Crowdsourced Geospatial Intelligence: Constructing 3D Urban Maps with Satellitic Radiance Fields

Abstract

In urban planning and research, 3D city maps are crucial for activities such as cellular network design, urban development, and climate research. Traditionally, creating these models has involved costly techniques like manual 3D mapping, interpretation of satellite or aerial images, or the use of sophisticated depth-sensing equipment. In this work, we propose a novel approach to develop 3D urban maps by examining the influence of urban structures on satellite signals, using GPS records crowdsourced from hundreds of smartphones during everyday user movements. We introduce the concept of satellitic radiance fields (SaRF), a novel neural scene representation technique designed to capture the distribution of GPS signals in urban settings. SaRF employs a sparse voxel octree framework to depict voxel-centric implicit fields, capturing physical properties like the density of each voxel. This model is progressively refined using a differentiable ray-marching process, ultimately leading to the reconstruction of 3D urban maps. Our thorough experimental evaluation, which incorporates approximately 27.4 million GPS records, reveals an average reconstruction accuracy of 83.1% in six varied urban scenes.