In response to the escalating demand for mineral resources and the imperative for sustainable management of natural assets, the development of effective methods for monitoring mining excavations is essential. This study presents an innovative decision-making model that employs a suite of spectral indices for the sustainable monitoring of mining activities. The integration of the Combinational Build-up Index (CBI) with additional spectral indices such as BRBA and BAEI, alongside multitemporal analysis, enhances the detection and differentiation of mining areas, ensuring greater stability and reliability of results, particularly when applied to single datasets from the Sentinel-2 satellite. The research indicates that the average accuracy of excavation detection (overall accuracy, OA) for all test fields and data is approximately 72–74%, varying with the method employed. Utilizing a single CBI index often results in a significant overestimation of producer’s accuracy (PA) over user’s accuracy (UA), by about 10–14%. Conversely, the introduction of a set of three complementary indices achieves a balance between PA and UA, with discrepancies of approximately 1–3%, and narrows the range of result variations across different datasets. Furthermore, the study underscores the limitations of employing average threshold values for excavation monitoring and suggests the adoption of dedicated monthly thresholds to diminish accuracy variability. These findings could have considerable implications for the advancement of autonomous and largely automated systems for the surveillance of illegal mining excavations, providing a predictable and reliable methodology for remote sensing applications in environmental monitoring.

The aim of this paper is to contribute to the long-standing topographical enigma of the identification of Gaugamela. In this study, a GIS method known as viewshed analysis is employed to solve a certain historical problem.¹ According to ancient sources, on the eve of the battle the approaching Macedonian army and the Persian troops that were waiting on the battlefield could not see each other because of intervening hills at a distance of c. 12 km. However, the two armies gained a full view of their respective positions once the Macedonians reached the hills c. six km away from the Persian positions. Our analysis shows that the identification of the battlefield near Tell Gomel, in the Kurdistan Region of Iraq, is consistent with the visibility requirements of the ancient sources, while the previous identifications of the battlefield in the vicinity of Karamleis and Qaraqosh (Stein 1942; Sushko 1936; Zouboulakis 2015, 2016) feature poor results in terms of expected visibility.

This paper presents a selected aspect of research conducted within the Gaugamela Project, which seeks to finally identify the location of one of the most important ancient battles: the Battle of Gaugamela (331 BCE). The aim of this study was to discover material remains of the Macedonian military camp on the Navkur Plain in Kurdish Iraq. For this purpose, three very high resolution satellite (VHRS) datasets from Pleiades and WorldView-2 were acquired and subjected to multi-variant image processing (development of different color composites, integration of multispectral and panchromatic images, use of principle component analysis transformation, use of vegetation indices). Documentation of photointerpretation was carried out through the vectorization of features/areas. Due to the character of the sought-after artifacts (remnants of a large enclosure), features were categorized into two types: linear features and areal features. As a result, 19 linear features and 2 areal features were found in the study area of the Mahad hills. However, only a few features fulfilled the expected geometric criteria (layout and size) and were subjected to field groundtruthing, which ended in negative results. It is concluded that no traces have been found that could be interpreted as remnants of an earthen enclosure capable of accommodating around 47,000 soldiers. Further research perspectives are also suggested.

Contemporary studies of geographical space, including archaeological research, incorporate multiple spatial digital data. Such data provide an opportunity to extend research to large areas, and to objectify studies on the basis of quantitative data thus obtained and gaining access to the hard-to-reach study area. Examples of such data are satellite images at various spatial resolutions and in a wide spectrum of electromagnetic radiation (visible, infrared, and microwave). The authors made an attempt to use satellite images to analyze the areas of probable location of the Battle of Gaugamela (the Navkur Plain and the Karamleis Plain in Iraq). The photointerpretation was performed, enhanced by the multivariate processing of the multispectral image. The aim of the work was indicating the most likely places where the camp and the battle were located based on the visual interpretation of an array of satellite data. The adopted methodology of precise allocation of interpretative values to remote sensing materials for every detected artifact provided an opportunity to accumulate an extensive amount of information. It also provided the basis for a synthetic analysis regarding the methods of image processing on the one hand and the dates of recording on the other. It turned out that the season in which the photos are recorded is very important—although the best data for analysis turned out to be the autumn data (38% of all recognized artifacts), the use of data from three seasons increased the total number of indicated artifacts by as much as about 50% (the so-called unique detections). In addition, advanced image processing (such as principal component analysis and decorrelation stretch) turned out to be important, as it increased the number of areal artifacts by 31% compared to the interpretation of only photos in natural (true) color composite and false color composite (with near-infrared). The conducted analyses have confirmed the usefulness of high-resolution satellite data for archaeological applications, and the detected and described anomalies visible in satellite images are excellent material for selecting sites for detailed field research.

The routing of road corridors in the project of a forest road network in the mountains, along with defining the functions of these roads, is the most important activity in the entire task. It must be preceded by a detailed inventory consisting of many factors characterising the analysed transport area, including economic, technical or environmental factors. This study presents an attempt at the semi-automatic routing of road corridors with the use of GIS technology. The original multi-stage methodology proposed by the present authors is based on raster analyses of the Digital Terrain Model at a resolution of 1 m. The initial stage consists in a preliminary outline of the road network being designed, which is performed by an experienced designer on a contour map. Due to the complicated terrain in mountain areas, the pre-determined road sections are divided into sections for which a generalised direction in the form of a straight line can be indicated. On the basis of the azimuths of these particular direction lines, “targeted” terrain inclinations are calculated for each section separately, which help in decisions concerning the location of the planned road sections. The results obtained indicate the key role of the initial concept of the designed road network, as outlined by the expert, and based on the expert’s professional experience. The problem lies in strongly varied terrain, which forces the designer to use short sections of corridors. This, in turn, significantly increases the amount of work. Good effects of road routing automation can be obtained in areas where land relief is complicated. The designed locations of road sections must be finally verified in terms of terrain inclination along the designated lines, which is essential for the possibility of subsequent detailed design, construction and use of road sections.