Authors

Manjunatha S

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

Nanjundeshwara Rao

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

Abstract

Road infrastructure degradation—manifested through potholes, surface cracking, and subgrade settlement—has intensified due to climatic variability, including heavy precipitation events and seasonal temperature fluctuations associated with global warming, as well as continued reliance on conventional pavement construction practices. This growing deterioration poses significant challenges for India’s transportation network. Traditional road maintenance and surface management strategies are increasingly inadequate in addressing the combined effects of escalating traffic density and progressive pavement distress, both of which accelerate structural wear and surface failure. Notably, national accident statistics reported a substantial increase in traffic incidents linked to pothole-related hazards in 2017, highlighting the critical safety implications of poor road conditions. Although prior research has explored various pothole repair methodologies, many proposed solutions lack scalability, cost-effectiveness, or real-world deployment feasibility. Therefore, there is an urgent need to develop efficient, accurate, and economically viable techniques for rapid pothole detection and assessment to support proactive maintenance and enhance roadway safety.

Keywords

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

Citation of this Article

Manjunatha S, & Nanjundeshwara Rao. (2025). Spatial Analytics and Machine Learning Techniques for Road Surface Damage Assessment. Current Journal of Engineering and Science Research. 2(8), 13-17. Article DOI: https://doi.org/10.47001/CJESR/2025.208003

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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