# An Attention-Based System for Damage Assessment Using Satellite Imagery

arXiv: https://arxiv.org/abs/2004.06643 · HTML: https://mlsanity.com/paper/2004.06643/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 11 of 11)

- [Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images](https://mlsanity.com/paper/2208.02205/) (2022)
- [BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images](https://mlsanity.com/paper/2105.07364/) (2021)
- [Robust Building Damage Detection in Cross-Disaster Settings Using Domain Adaptation](https://mlsanity.com/paper/2603.14694/) (2026)
- [Building Damage Mapping with Self-PositiveUnlabeled Learning](https://mlsanity.com/paper/2111.02586/) (2021)
- [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)
- [DamageScope: Vision-Language Retrieval at Scale for Disaster Damage Assessment from Satellite Imagery](https://mlsanity.com/paper/2608.21529/) (2026)
- [Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery](https://mlsanity.com/paper/2201.10523/) (2022)
- [Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks](https://mlsanity.com/paper/2210.12310/) (2022)
- [AB2CD: AI for Building Climate Damage Classification and Detection](https://mlsanity.com/paper/2309.01066/) (2023)
- [Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer Learning](https://mlsanity.com/paper/2401.02424/) (2023)

## Abstract (arXiv)

When disaster strikes, accurate situational information and a fast, effective response are critical to save lives. Widely available, high resolution satellite images enable emergency responders to estimate locations, causes, and severity of damage. Quickly and accurately analyzing the extensive amount of satellite imagery available, though, requires an automatic approach. In this paper, we present Siam-U-Net-Attn model - a multi-class deep learning model with an attention mechanism - to assess damage levels of buildings given a pair of satellite images depicting a scene before and after a disaster. We evaluate the proposed method on xView2, a large-scale building damage assessment dataset, and demonstrate that the proposed approach achieves accurate damage scale classification and building segmentation results simultaneously.