This study presents an integrated geospatial analysis of arable land in the Sic area, aiming to highlight the relationships between agricultural land use, soil units, erosion processes, and the delineation of homogeneous territories through the use of Python and GIS techniques. The research is based on the processing and integration of multiple thematic spatial datasets, including land use categories, soil types, erosion intensity, and territorial characteristics of the study area. The methodological workflow involves the use of specialized Python libraries for geospatial analysis, such as GeoPandas, Rasterio, Matplotlib, and Shapely, enabling efficient data import, management, overlay operations, and spatial interpretation of both vector and raster data. Arable land areas were extracted and spatially correlated with existing soil units, followed by the assessment of erosion patterns and the identification of homogeneous territorial units based on similarities in pedological and morphometric characteristics. The results highlight the spatial distribution of agriculturally suitable lands, areas affected by erosion, and zones with relatively uniform territorial behavior. These findings provide valuable support for agricultural management strategies, soil conservation measures, and sustainable land-use planning. Furthermore, the study demonstrates the effectiveness of integrating Python-based workflows with GIS technologies in conducting reproducible, scalable, and analytically robust spatial assessments of rural environments.

GIS, Python, arable land

geodesy engineering

Presentation: poster