, conditioned on the debris segmentation of CLIPSeg-debris from our prior work. Because no post-hurricane debris-height ground truth exists, we synthesize the training target by confidence-weighted LiDAR-monocular fusion (CW-LMF), designed to suppress non-debris LiDAR returns. This fused target is a constructed supervision signal rather than ground truth, so we corroborate it against external references rather than claiming it as truth. A region-level power-law calibration, driven by each region's low-density debris fraction, converts model volume into an estimate of the reported hauled debris with quantified uncertainty. Across ten regions spanning five hurricanes and three states, the uncalibrated model agrees with an independent uncrewed-aerial-vehicle (UAV) survey of the training region at Spearman $\rho = 0.87$ and lands within 30% of the reported record where the Hazus and FEMA-hybrid parametric forecasts over-predict it by 2.7-4.8$\times$. Deployment requires no LiDAR, no ground access, and no second flight, so the method can produce spatially explicit volume estimates wherever single-pass post-event imagery is flown.

Unsupervised Estimation of Post-Event Standing Urban Floodwater Depth Using Aerial Imagery and Digital Terrain Models

Accurate estimation of floodwater depth is vital for disaster management but traditionally relies on data-intensive hydrodynamic models or supervised deep learning restricted by labeled data requirements. To address these bottlenecks, this study proposes a fully unsupervised, training-free framework for rapid depth estimation of standing or slowly receding residual floodwater using post-event remote sensing imagery and DTMs. First, a binary flood extent map is automatically delineated by adapting an existing unsupervised color-based segmentation algorithm for UAV imagery. Second, leveraging the hydrostatic equilibrium principle, floodwater depth is computed by integrating the extracted flood footprint with the underlying DTM. This framework was evaluated using the Inundation2Depth dataset, encompassing twelve urban and peri-urban sites in the Southeastern US impacted by Hurricanes Matthew and Florence. Experimental results across all examined sites demonstrated the framework’s viability, with segmentation F1-scores ranging from 63% to 96% and absolute flood depth RMSE ranged from 0.16 m in well-defined catchments to 1.69 m in highly obscured regions. Bypassing the need for manual annotations and task-specific training, the proposed framework offers a scalable, rapidly deployable solution for first-order flood mapping and depth estimation. Its computational efficiency enables execution on standard CPU hardware within seconds, making it ideal for time-critical, on-site emergency response.

HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment

This work presents HASTE (High-speed Assessment and Satellite Tracking for Emergencies), a no-code web platform that lets analysts who are not machine learning engineers produce per-building damage maps from post-disaster satellite imagery, and describes the platform, methods, and the engineering that supports them.

AFTERMAP: A FEMA-Aligned Dataset for Damaged Structure Segmentation in UAV Imagery

Rapid and accurate building damage assessment is essential for effective post-disaster response, yet the development of reliable artificial intelligence (AI) models remains constrained by the limited availability of high-quality, operationally relevant benchmark datasets. To address this gap, this paper presents AFTERMAP (Aerial FEMA-Aligned Targeted Extraction and Reconstruction Mapping for Post-Disaster Building Damage), a benchmark UAV imagery dataset for instance segmentation of post-disaster building damage. The dataset contains 1926 high-resolution UAV images with pixel-level annotations for five building damage categories, following FEMA Preliminary Damage Assessment (PDA) guidelines where applicable: Destroyed, Major Damage, Minor Damage, Tarp, and No Damage. Using AFTERMAP, we benchmarked recent YOLO-based instance segmentation models and evaluated their performance using mAP50 and mAP50-95. Among the evaluated models, YOLO26-L achieved the best performance, obtaining a test mAP50 of 0.600 and outperforming both Mask R-CNN (0.524) and Mask2Former (0.368) in cross-architecture comparisons. Model generalization was further demonstrated through an independent case study using UAV imagery collected after the 2025 Somerset–London tornado in Kentucky, where the trained model successfully identified building-level damage patterns under real-world field conditions. The results demonstrate that AFTERMAP provides a challenging and realistic benchmark for UAV-based post-disaster building damage assessment. By combining FEMA-aligned annotations with high-resolution UAV imagery, the dataset establishes a standardized resource for developing and evaluating instance segmentation models that can support rapid post-disaster damage assessment and emergency response.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge

Rapid post-disaster response requires timely, building-level information on whether structures remain intact, are damaged, or are destroyed. Post-event optical imagery, however, may be unavailable because of cloud, smoke, or darkness. The Bright Challenge evaluated all-weather building damage mapping from a submeter-resolution pre-event optical image and a post-event SAR image. Participants were required to detect and delineate each building and assign exactly one of three mutually exclusive damage labels. The challenge extended the globally distributed \textsc{Bright} dataset with instance-level annotations for about 291,000 buildings across 16 disaster events spanning seven disaster types. The final phase was evaluated exclusively on two 2025 events absent from training: a wildfire event in California and a hurricane in Jamaica. A total of 157 participants made 1,289 submissions, and 46 teams entered the final phase. The two winning solutions achieved test mAPs of 0.182 and 0.181, approximately 8.7 times the public baseline of 0.021, but remained far below the best in-domain holdout score of 0.513. Across teams ranked in both phases, performance declined sharply and the rank order changed substantially. The two leading solutions independently favored modality-specific encoding, staged or late optical--SAR fusion, and an optical-dominant separation of building localization from damage recognition. The winning method additionally used scene-aware threshold adjustment and pseudo-label adaptation. These results identify cross-event generalization and stable severity discrimination as the principal remaining challenges. All data, annotations, baseline code, and winning solutions are publicly available at https://github.com/ChenHongruixuan/BRIGHT.