AI-Enabled Autonomous Drones for Fast Climate Change Crisis Assessment

Climate change is one of the greatest challenges for modern societies. Its consequences, often associated with extreme events, have dramatic results worldwide. New synergies between different disciplines, including artificial intelligence (AI), Internet of Things (IoT), and edge computing can lead to radically new approaches for the real-time tracking of natural disasters that are also designed to reduce the environmental footprint. In this article, we propose an AI-based pipeline for processing natural disaster images taken from drones. The purpose of this pipeline is to reduce the number of images to be processed by the first responders of the natural disaster. It consists of three main stages: 1) a lightweight autoencoder based on deep learning; 2) a dimensionality reduction using the
$t$
-distributed stochastic neighbor embedding algorithm; and 3) a fuzzy clustering procedure. This pipeline is evaluated on several edge computing platforms with low-power accelerators to assess the design of intelligent autonomous drones to provide this service in real time. Our experimental evaluation focuses on flooding, showing that the amount of information to be processed is substantially reduced, whereas edge computing platforms with low-power graphics accelerators are placed as a compelling alternative for processing these heavy computational workloads, obtaining a performance loss of only
$2.3\times $
compared to its cloud counterpart version, running both the training and inference steps.