# EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring Using Atrous Convolutional Feature Fusion

arXiv: https://arxiv.org/abs/2104.14006 · HTML: https://mlsanity.com/paper/2104.14006/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 58 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://mlsanity.com/paper/1403.6382/) (2014)
- [Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers](https://mlsanity.com/paper/1801.06889/) (2018)
- [Data Augmentation by Pairing Samples for Images Classification](https://mlsanity.com/paper/1801.02929/) (2018)
- [Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car](https://mlsanity.com/paper/1704.07911/) (2017)
- [Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles](https://mlsanity.com/paper/1906.08716/) (2019)
- [ScratchDet: Training Single-Shot Object Detectors from Scratch](https://mlsanity.com/paper/1810.08425/) (2018)
- [Efficient ConvNet-based Object Detection for Unmanned Aerial Vehicles by Selective Tile Processing](https://mlsanity.com/paper/1911.06073/) (2019)
- [Disaster Monitoring using Unmanned Aerial Vehicles and Deep Learning](https://mlsanity.com/paper/1807.11805/) (2018)
- [Applications of Online Deep Learning for Crisis Response Using Social Media Information](https://mlsanity.com/paper/1610.01030/) (2016)

## Built on by (most cited first, 18 of 18)

- [RescueADI: Adaptive Disaster Interpretation in Remote Sensing Images with Autonomous Agents](https://mlsanity.com/paper/2410.13384/) (2024)
- [Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision](https://mlsanity.com/paper/2506.06253/) (2025)
- [FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management](https://mlsanity.com/paper/2412.02831/) (2024)
- [Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets](https://mlsanity.com/paper/2205.15688/) (2022)
- [DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition](https://mlsanity.com/paper/2410.13663/) (2024)
- [LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery](https://mlsanity.com/paper/2406.02780/) (2024)
- [UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking](https://mlsanity.com/paper/2409.03245/) (2024)
- [TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios](https://mlsanity.com/paper/2501.05880/) (2025)
- [Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2505.09106/) (2025)
- [MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks](https://mlsanity.com/paper/2608.01128/) (2026)
- [UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model](https://mlsanity.com/paper/2501.12087/) (2025)
- [Decoupling Ego-Motion from Target Dynamics via Dual-Interval Motion Cues for UAV Detection](https://mlsanity.com/paper/2605.22605/) (2026)
- [Cross-Geography Generalization of Machine Learning Methods for Classification of Flooded Regions in Aerial Images](https://mlsanity.com/paper/2210.01588/) (2022)
- [Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning](https://mlsanity.com/paper/2412.10756/) (2024)
- [Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques](https://mlsanity.com/paper/2503.02510/) (2025)
- [GlimmerNet: A Lightweight Grouped Dilated Depthwise Convolutions for UAV-Based Emergency Monitoring](https://mlsanity.com/paper/2512.07391/) (2025)
- [Asynchronous Probability Ensembling for Federated Disaster Detection](https://mlsanity.com/paper/2604.14450/) (2026)
- [BoltNet: An Ultra-Lightweight Convolutional Network for On-Device Plant Species Identification](https://mlsanity.com/paper/2608.11844/) (2026)

## Abstract (arXiv)

Deep learning-based algorithms can provide state-of-the-art accuracy for remote sensing technologies such as unmanned aerial vehicles (UAVs)/drones, potentially enhancing their remote sensing capabilities for many emergency response and disaster management applications. In particular, UAVs equipped with camera sensors can operating in remote and difficult to access disaster-stricken areas, analyze the image 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. However, the integration of deep learning introduces heavy computational requirements, preventing the deployment of such deep neural networks in many scenarios that impose low-latency constraints on inference, in order to make mission-critical decisions in real time. To this end, this article focuses on the efficient aerial image classification from on-board a UAV for emergency response/monitoring applications. Specifically, a dedicated Aerial Image Database for Emergency Response applications is introduced and a comparative analysis of existing approaches is performed. Through this analysis a lightweight convolutional neural network architecture is proposed, referred to as EmergencyNet, based on atrous convolutions to process multiresolution features and capable of running efficiently on low-power embedded platforms achieving upto 20x higher performance compared to existing models with minimal memory requirements with less than 1% accuracy drop compared to state-of-the-art models.