# Train and Deploy an Image Classifier for Disaster Response

arXiv: https://arxiv.org/abs/2005.05495 · HTML: https://mlsanity.com/paper/2005.05495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 1 of 1)

- [DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition](https://mlsanity.com/paper/2410.13663/) (2024)

## Abstract (arXiv)

With Deep Learning Image Classification becoming more powerful each year, it is apparent that its introduction to disaster response will increase the efficiency that responders can work with. Using several Neural Network Models, including AlexNet, ResNet, MobileNet, DenseNets, and 4-Layer CNN, we have classified flood disaster images from a large image data set with up to 79% accuracy. Our models and tutorials for working with the data set have created a foundation for others to classify other types of disasters contained in the images.