Urban Zoning & Infrastructure Compliance Annotation Dataset
Designed and executed an image-based annotation project focused on training AI systems to interpret urban zoning maps and infrastructure layouts for regulatory compliance and land-use classification. Annotated 9,800 high-resolution zoning maps, satellite overlays, and municipal planning diagrams across 6 urban jurisdictions. The dataset supported AI training for automated land-use detection, development constraint identification, and infrastructure proximity analysis. Annotation tasks included: • Polygon segmentation of zoning boundaries (residential, commercial, mixed-use, industrial, environmental overlays) • Bounding box labeling of infrastructure elements (roads, utilities, public facilities, transit lines) • Pixel-level segmentation of restricted zones (floodplains, heritage districts, setback areas) • Multi-class land-use classification • Conflict-zone tagging where overlapping regulatory layers existed Developed a structured taxonomy of 42 zoning and infrastructure categories to ensure cross-jurisdiction consistency. Quality assurance measures: • 15% blind re-annotation sampling • Inter-annotator agreement validation (0.84 consistency score) • Edge-case stress testing for mixed-use and overlay zones • Standardized annotation guidelines documentation (28-page rulebook) Dataset was delivered in structured JSON format compatible with GIS and computer vision model pipelines. Post-training evaluation results (internal testing): • 29% reduction in zoning misclassification • 34% improvement in regulatory boundary detection • 21% increase in infrastructure proximity accuracy