Real-Time Change Detection At the Edge

Abstract

Detecting changes in real-time using remote sensing data is of paramount importance in areas such as crop health monitoring, weed detection, and disaster management. However, real-time change detection using remote sensing imagery faces several challenges: a) it requires real-time data extraction which is a challenge for traditional satellite imagery sources such as MODIS and LANDSAT due to the latency associated with collecting and processing the data. Due to the advances made in the past decade in drone technology, Unmanned Aerial Vehicles (UAVs) can be used for real-time data collection. However, a large percentage of this data will be unlabeled which limits the use of well-known supervised machine learning methods; b) from an infrastructure perspective, the cloud-edge solution of processing the data collected from UAVs (edge) only on the cloud is also constrained by latency and bandwidth-related issues. Due to these limitations, transferring large amounts of data between cloud and edge, or storing large amounts of information regarding past time periods on an edge device is infeasible. We can limit the amount of data transferred between the cloud and edge by performing analyses on-the-fly at the edge using low-power devices (edge devices) that can be connected to UAVs. However, edge devices have computational and memory bottlenecks, which would limit the usage of complex machine learning algorithms. In this paper, we demonstrate how an unsupervised GMM-based real-time change detection method at the edge can be used to identify weeds in real-time. We evaluate the scalability of our method on edge computing and traditional devices such as NVIDIA Jetson TX2, RTX 2080, and traditional Intel CPUs. We perform a case study for weed detection on images collected from UAVs. Our results demonstrate both the efficacy and computational efficiency of our method.