Authors

Kiran Kumar B

Master of Computer Applications, RV College of Engineering, Bengaluru - 560 059, Karnataka, India

Manjunatha S

Master of Computer Applications, RV College of Engineering, Bengaluru - 560 059, Karnataka, India

Abstract

The deterioration of roads, marked by problems such as potholes, cracks, and subgrade settlement, is escalating due to fluctuations in weather patterns, including heavy rainfall and seasonal temperature changes linked to global warming, as well as the reliance on traditional road construction methods. This scenario presents considerable challenges for India. Conventional strategies for managing road surfaces are proving increasingly inadequate in the face of rising traffic volume and deteriorating roadway conditions, which worsen on a daily basis. In 2017, India experienced a significant 50% rise in traffic accidents associated with pothole-related issues compared to earlier years. While many studies have focused on pothole repair, they frequently fall short in terms of practical implementation. Consequently, it is crucial to establish efficient methods for the swift and accurate detection of potholes at a low cost.

Keywords

Potholes Geo-tagging QGIS Machine Learning Geospatial Techniques Artificial intelligence Cracks Deterioration of roads

Citation of this Article

Kiran Kumar B, & Manjunatha S. (2025). Identification and Assessment of Potholes through Geospatial Techniques and Machine Learning. Current Journal of Engineering and Science Research. 2(2), 1-5. Article DOI: https://doi.org/10.47001/CJESR/2025.202001

Licence Copyright (c) 2026 Current Journal of Engineering and Science Research. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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