**1.3 Relevant studies**

Several studies have utilized satellite data and remote sensing methods to investigate an issue related to mining activities. Monitoring and evaluating reclamation procedure in mining areas is a common application [16]. LaJeunesse Connette et al. [17] developed a methodology to detect mining areas and evaluate mining expansion in Myanmar. For this reason they used data free of charge and open-source software. Likewise, Li et al. [18] employed multitemporal Landsat data to monitor the expansion of coal mining activity. Demirel et al. [6] proposed a methodology for detecting land use changes in surface coal mines with the use of multi-temporal high-resolution satellite data. Similarly, Guan et al. [19] investigated land use changes in a surface coal mine area located in the northeast China. In addition, Latifovic et al. [20] presented a methodology for land-cover change evaluation in the Athabasca Oil Sands region, northeast Alberta, Canada. For this purpose Landsat data were obtained. Maxwell et al. [21] combined very high resolution imagery and LIDAR data for mapping land-cover of a surface coal mine area in the southern coalfields of West Virginia, USA. Demirel et al. [22] investigated the potential implementation of a machine learning classifier (Support Vector Machines) for classifying high spatial resolution multispectral data of an open-cast mine area. Lechner et al. [23] carried out a spatial assessment of mine disturbance and rehabilitation of an open-pit mining study area. Townsend et al. [24] presented a methodology for quantifying land-use and land-cover change patterns due to surface mining and reclamation in the Central Appalachian Mountain region of the Eastern U.S., during a 30-year timeframe.

## **1.4 Scope of the study**

The primary objective of the present work is to provide an object-based methodology for rapid detection and delineation of an open-pit mining area boundaries located nearby Amyntaio town, in northwestern Greece. Since image segmentation quality is a critical part in our analysis, an unsupervised evaluation of image segmentation performance was conducted, quantifying the internal homogeneity of segments and between segment separability.
