A Strategy for Implementing Automated Surface-Level Pipeline Monitoring Systems Based on Machine Vision for Geohazard Assessment and Risk Management: A Case Study in Almaty

Seismically active regions such as Almaty pose an increasing threat to urban gas pipeline infrastructure due to hazards including earthquakes, landslides, and erosion. To address these challenges, we propose an integrated machine vision framework combining YOLOv11 with Roboflow 3.0, embedded within a Geographic Information System (GIS) environment to enable real-time geohazard monitoring and risk assessment. The methodology leverages deep learning for image-based defect detection, complemented by GIS-driven geostatistical analysis for hazard prediction and spatial risk modeling. A pilot implementation in Almaty achieved 95% defect detection accuracy, 30% faster response times, and significant improvements in maintenance planning efficiency. These findings highlight the system’s scalability for deployment in other geohazard-prone regions and its potential integration into national infrastructure resilience and disaster mitigation strategies.