# Satellite imagery analysis for operational damage assessment in Emergency situations

arXiv: https://arxiv.org/abs/1803.00397 · HTML: https://mlsanity.com/paper/1803.00397/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2018.

## Built on by (most cited first, 6 of 6)

- [Lebanon Solar Rooftop Potential Assessment using Buildings Segmentation from Aerial Images](https://mlsanity.com/paper/2111.11397/) (2021)
- [Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2105.05516/) (2021)
- [Challenges in data-based geospatial modeling for environmental research and practice](https://mlsanity.com/paper/2311.11057/) (2023)
- [Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery](https://mlsanity.com/paper/2201.10523/) (2022)
- [AI for Anticipatory Action: Moving Beyond Climate Forecasting](https://mlsanity.com/paper/2307.15727/) (2023)
- [Automated National Urban Map Extraction](https://mlsanity.com/paper/2404.06202/) (2024)

## Abstract (arXiv)

When major disaster occurs the questions are raised how to estimate the damage in time to support the decision making process and relief efforts by local authorities or humanitarian teams. In this paper we consider the use of Machine Learning and Computer Vision on remote sensing imagery to improve time efficiency of assessment of damaged buildings in disaster affected area. We propose a general workflow that can be useful in various disaster management applications, and demonstrate the use of the proposed workflow for the assessment of the damage caused by the wildfires in California in 2017.