Spatiotemporal data fusion and deep learning for remote sensing-based sustainable urban planning

Urban settlements exhibit a high degree of dynamism and complexity, since they are subject to fast changes in spatial organization, temporal patterns, and socioeconomic dynamics. The achievement of sustainable development in cities requires the implementation of planning strategies that are informed by empirical knowledge and guided by a comprehensive comprehension of the complex urban changes taking place. This study aims to use spatiotemporal data fusion and deep learning methods to integrate various urban data streams and effectively predict intricate urban development patterns. The goal is to utilize these models to provide valuable insights for sustainable planning strategies. Convolutional fusion architectures are used to integrate high-resolution satellite images with population census information, transportation networks, land use maps, and housing statistics. The use of a long short-term memory neural network facilitates the comprehensive capture of complex spatial and temporal relationships. The methodology is used to three rapidly expanding cities to model detailed future urbanization scenarios and assess the sustainability implications of different growth strategies in the next several decades. The findings demonstrate that the fusion framework has a high level of reliability in accurately forecasting emerging development hotspots, urban density patterns, and infrastructure challenges, with an accuracy rate of 80%. The findings from simulations indicate that if peripheral growth is not regulated, it would place significant pressure on water supplies and exacerbate the phenomenon of urban heat islands. However, implementing certain policies that promote densification may help alleviate these problems and facilitate the adoption of sustainable development pathways. This paper presents a scalable methodology that use data-driven techniques to anticipate and guide the complex dynamics of rapidly evolving urban areas towards sustainable development.