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1.中山大学地理科学与规划学院,广东 广州,510006
2.广东国地科技股份有限公司,广东 广州,510650
Received:01 September 2026,
Published:05 November 2026
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高金顶,崔学森,刘耿,等.城市更新的片区‑地块空间方案生成与优化方法[J].武汉大学学报(信息科学版),2026,51(11):2485-2501.
GAO Jinding,CUI Xuesen,LIU Geng,et al.Generation and Optimization of District‑ and Plot‑Level Spatial Schemes for Urban Renewal[J].Geomatics and Information Science of Wuhan University,2026,51(11):2485-2501.
高金顶,崔学森,刘耿,等.城市更新的片区‑地块空间方案生成与优化方法[J].武汉大学学报(信息科学版),2026,51(11):2485-2501. DOI:10.13203/j.whugis20260324
GAO Jinding,CUI Xuesen,LIU Geng,et al.Generation and Optimization of District‑ and Plot‑Level Spatial Schemes for Urban Renewal[J].Geomatics and Information Science of Wuhan University,2026,51(11):2485-2501. DOI:10.13203/j.whugis20260324
城市更新需要在共同场地条件下开展多方案推演,并衔接片区功能组织与地块建筑布局。针对现有生成式城市设计聚焦单一对象、难以支持多情景生成与跨层级调整的问题,提出片区‑地块空间方案生成与调整方法。该方法以共同规划情景统筹用地约束和建设控制参数,贯通分层道路生成、地块构建、多情景用地配置与细分、逐地块建筑布局生成及对象级调整。实验结果表明,所提方法可在同一道路-地块骨架上形成差异明确的多情景用地方案,住宅布局生成取得最低Fréchet Inception距离,4项建设控制参数误差均低于对比模型,并将建筑重叠面积由59.4 m²降至0 m²。广钢新城案例形成4类更新方案,实现道路、地块、用地和建筑布局的一体化生成。该方法可将更新方案组织为可比较、可测算和可局部修正的对象化状态,为城市更新前期方案推演与迭代调整提供支撑。
Objectives
2
Urban renewal requires multiple alternative schemes under common site conditions and coordinated spatial design at district and plot levels. Existing generative urban-design studies commonly focus on isolated objects such as roads
land use
or buildings
and do not jointly support multi-scenario generation
district-to-plot linkage
and scheme adjustment.
Methods
2
We propose a method for generating and adjusting district- and plot-level spatial schemes. A shared planning scenario coordinates district-level land-use constraints and development m²control parameters for different plot types. Explicit road
plot
land-use
and building objects connect hierarchical road generation
plot construction
multi-scenario land-use allocation and subdivision
plot-by-plot building-layout generation
object-based representation
and local adjustment.
Results
2
Multi-scale Transformer-vector‑quantized generative adversarial network road generation generates large-area road networks hierarchically at primary and secondary levels and increases length-weighted metric integration in one local regeneration area from 3.20 to 3.94. Deep reinforcement learning and generative adversarial network-based land-use selection respondes to functional orientations and specifies facility counts on the same road-plot skeleton
producing multi-scenario schemes with distinct land-use compositions and spatial distributions. Multi-condition GauGAN achieves the lowest Fréchet Inception distance in residential-layout baseline comparisons. The root mean square error (RMSE) values for the four development-control parameters are lower than those of comparable baselines
and modifying a single parameter produces targeted adjustments to building layouts. The Fruchterman‑Reingold force-directed algorithm reduces the building-overlap area from 59.4 m² to 0 m² through object-level local correction. In Guanggang New Town
four renewal schemes are generated under common site conditions
and each scheme integrates road‑plot‑land-use organization
plot-by-plot building layouts
and the assembled district-scale built form.
Conclusions
2
The proposed method organizes urban-renewal schemes as object-based states that are comparable
measurable
adjustable
and locally correctable
supporting early-stage scenario exploration
development-intensity checking
and iterative refinement.
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