Unsupervised Urban Land Use Mapping with Street View Contrastive Clustering and a Geographical Prior
Journal:
arXiv
Published Date:
Apr 24, 2025
Abstract
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.
