Extraction and calculation of Road area from Satellite Images using Machine learning: A Case study in Khanbogd, Tsogttsetsii, and Sevrei soums of Umnugovi Aimag (province)
Keywords:
Road-induced degradation, Remote sensing, Infrastructure developmentAbstract
Mongolia’s arid landscapes and sparse population pose unique transportation challenges, particularly in Umnugovi aimag (province), where over 90% of roads remain unpaved. Informal dirt tracks, which were made because there weren't enough paved roads, have damaged the environment by causing soil erosion, loss of vegetation, and habitat fragmentation. This study applies advanced remote sensing techniques, including machine learning classification and GIS-based analysis, to quantify the spatial extent of paved and unpaved roads in the desert steppe of Khanbogd, Tsogttsetsii, and Sevrei soums (sub-district). High-resolution Sentinel-2 imagery (2010–2025) was processed to extract road networks and calculate their surface areas. Results indicate rapid road expansion, with Tsogttsetsii’s road area increasing from 324 ha in 2015 to 1,105 ha in 2024, at an average annual growth of 86.35 ha. Road expansion in Sevrei soum, a non-mining control area, was higher due to wider pastoral corridors. Findings highlight the importance of integrated infrastructure planning that mitigates environmental impacts through dust control, wildlife corridors, and pastureland conservation. This study provides a methodological framework for assessing road-induced landscape transformation in arid regions, contributing to sustainable transportation and land-use policy development.
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