Integrating User-Generated POI Data and Satellite Imagery for Enhanced Urban Land Use Classification: A Topic Modeling Approach

The Integration of extensive user-generated data, for example, Points of Interest (POI), along with novel advances in machine learning that are applied in the analysis of satellite imagery, brings about a revolutionary approach toward urban land use classification bridging significant deficiencies in understanding the dynamics of urban life. POI data captures much of the various social and economic activities, whereas satellite imagery expresses spatial and physical context, but neither together captures the complexities of the urban setting. This paper proposes a new approach in which all these types of data are fused into a single text-based format through transformation of structured POI datasets and satellite images, thus enabling topic modeling for the classification and mapping of land use. The integration of these data formats addresses a variety of challenges, such as spatial heterogeneity, non-linear relationships, and extrapolation artifacts, to allow for a scalable and precise solution in urban data analysis. This methodology is in line with the challenges and opportunities that large-scale mapping presents, whereby machine learning algorithms can yield robust and spatially explicit predictions by connecting diverse datasets. This approach improves the accuracy and resolution of urban land use maps and thus offers insights into the interplay of human activities and physical spaces, which are very important for planning and policy-making in urban regions. The results demonstrate significant promise as the combination of POI data and satellite imagery enhances the understanding of complex urban systems and supports sustainable development with practical tools for designing more adaptable and resilient urban environments. Moreover, this contribution is aligned with the session focus on comprehensive mapping techniques addressing some of the biggest challenges in upscaling data; building representative measurement models; handling uncertainty; and ensuring robust validation. In so doing, it helps to further develop better policies for urban land use, besides contributing to overall goals of mapping environmental variables in diverse and dynamic environments. This methodology not only enhances the techniques of urban planning but also sets a benchmark for the incorporation of intricate datasets to improve comprehension and management of the difficulties faced by contemporary urban areas, thereby promoting a more profound relationship between human actions and the physical contexts in which they take place.