# Damage Estimation and Localization from Sparse Aerial Imagery

arXiv: https://arxiv.org/abs/2111.03708 · HTML: https://mlsanity.com/paper/2111.03708/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [Lending Orientation to Neural Networks for Cross-view Geo-localization](https://mlsanity.com/paper/1903.12351/) (2019)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [Cross-View Image Matching for Geo-localization in Urban Environments](https://mlsanity.com/paper/1703.07815/) (2017)
- [RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery](https://mlsanity.com/paper/2004.07312/) (2020)
- [Satellite Image-based Localization via Learned Embeddings](https://mlsanity.com/paper/1704.01133/) (2017)
- [UAV Pose Estimation using Cross-view Geolocalization with Satellite Imagery](https://mlsanity.com/paper/1809.05979/) (2018)
- [Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery](https://mlsanity.com/paper/1812.05581/) (2018)
- [Large Scale Organization and Inference of an Imagery Dataset for Public Safety](https://mlsanity.com/paper/1908.09006/) (2019)

## Abstract (arXiv)

Aerial images provide important situational awareness for responding to natural disasters such as hurricanes. They are well-suited for providing information for damage estimation and localization (DEL); i.e., characterizing the type and spatial extent of damage following a disaster. Despite recent advances in sensing and unmanned aerial systems technology, much of post-disaster aerial imagery is still taken by handheld DSLR cameras from small, manned, fixed-wing aircraft. However, these handheld cameras lack IMU information, and images are taken opportunistically post-event by operators. As such, DEL from such imagery is still a highly manual and time-consuming process. We propose an approach to both detect damage in aerial images and localize it in world coordinates, with specific focus on detecting and localizing flooding. The approach is based on using structure from motion to relate image coordinates to world coordinates via a projective transformation, using class activation mapping to detect the extent of damage in an image, and applying the projective transformation to localize damage in world coordinates. We evaluate the performance of our approach on post-event data from the 2016 Louisiana floods, and find that our approach achieves a precision of 88%. Given this high precision using limited data, we argue that this approach is currently viable for fast and effective DEL from handheld aerial imagery for disaster response.