  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.
