<![CDATA[Road damage, particularly potholes, in Genuk District, Semarang City, is dynamic due to tidal flooding and heavy logistics vehicle loads. Conventional monitoring based on manual surveys and citizen reports is relatively slow and may delay repair responses. This study develops a real-time pothole detection and mapping system by integrating the YOLOv11 algorithm with a Web-GIS interface. The system uses a distributed architecture in which a Python-based local inference layer processes video streams and synchronizes detected potholes with GPS coordinates. Image evidence is uploaded to Cloudinary, while spatial coordinates are stored in a PostGIS-configured PostgreSQL database. The resulting spatial data are visualized through an interactive map developed using TypeScript and Leaflet.js on Vercel serverless infrastructure. The YOLOv11 model was trained for 300 epochs using an NVIDIA Tesla T4 GPU. Evaluation achieved 97.4% precision, 90.4% recall, and 95.1% mAP@50, with an inference latency of 9.3 milliseconds per frame, equivalent to approximately 107 FPS. Functional testing confirmed reliable data transmission and automatic deduplication using the ST_DWithin spatial query. The system provides accurate, responsive, and reliable support for mapping potholes and accelerating road infrastructure repair processes.]]>
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