# Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images

arXiv: https://arxiv.org/abs/2208.02205 · HTML: https://mlsanity.com/paper/2208.02205/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 63 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion](https://mlsanity.com/paper/2004.05525/) (2020)
- [RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery](https://mlsanity.com/paper/2004.07312/) (2020)
- [BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images](https://mlsanity.com/paper/2105.07364/) (2021)
- [An Attention-Based System for Damage Assessment Using Satellite Imagery](https://mlsanity.com/paper/2004.06643/) (2020)
- [Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking](https://mlsanity.com/paper/2111.09117/) (2021)
- [Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks](https://mlsanity.com/paper/2111.09862/) (2021)

## Built on by (most cited first, 24 of 24)

- [BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response](https://mlsanity.com/paper/2501.06019/) (2025)
- [CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery](https://mlsanity.com/paper/2407.17673/) (2024)
- [DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization](https://mlsanity.com/paper/2504.11637/) (2025)
- [ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting Street View Images](https://mlsanity.com/paper/2306.03050/) (2023)
- [Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach](https://mlsanity.com/paper/2404.07399/) (2024)
- [Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean](https://mlsanity.com/paper/2307.16177/) (2023)
- [An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series](https://mlsanity.com/paper/2406.02506/) (2024)
- [Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery](https://mlsanity.com/paper/2506.06667/) (2025)
- [CrisiSense-RAG: Crisis Sensing Multimodal Retrieval-Augmented Generation for Rapid Disaster Impact Assessment](https://mlsanity.com/paper/2602.13239/) (2026)
- [DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints](https://mlsanity.com/paper/2605.27957/) (2026)
- [Post-Hurricane Debris Segmentation Using Fine-Tuned Foundational Vision Models](https://mlsanity.com/paper/2504.12542/) (2025)
- [Recov-Vision: Linking Street View Imagery and Vision-Language Models for Post-Disaster Recovery](https://mlsanity.com/paper/2509.20628/) (2025)
- [Multi-step feature fusion for natural disaster damage assessment on satellite images](https://mlsanity.com/paper/2410.21901/) (2024)
- [The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2](https://mlsanity.com/paper/2511.05461/) (2025)
- [Damage-TriageFormer: A Foundation-Model Framework for Typology-Based Building Damage Assessment from Mono-Temporal Imagery](https://mlsanity.com/paper/2606.12248/) (2026)
- [Beyond Backscatter: AlphaEarth Land-Cover Priors for Rapid SAR Flood Segmentation Across Foundation Backbones](https://mlsanity.com/paper/2606.29134/) (2026)
- [A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake](https://mlsanity.com/paper/2506.22338/) (2025)
- [AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directions](https://mlsanity.com/paper/2507.01547/) (2025)
- [Structured AI Decision-Making in Disaster Management](https://mlsanity.com/paper/2509.01576/) (2025)
- [Multi-Modal Building Inspection via Perceiver IO Fusion of Satellite and Street-Level Imagery](https://mlsanity.com/paper/2605.26381/) (2026)
- [HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment](https://mlsanity.com/paper/2607.11838/) (2026)
- [Adaptive Sampling for Automated Post-Disaster Rapid Damage Assessment via Level-Set Cost-Aware Bayesian Optimization](https://mlsanity.com/paper/2608.02868/) (2026)
- [Rapid Debris-Volume Estimation from Post-Hurricane Aerial Imagery](https://mlsanity.com/paper/2608.17165/) (2026)
- [Keep Your Friends Close, and the Right Neighbours Closer: Disaster-Conditioned Kernel-Regularized Graph Attention for Building Damage Classification](https://mlsanity.com/paper/2608.20548/) (2026)

## Abstract (arXiv)

This paper presents dahitra, a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of natural disasters. Satellite imagery provides real-time and high-coverage information and offers opportunities to inform large-scale post-disaster building damage assessment, which is critical for rapid emergency response. In this work, a novel transformer-based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures the temporal differences in the feature domain after applying a transformer encoder on the spatial features. The proposed network achieves state-of-the-art performance when tested on a large-scale disaster damage dataset (xBD) for building localization and damage classification, as well as on LEVIR-CD dataset for change detection tasks. In addition, this work introduces a new high-resolution satellite imagery dataset, Ida-BD (related to 2021 Hurricane Ida in Louisiana in 2021) for domain adaptation. Further, it demonstrates an approach of using this dataset by adapting the model with limited fine-tuning and hence applying the model to newly damaged areas with scarce data.