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

2025/06/14 by Denteh, Eugene Kofi Okrah, Danyo, Andrews, Asamoah, Joshua Kofi +2
Engineering · Environmental Science · Social Sciences · #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Impact of Light on Environment and Health

paper · pdf · doi:10.48550/arxiv.2506.12456

openalex publication_date 2025/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Visual Dominance and Emerging Multimodal Approaches in Distracted Driving Detection: A Review of Machine Learning Techniques
• An Improved ResNet50 Model for Predicting Pavement Condition Index (PCI) Directly from Pavement Images
• Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformations
• Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images
• Advancing Pavement Distress Detection in Developing Countries: A Novel Deep Learning Approach with Locally-Collected Datasets
• PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI Estimation
• The impact of transportation and information infrastructure on urban productivity: Evidence from 256 cities in China
• The Multi-Temporal Urban Development SpaceNet Dataset
• Examining the impacts of residential self-selection on travel behavior: A focus on methodologies

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