# Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles

## TL;DR

This paper presents a lightweight deep learning model for real-time aerial image classification on UAVs, enhancing emergency response capabilities by efficiently identifying disaster scenarios with minimal memory use.

## Contribution

It introduces a new UAV aerial image database for emergencies and develops a lightweight CNN that outperforms existing models in efficiency and accuracy.

## Key findings

- Achieved ~3x higher performance than existing models
- Maintained less than 2% accuracy drop compared to state-of-the-art
- Developed a CNN suitable for embedded platforms in UAVs

## Abstract

Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to access areas. In addition, by utilizing an embedded platform and deep learning UAVs can autonomously monitor a disaster stricken area, analyze the image in real-time and alert in the presence of various calamities such as collapsed buildings, flood, or fire in order to faster mitigate their effects on the environment and on human population. To this end, this paper focuses on the automated aerial scene classification of disaster events from on-board a UAV. Specifically, a dedicated Aerial Image Database for Emergency Response (AIDER) applications is introduced and a comparative analysis of existing approaches is performed. Through this analysis a lightweight convolutional neural network (CNN) architecture is developed, capable of running efficiently on an embedded platform achieving ~3x higher performance compared to existing models with minimal memory requirements with less than 2% accuracy drop compared to the state-of-the-art. These preliminary results provide a solid basis for further experimentation towards real-time aerial image classification for emergency response applications using UAVs.