# Fully convolutional Siamese neural networks for buildings damage assessment from satellite images

arXiv: https://arxiv.org/abs/2111.00508 · HTML: https://mlsanity.com/paper/2111.00508/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 31 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification](https://mlsanity.com/paper/1707.02131/) (2017)
- [TernausNetV2: Fully Convolutional Network for Instance Segmentation](https://mlsanity.com/paper/1806.00844/) (2018)
- [Feature Pyramid Network for Multi-Class Land Segmentation](https://mlsanity.com/paper/1806.03510/) (2018)
- [Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery](https://mlsanity.com/paper/1806.05182/) (2018)
- [Roof material classification from aerial imagery](https://mlsanity.com/paper/2004.11482/) (2020)

## Built on by (most cited first, 4 of 4)

- [Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event](https://mlsanity.com/paper/2306.12589/) (2023)
- [Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery](https://mlsanity.com/paper/2310.01565/) (2023)
- [A General Purpose Neural Architecture for Geospatial Systems](https://mlsanity.com/paper/2211.02348/) (2022)
- [Robust Building Damage Detection in Cross-Disaster Settings Using Domain Adaptation](https://mlsanity.com/paper/2603.14694/) (2026)

## Abstract (arXiv)

Damage assessment after natural disasters is needed to distribute aid and forces to recovery from damage dealt optimally. This process involves acquiring satellite imagery for the region of interest, localization of buildings, and classification of the amount of damage caused by nature or urban factors to buildings. In case of natural disasters, this means processing many square kilometers of the area to judge whether a particular building had suffered from the damaging factors. 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. Our solution is based on Siamese neural networks with encoder-decoder architecture. We include an extensive ablation study and compare different encoders, decoders, loss functions, augmentations, and several methods to combine two images. The solution achieved one of the best results in the Computer Vision for Building Damage Assessment competition.