Volunteered Geographic Information (VGI) maps (e.g., Open Street Map, Mapillary) are widely used for urban analytics, disaster response, and environmental applications. However, its quality is uneven: while highly accurate in developed urban areas, VGI often suffers from incompleteness and positional errors in rural regions and the Global South due to limited contributions and expert effort in calibration. This spatial bias can introduce uncertainty into downstream analysis, particularly in data-sparse regions.  This project, undertaken by Team 1 during I-GUIDE's 2026 Summer School, aimed to develop a systematic approach to evaluate and calibrate VGI maps using multimodal remote sensing data (e.g., Landsat satellite remote sensing imagery and LiDAR remote sensing data). The key research questions were 1) How does VGI accuracy and completeness vary across geographic and socioeconomic contexts? 2) Can remote sensing data detect discrepancies in VGI data such as roads and buildings? 3) How can we develop scalable (AI) approaches to automatically improve VGI quality in data-sparse regions?   During the Summer School, the team collaborated to design evaluation metrics and workflows, extract features from imagery and LiDAR, and develop models for detection and calibration. They will describe their scalable workflow for benchmarking and enhancing VGI data quality across diverse geographic contexts.