# Train and Deploy an Image Classifier for Disaster Response

Source: [Train and Deploy an Image Classifier for Disaster Response](<https://www.sota2.com/research/paper/68fa678f1e209131cea16fcb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:17:13.228Z

Authors: Jianyu Mao, Kiana Harris, Nae-Rong Chang, Caleb Pennell, Yiming Ren

Published: 2020-05-12T00:45:48.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2005.05495>)

[PDF](<https://arxiv.org/pdf/2005.05495.pdf>)