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<p>Global-scale and long-term projections of future urban 3D expansion are essential for understanding the environmental effects of future urbanization. Here, we develop a multi-task learning and end-to-end cellular automaton model for urban 3D projection (named MECA-3D). Within the MECA-3D model, the top-down component determines urban built-up volume demand based on panel data regression model and socioeconomic factors, while the bottom-up component estimates urban land suitability and built-up height by a multi-task residual neural network model. Using the MECA-3D model, for the first time, we present the global projections of future urban 3D expansion dataset (named FU3D) from 2020 to 2100 at a 1 km resolution under the five Shared Socioeconomic Pathways (SSPs). Our projections show that by 2100, global urban built-up volume will increase to about 184%–409% under the five SSPs, with the largest 3D expansion (exceeding 4000 km<sup>3</sup>) projected under the fossil-fuelled development scenario (SSP5). The validation procedures indicate that the FU3D dataset exhibits sufficient accuracy, long-term reliability and reasonable uncertainty. In general, the FU3D dataset overcomes the limitations of 2D projections, provides valuable 3D morphological information for future sustainable cities and can serve as a valuable input in relevant fields.</p>

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FU3D: the first global projections of future urban three-dimensional (3D) expansion for the 21st century under shared socioeconomic pathways

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月25日

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  • Qikang Zhao,
  • Qingyan Meng,
  • Liang Gao,
  • Mingming Zhu

摘要

Global-scale and long-term projections of future urban 3D expansion are essential for understanding the environmental effects of future urbanization. Here, we develop a multi-task learning and end-to-end cellular automaton model for urban 3D projection (named MECA-3D). Within the MECA-3D model, the top-down component determines urban built-up volume demand based on panel data regression model and socioeconomic factors, while the bottom-up component estimates urban land suitability and built-up height by a multi-task residual neural network model. Using the MECA-3D model, for the first time, we present the global projections of future urban 3D expansion dataset (named FU3D) from 2020 to 2100 at a 1 km resolution under the five Shared Socioeconomic Pathways (SSPs). Our projections show that by 2100, global urban built-up volume will increase to about 184%–409% under the five SSPs, with the largest 3D expansion (exceeding 4000 km3) projected under the fossil-fuelled development scenario (SSP5). The validation procedures indicate that the FU3D dataset exhibits sufficient accuracy, long-term reliability and reasonable uncertainty. In general, the FU3D dataset overcomes the limitations of 2D projections, provides valuable 3D morphological information for future sustainable cities and can serve as a valuable input in relevant fields.