A unified deep learning framework integrating OpenStreetMap for multi-domain urban planning tasks.

Urban planning increasingly requires multi-source geospatial intelligence to address the complexity of modern cities. This study introduces a unified deep learning framework integrating OpenStreetMap (OSM) data with multispectral satellite imagery and demographic-environmental datasets. The framework employs task-specific architectures, including Convolutional Neural Networks (CNNs) for land-use classification, U-Net segmentation for building footprint extraction, Long Short-Term Memory (LSTM) networks for traffic flow prediction, and a hybrid CNN-RNN model for air-quality forecasting. The land-use model achieved 91.6% accuracy, the U-Net building footprint extractor reached 94.0% accuracy, the LSTM traffic model obtained an RMSE of 3.6 vehicles per hour, and the hybrid air-quality model achieved an RMSE of 2.3 µg/m³. These metrics reflect performance on OSM-aligned datasets specifically curated for Krasnodar. The novelty of this work lies in establishing a multi-task, multi-source analytical workflow that unifies OSM, satellite, and temporal environmental data within a coherent deep learning system. The resulting framework provides urban planners with a scalable and low-cost approach for generating high-resolution urban intelligence to support sustainable city management.