Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery

• URL: http://arxiv.org/abs/2506.12456v2
• Date: Fri, 20 Jun 2025 01:43:44 GMT
• Title: Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery
• Authors: Eugene Kofi Okrah Denteh, Andrews Danyo, Joshua Kofi Asamoah, Blessing Agyei Kyem, Armstrong Aboah,
• Abstract summary: This study presents a novel demographics informed deep learning framework designed to forecast urban spatial transformations.<n>The proposed model employs an encoder-decoder architecture with temporal gated residual connections, integrating satellite imagery and demographic data.<n>The study also contributes a comprehensive multimodal dataset pairing satellite imagery sequences with corresponding demographic and travel behavior attributes.
• Score: 3.378738346115004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents a novel demographics informed deep learning framework designed to forecast urban spatial transformations by jointly modeling geographic satellite imagery, socio-demographics, and travel behavior dynamics. The proposed model employs an encoder-decoder architecture with temporal gated residual connections, integrating satellite imagery and demographic data to accurately forecast future spatial transformations. The study also introduces a demographics prediction component which ensures that predicted satellite imagery are consistent with demographic features, significantly enhancing physiological realism and socioeconomic accuracy. The framework is enhanced by a proposed multi-objective loss function complemented by a semantic loss function that balances visual realism with temporal coherence. The experimental results from this study demonstrate the superior performance of the proposed model compared to state-of-the-art models, achieving higher structural similarity (SSIM: 0.8342) and significantly improved demographic consistency (Demo-loss: 0.14 versus 0.95 and 0.96 for baseline models). Additionally, the study validates co-evolutionary theories of urban development, demonstrating quantifiable bidirectional influences between built environment characteristics and population patterns. The study also contributes a comprehensive multimodal dataset pairing satellite imagery sequences (2012-2023) with corresponding demographic and travel behavior attributes, addressing existing gaps in urban and transportation planning resources by explicitly connecting physical landscape evolution with socio-demographic patterns.

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