Title: Recognition of critical built-up areas located on high-hill slope regions using decision tree technique

Authors: B.G. Kodge

Addresses: Department of Computer Science, School of Science, GITAM University, Hyderabad, Telangana State, India

Abstract: In mountainous places, structures are being built for residential or commercial uses without the necessary safety precautions. Every year, landslides, torrential downpours, severe snowfall, earthquakes, volcanic eruptions, and floods cause buildings to collapse. The bulk of them are found in high-hill slope areas with loose soil types, close to river flows and other sorts of water sources. Therefore, these incidents have claimed thousands of lives. This paper deals with the process of automatic identification of critical buildings (residential/commercial) located in mountainous area which are on high-hill-slope, close to river flows, having loose soil type and high variations in land elevation contours. This study uses the primary data like, built-up/residential area and water body areas which are extracted from sample land use and land cover (LULC) using image classification techniques, and another important data like slope map and land elevation contour maps which are generated from digital elevation model (DEM). In addition, the supplementary data like, river maps, soil maps and other base maps, are also collected. All the data are integrated and taken into consideration for the identification and extraction of critical residential/build-up areas using spatial data mining technique.

Keywords: critical residential area identification; LUCL; DEM; image segmentation; decision tree; spatial data mining; SDM.

DOI: 10.1504/IJDMMM.2026.151835

International Journal of Data Mining, Modelling and Management, 2026 Vol.18 No.1, pp.82 - 90

Received: 22 Nov 2023
Accepted: 25 Oct 2024
Published online: 23 Feb 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article