Machine Learning-Based Detection of White Lands in Riyadh from Satellite Data

In response to Saudi Arabia’s amended White Land Fees Law, which imposes charges of up to 10% of land value on undeveloped urban plots, this study presents TerraVision, an intelligent framework for large-scale White Land detection and urban land monitoring using high-resolution satellite imagery and deep learning. The proposed framework aims to support sustainable urban development by enabling municipalities and planners to identify underutilized urban land, improve land-use efficiency, and support evidence-based planning decisions. Satellite imagery was acquired through the Esri ArcGIS platform at a spatial resolution ranging from 0.31 to 0.34 m per pixel. The Riyadh study area was divided into 1317 geographic tiles, of which 80 tiles covering approximately 180 km² were manually annotated to construct the training and evaluation dataset. Ten segmentation models representing four architectural families were evaluated, including encoder–decoder networks, transformer-based architectures, YOLO segmentation models, and the zero-shot Segment Anything Model 3 (SAM3). Six fine-tuned semantic segmentation models achieved Intersection over Union (IoU) scores between 0.94 and 0.96 on the held-out test set, with SegFormer achieving the highest performance at an IoU of 0.9563. A post-inference geoprocessing pipeline was developed to reconstruct city-scale prediction maps, estimate neighborhood-level White Land availability, and export results into GIS- and web-compatible formats. The framework was further integrated into a bilingual (Arabic and English) decision-support dashboard that enables visualization and spatial analysis of vacant land distribution. The results demonstrate that semantic segmentation models provide an accurate solution for monitoring undeveloped urban land that scales to city-wide inference across Riyadh, and can support preliminary screening for strategic urban planning and sustainable city development initiatives in Riyadh.