# From Satellite Imagery to Disaster Insights

arXiv: https://arxiv.org/abs/1812.07033 · HTML: https://mlsanity.com/paper/1812.07033/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2018.

## Built on by (most cited first, 9 of 9)

- [RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2202.12361/) (2022)
- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [Single View Geocentric Pose in the Wild](https://mlsanity.com/paper/2105.08229/) (2021)
- [DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition](https://mlsanity.com/paper/2410.13663/) (2024)
- [Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region](https://mlsanity.com/paper/2303.07403/) (2023)
- [From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images](https://mlsanity.com/paper/2507.02781/) (2025)
- [Flood Data Analysis on SpaceNet 8 Using Apache Sedona](https://mlsanity.com/paper/2404.18235/) (2024)
- [Leveraging Self-Supervised Features for Efficient Flooded Region Identification in UAV Aerial Images](https://mlsanity.com/paper/2507.04915/) (2025)
- [Satellite to Street : Disaster Impact Estimator](https://mlsanity.com/paper/2512.00065/) (2025)

## Abstract (arXiv)

The use of satellite imagery has become increasingly popular for disaster monitoring and response. After a disaster, it is important to prioritize rescue operations, disaster response and coordinate relief efforts. These have to be carried out in a fast and efficient manner since resources are often limited in disaster-affected areas and it's extremely important to identify the areas of maximum damage. However, most of the existing disaster mapping efforts are manual which is time-consuming and often leads to erroneous results. In order to address these issues, we propose a framework for change detection using Convolutional Neural Networks (CNN) on satellite images which can then be thresholded and clustered together into grids to find areas which have been most severely affected by a disaster. We also present a novel metric called Disaster Impact Index (DII) and use it to quantify the impact of two natural disasters - the Hurricane Harvey flood and the Santa Rosa fire. Our framework achieves a top F1 score of 81.2% on the gridded flood dataset and 83.5% on the gridded fire dataset.