Automated remote monitoring of technogenically disturbed areas through machine training methods

This article deals with a calling issue of sustainable development in regions with mining industries. The authors developed an algorithm and presented the architecture of a software system for automated remote monitoring of man-made land problems. It is based on comprehensive application of modern machine training methods, specifically self-supervised learning (Dino, MAE, MoCo) and the Vision Transformer architecture for the analysis of multispectral satellite imagery from open sources (Sentinel, Landsat). The proposed solution automates the entire data management cycle from gathering and preprocessing to segmentation of objects (quarries, waste heaps), area measurement, and time-series analysis of spectral indices, such as the normalized difference vegetation (NDVI) and the normalized difference water (NDWI) ones, which serve as indicators of vegetation cover and water body condition, respectively. The system is integrated with GIS via a QGIS module, and its functionality is accessible via an API, ensuring easy use and integration into existing workflows. The proposed approach provides increased efficiency and accuracy of monitoring, making a basis for informed management decisions in the area of rational employing natural- and subsoil resources