Building Damage Assessment

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

16 shown of 16 papers with code (32 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Building Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks
  4 Jul 2018 6 repositories listed
  In this paper, we propose to improve the efficiency of building damage assessment by applying image classification algorithms to post-hurricane satellite imagery.
• xBD: A Dataset for Assessing Building Damage from Satellite Imagery
  21 Nov 2019 4 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  xBD is the largest building damage assessment dataset to date, containing 850, 736 building annotations across 45, 362 km\textsuperscript{2} of imagery.
• Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images
  3 Aug 2022 2 repositories listed
  In this work, a novel transformer-based network is proposed for assessing building damage.
• Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery
  7 Jun 2025 1 repository listed
  Most post-disaster damage classifiers succeed only when destructive forces leave clear spectral or structural signatures -- conditions rarely present after inundation.
• Now you see it, Now you don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery
  12 May 2025 1 repository listed
  This paper audits damage labels derived from coincident satellite and drone aerial imagery for 15, 814 buildings across Hurricanes Ian, Michael, and Harvey, finding 29.
• Building-Guided Pseudo-Label Learning for Cross-Modal Building Damage Mapping
  8 May 2025 1 repository listed
  Accurate building damage assessment using bi-temporal multi-modal remote sensing images is essential for effective disaster response and recovery planning.
• DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization
  15 Apr 2025 1 repository listed
  In fact, the determination of damage typology is crucial for response and recovery efforts.
• Change3D: Revisiting Change Detection and Captioning from A Video Modeling Perspective
  24 Mar 2025 1 repository listed
  Therefore, we can get rid of the intricate change extractors, providing a unified framework for different change detection and captioning tasks.
• BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response
  10 Jan 2025 1 repository listed
  In this paper, we present a BDA dataset using veRy-hIGH-resoluTion optical and SAR imagery (BRIGHT) to support AI-based all-weather disaster response.
• ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model
  4 Apr 2024 1 repository listed Syntology ran 5 of 9 samples · 4 unverified
  Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD).
• Learning Efficient Unsupervised Satellite Image-based Building Damage Detection
  4 Dec 2023 1 repository listed
  Existing Building Damage Detection (BDD) methods always require labour-intensive pixel-level annotations of buildings and their conditions, hence largely limiting their applications.
• Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets
  31 May 2022 1 repository listed
  In the field of post-disaster assessment, for timely and accurate rescue and localization after a disaster, people need to know the location of damaged buildings.
• Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks
  25 Jan 2022 1 repository listed
  In the aftermath of disasters, building damage maps are obtained using change detection to plan rescue operations.
• Fully convolutional Siamese neural networks for buildings damage assessment from satellite images
  31 Oct 2021 1 repository listed
  In this work, we develop a computational approach for an automated comparison of the same region's satellite images before and after the disaster, and classify different levels of damage in buildings.
• BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
  16 May 2021 1 repository listed
  With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings.
• Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion
  12 Apr 2020 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  Automatic change detection and disaster damage assessment are currently procedures requiring a huge amount of labor and manual work by satellite imagery analysts.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.