# Deep Learning-based Aerial Image Segmentation with Open Data for Disaster Impact Assessment

arXiv: https://arxiv.org/abs/2006.05575 · HTML: https://mlsanity.com/paper/2006.05575/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints](https://mlsanity.com/paper/2312.02464/) (2023)
- [Deep learning universal crater detection using Segment Anything Model (SAM)](https://mlsanity.com/paper/2304.07764/) (2023)
- [Recov-Vision: Linking Street View Imagery and Vision-Language Models for Post-Disaster Recovery](https://mlsanity.com/paper/2509.20628/) (2025)
- [Shape Distribution Matters: Shape-specific Mixture-of-Experts for Amodal Segmentation under Diverse Occlusions](https://mlsanity.com/paper/2508.01664/) (2025)
- [Deep Feature Response Discriminative Calibration](https://mlsanity.com/paper/2411.13582/) (2024)

## Abstract (arXiv)

Satellite images are an extremely valuable resource in the aftermath of natural disasters such as hurricanes and tsunamis where they can be used for risk assessment and disaster management. In order to provide timely and actionable information for disaster response, in this paper a framework utilising segmentation neural networks is proposed to identify impacted areas and accessible roads in post-disaster scenarios. The effectiveness of pretraining with ImageNet on the task of aerial image segmentation has been analysed and performances of popular segmentation models compared. Experimental results show that pretraining on ImageNet usually improves the segmentation performance for a number of models. Open data available from OpenStreetMap (OSM) is used for training, forgoing the need for time-consuming manual annotation. The method also makes use of graph theory to update road network data available from OSM and to detect the changes caused by a natural disaster. Extensive experiments on data from the 2018 tsunami that struck Palu, Indonesia show the effectiveness of the proposed framework. ENetSeparable, with 30% fewer parameters compared to ENet, achieved comparable segmentation results to that of the state-of-the-art networks.