# Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2201.10395 · HTML: https://mlsanity.com/paper/2201.10395/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [Graph Convolutional Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2008.02457/) (2020)
- [Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks](https://mlsanity.com/paper/1910.06444/) (2019)
- [Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis](https://mlsanity.com/paper/2006.05612/) (2020)
- [Assessing out-of-domain generalization for robust building damage detection](https://mlsanity.com/paper/2011.10328/) (2020)

## Built on by (most cited first, 2 of 2)

- [Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation](https://mlsanity.com/paper/2411.03223/) (2024)
- [Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network](https://mlsanity.com/paper/2605.16764/) (2026)

## Abstract (arXiv)

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. Our proposed model architecture learns from both local and neighborhood features to predict building damage. Specifically, we adopt the sample and aggregate graph convolution strategy to learn aggregation functions that generalize to unseen graphs which is essential for alleviating the time needed to obtain predictions for new disasters. Our experiments on the xBD dataset and comparisons with a classical convolutional neural network reveal that while our approach is handicapped by class imbalance, it presents a promising and distinct advantage when it comes to cross-disaster generalization.