Environmental impact assessment is now an integral part of mining operations. Remote sensing data enables the identification, delineating, and monitoring of pollution sources and affected areas, including derelict land, and changes in surface land use and to water bodies. The aim of this work was to evaluate the use of multi-temporal Landsat-5 and Landsat-7 images, SPOT Panchromatic, and ASTER data to map the natural environment on a local scale, and to assess the impact of mining activities by indicating the changes on land and water resources. Three case studies are presented: Lake Vegoritis and the Amynteon mine (both located in northern Greece) and the Lavrio mine area, in central Greece. We found that using high resolution satellite remote sensing data and state of the art GIS techniques with parallel development of a fully integrated geospatial database system provided monitoring and feedback at appropriate spatial scales; therefore, such data can be used for long-term environmental management and monitoring of reclamation and rehabilitation of mining areas.

Generating Digital Elevation Models (DEM) from satellite imagery or other data sources constitutes an essential tool for a plethora of applications and disciplines, ranging from 3D flight planning and simulation, autonomous driving and satellite navigation, such as GPS, to modeling water flow, precision farming and forestry. The task of extracting this 3D geometry from a given surface hitherto requires a combination of appropriately collected corresponding samples and/or specialized equipment, as inferring the elevation from single image data is out of reach for contemporary approaches. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have experienced unprecedented growth in recent years as they can extrapolate rules in a data-driven manner and retrieve convoluted, nonlinear one-to-one mappings, such as an approximate mapping from satellite imagery to DEMs. Therefore, we propose an end-to-end Deep Learning (DL) approach to construct this mapping and to generate an absolute or relative point cloud estimation of a DEM given a single RGB satellite (Sentinel-2 imagery in this work) or drone image. The model has been readily extended to incorporate available information from the non-visible electromagnetic spectrum. Unlike existing methods, we only exploit one image for the production of the elevation data, rendering our approach less restrictive and constrained, but suboptimal compared to them at the same time. Moreover, recent advances in software and hardware allow us to make the inference and the generation extremely fast, even on moderate hardware. We deploy Conditional Generative Adversarial networks (CGAN), which are the state-of-the-art approach to image-to-image translation. We expect our work to serve as a springboard for further development in this field and to foster the integration of such methods in the process of generating, updating and analyzing DEMs.

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