Enhancing spatial feature development from imagery using computer vision aided by GIS

Abstract

This dissertation addresses the challenge of detecting and validating spatial features of differing geometric complexity. It focuses on buildings and swimming pools extracted from high-resolution satellite imagery and orthophotos for GIS applications. Conventional object detection workflows often lack mechanisms to reconcile computer vision outputs with authoritative spatial data. This limitation reduces their reliability for urban and environmental planning. The research is motivated by the need to integrate deep learning–based detection with GIS-based spatial validation to improve confidence and interpretability. The methodology uses the YOLOv11 object detection framework trained on approximately 1,000 manually annotated images. Both single-class and multi-class configurations are evaluated. Due to limitations in ArcGIS Pro’s native deep learning toolbox, inference is performed externally. Detection outputs are then reintroduced into the GIS environment. A novel GIS–CV integration pipeline is implemented using the arcpy library. Post-inference spatial refinement is applied using Intersection over Union (IoU) and Dice coefficient analysis. Authoritative planning basemap polygons are used to enable confidence reweighting. After spatial validation, the single-class swimming pool model achieved a mAP@0.5 of 0.78. It obtained a precision of 0.85 and a recall of 0.75 after 122 epochs. The runtime for this model was 0.289 hours. The building detection model achieved a mAP@0.5 of 0.45 after 100 epochs. It recorded a precision of 0.698 and a recall of 0.626, with a runtime of 0.254 hours. Pool mAP@0.5 increased from 0.74 to 0.78, while building mAP@0.5 increased from 0.439 to 0.45. A multi-class model detecting buildings, pools, and vegetation achieved an overall mAP@0.5 of 0.475. This model recorded a precision of 0.54 and a recall of 0.489. This main contribution is a custom GIS–CV pipeline with a novel post-inference validation framework. This approach enhances detection reliability and supports scalable integration of computer vision outputs into operational GIS workflows.