Scholarlink

<p>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&#xa0;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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

查看全文
  • YanHua Chen,
  • Waleed Saeed Afandi,
  • Dmitry Gura,
  • Yury Kosenok

摘要

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.