# Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1910.06444 · HTML: https://mlsanity.com/paper/1910.06444/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 30 of 34)

- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning](https://mlsanity.com/paper/2402.01444/) (2024)
- [CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detection](https://mlsanity.com/paper/2406.04207/) (2024)
- [TINYCD: A (Not So) Deep Learning Model For Change Detection](https://mlsanity.com/paper/2207.13159/) (2022)
- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images](https://mlsanity.com/paper/2105.07364/) (2021)
- [Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2205.13769/) (2022)
- [SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection](https://mlsanity.com/paper/2212.01287/) (2022)
- [Continuous Cross-resolution Remote Sensing Image Change Detection](https://mlsanity.com/paper/2305.14722/) (2023)
- [FoBa: A Foreground-Background co-Guided Method and New Benchmark for Remote Sensing Semantic Change Detection](https://mlsanity.com/paper/2509.15788/) (2025)
- [Exploring Effective Priors and Efficient Models for Weakly-Supervised Change Detection](https://mlsanity.com/paper/2307.10853/) (2023)
- [DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery](https://mlsanity.com/paper/2405.04800/) (2024)
- [BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge](https://mlsanity.com/paper/2201.10389/) (2022)
- [End-to-end Remote Sensing Change Detection of Unregistered Bi-temporal Images for Natural Disasters](https://mlsanity.com/paper/2307.15128/) (2023)
- [Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data](https://mlsanity.com/paper/2405.13779/) (2024)
- [Learning Efficient Unsupervised Satellite Image-based Building Damage Detection](https://mlsanity.com/paper/2312.01576/) (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)
- [Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning](https://mlsanity.com/paper/2107.04983/) (2021)
- [Building Damage Mapping with Self-PositiveUnlabeled Learning](https://mlsanity.com/paper/2111.02586/) (2021)
- [Disaster mapping from satellites: damage detection with crowdsourced point labels](https://mlsanity.com/paper/2111.03693/) (2021)
- [Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks](https://mlsanity.com/paper/2201.10395/) (2022)
- [Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning](https://mlsanity.com/paper/2405.17734/) (2024)
- [Cross Branch Feature Fusion Decoder for Consistency Regularization-based Semi-Supervised Change Detection](https://mlsanity.com/paper/2409.15021/) (2024)
- [GaLeNet: Multimodal Learning for Disaster Prediction, Management and Relief](https://mlsanity.com/paper/2206.09242/) (2022)
- [Multi-view deep learning for reliable post-disaster damage classification](https://mlsanity.com/paper/2208.03419/) (2022)
- [Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing](https://mlsanity.com/paper/2503.07911/) (2025)
- [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)
- [Flood Data Analysis on SpaceNet 8 Using Apache Sedona](https://mlsanity.com/paper/2404.18235/) (2024)

## Abstract (arXiv)

In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite images is slow and labor-intensive. In this work, we use machine learning to automate the detection of building damage in satellite imagery. We compare the performance of four different convolutional neural network models in detecting damaged buildings in the 2010 Haiti earthquake. We also quantify how well the models will generalize to future disasters by training and testing models on different disaster events.