Environment
Elena Drobinina; Marina Kitaeva; Artem Mizev; Elizaveta Romanova
Abstract
The study presents an integrated approach to karst susceptibility assessment using Geographic Information Systems (GIS) and Remote Sensing (RS) data for sinkhole mapping and spatial analysis. The approach enables rapid and reliable karst susceptibility assessment in areas where linear infrastructure ...
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The study presents an integrated approach to karst susceptibility assessment using Geographic Information Systems (GIS) and Remote Sensing (RS) data for sinkhole mapping and spatial analysis. The approach enables rapid and reliable karst susceptibility assessment in areas where linear infrastructure has been designed within the Pivovarovo karst area (Vladimir Region, Russia). The research highlights the advantages of automated zoning along the construction route based on both sinkhole distribution and environmental conditions. A significant methodological contribution to the assessment of karst susceptibility is the development of a custom Python-based tool for the automated morphometric analysis of sinkholes, including diameter measurement and orientation assessment. This approach provides an effective solution for karst susceptibility assessment, because it enables the rapid processing of large datasets, producing high-quality results that can support engineering design decisions.
