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

arXiv: https://arxiv.org/abs/2506.12456 · HTML: https://mlsanity.com/paper/2506.12456/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 14 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [The Multi-Temporal Urban Development SpaceNet Dataset](https://mlsanity.com/paper/2102.04420/) (2021)
- [Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?](https://mlsanity.com/paper/2303.04204/) (2023)
- [PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI Estimation](https://mlsanity.com/paper/2408.04110/) (2024)
- [Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images](https://mlsanity.com/paper/2501.14413/) (2025)
- [Advancing Pavement Distress Detection in Developing Countries: A Novel Deep Learning Approach with Locally-Collected Datasets](https://mlsanity.com/paper/2408.05649/) (2024)
- [Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformations](https://mlsanity.com/paper/2503.21158/) (2025)
- [Visual Dominance and Emerging Multimodal Approaches in Distracted Driving Detection: A Review of Machine Learning Techniques](https://mlsanity.com/paper/2505.01973/) (2025)
- [An Improved ResNet50 Model for Predicting Pavement Condition Index (PCI) Directly from Pavement Images](https://mlsanity.com/paper/2504.18490/) (2025)

## Built on by (most cited first, 1 of 1)

- [Hybrid Congestion Classification Framework Using Flow-Guided Attention and Empirical Mode Decomposition](https://mlsanity.com/paper/2605.04752/) (2026)

## Abstract (arXiv)

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.