Unsupervised Urban Land Use Mapping with Street View Contrastive Clustering and a Geographical Prior
It addresses the challenge of precise land use classification in complex urban environments for urban planners, offering a scalable and customizable solution, though it is incremental as it builds on existing unsupervised and street view-based methods.
This study tackled the problem of urban land use mapping by developing an unsupervised contrastive clustering model with a geographical prior for street view images, enabling the generation of land use maps from geotagged datasets in two cities without requiring labeled data.
Urban land use classification and mapping are critical for urban planning, resource management, and environmental monitoring. Existing remote sensing techniques often lack precision in complex urban environments due to the absence of ground-level details. Unlike aerial perspectives, street view images provide a ground-level view that captures more human and social activities relevant to land use in complex urban scenes. Existing street view-based methods primarily rely on supervised classification, which is challenged by the scarcity of high-quality labeled data and the difficulty of generalizing across diverse urban landscapes. This study introduces an unsupervised contrastive clustering model for street view images with a built-in geographical prior, to enhance clustering performance. When combined with a simple visual assignment of the clusters, our approach offers a flexible and customizable solution to land use mapping, tailored to the specific needs of urban planners. We experimentally show that our method can generate land use maps from geotagged street view image datasets of two cities. As our methodology relies on the universal spatial coherence of geospatial data ("Tobler's law"), it can be adapted to various settings where street view images are available, to enable scalable, unsupervised land use mapping and updating. The code will be available at https://github.com/lin102/CCGP.