Integration of Google Maps with the YOLO Method for Urban Greening Monitoring

The development of artificial intelligence (AI) and computer vision technology has opened up new opportunities in environmental monitoring, especially green open spaces (RTH) in urban areas. This study aims to develop an automatic tree-detection system for satellite imagery using the You Only Look Once version 8 (YOLOv8) algorithm integrated with the Google Maps API. Therefore, the novelty of this study lies in integrating the YOLOv8 model with the Google Maps JavaScript API to produce an interactive spatial visualization of detection results, enabling real-time analysis of vegetation distribution to support sustainable urban policies. The methods used include collecting a 640 × 640-pixel-resolution satellite image dataset from 21 sub-districts in Medan City, object labelling using the Roboflow platform, data augmentation to increase diversity, and model training on Google Colaboratory. The resulting YOLOv8 model achieved excellent performance, with a precision of 0.864, recall of 0.788, and an mAP@0.5 of 0.938, indicating strong object detection capabilities. The detection results were then converted to geographic coordinates and visualized using the Google Maps JavaScript API to provide interactive spatial information on tree distribution in urban areas. This study not only demonstrates the technical feasibility of integrating the YOLOv8 algorithm with the Google Maps API but also provides a methodological framework for spatial AI coupling that can be replicated across other environmental monitoring domains. In addition, the study identifies several limitations, including dependence on image resolution, limited temporal coverage, and relatively lower mAP@0.5–0.95 performance in dense vegetation, which will guide future model improvement.