Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series

Abstract

The synergistic use of remote sensing and unsupervised machine learning has emerged as a potential tool for addressing a variety of environmental monitoring applications, such as detecting disaster-affected areas and deforestation. This paper proposes a new machine-intelligent approach to detecting and characterizing spatio-temporal changes on the Earth’s surface by using remote sensing data and unsupervised learning. Our framework was designed to be fully automatic by integrating unsupervised anomaly detection models, remote sensing image series, and open data extracted from the Google Earth Engine platform. The methodology was evaluated by taking both simulated and real-world environmental data acquired from several imaging sensors, including Landsat-8 OLI, Sentinel-2 MSI, and Terra MODIS. The experimental results were measured with the kappa and F1-score metrics, and they indicated an assertiveness level of 0.85 for the change detection task, demonstrating the accuracy and robustness of the proposed approach when addressing distinct environmental monitoring applications, including the detection of disaster-affected areas and deforestation mapping.

Analysis of Eco-Environmental Quality and Driving Forces in Opencast Coal Mining Area Based on GWANN Model: A Case Study in Shengli Coalfield, China

“…This can ensure a moderate change in the contribution of driving factor i; the threshold value was proven by Li et al [17]. Then, the new predicted value, RSEI ˆxi , corresponding to the increase in driving factor i was calculated, and the partial derivatives of each driving factor i were calculated respectively, as in Equation (13). Finally, the partial derivatives of each factor were normalized, and the contribution of each driving factor i to the RSEI was obtained using Equation (14).…”

“…GEE, which can provide users with massive multisource remote sensing data, has large spatial analysis and supercomputing power ability, greatly saves time and cost, and is often used in ecological quality assessment such as eco-environmental assessment based on RSEI [13][14][15]. In recent years, geographically weighted regression (GWR) has been widely used in ecological research and evaluation, but simple linear regression models cannot accurately fit complex ecological problems [16,17].…”

Unsupervised burned areas detection using multitemporal synthetic aperture radar data

“…In this context, the Remote Sensing technology emerges as an extremely useful technology, since it allows for extensive spatial and multitemporal analyses. Among a plethora of studies in the literature, remote sensing data have been successfully employed for landslides detection, 3 – 5 flood monitoring, 6 , 7 dam failure assessment, 8 – 10 and extreme weather event analysis 11 – 13 These techniques have proven valuable in providing timely and accurate information to support disaster response and management efforts.…”