Techniques for automatically generating designs having characteristic topologies for urban design projects
US-2019147120-A1 · May 16, 2019 · US
US12505265B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12505265-B2 |
| Application number | US-202117611740-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jan 6, 2021 |
| Priority date | Dec 18, 2020 |
| Publication date | Dec 23, 2025 |
| Grant date | Dec 23, 2025 |
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The present invention discloses an artificial intelligence (AI)-based urban design multi-plan generation method for a regulatory plot. The method includes operation steps performed by the following modules: (1) plot space calculation sand table module; (2) regulatory characteristic parameter input module; (3) characteristic case intelligent learning module; (4) plot road intelligent generation module; (5) plot landscape intelligent generation module; (6) plot architecture intelligent generation module; and (7) outputted plan auxiliary drawing module. By means of the AI-based urban design multi-plan generation method for a regulatory plot of the present invention, the planners realize real-time, accurate, and efficient feedback adjustment to a plurality of urban design plans under regulatory conditions, resolving a plurality of problems such as a long working period of a conventional design plan.
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What is claimed is: 1 . An artificial intelligence (AI)-based urban design multi-plan generation method for a regulatory plot, the method comprising the following operation steps: (a) collecting two-dimensional vector data of surrounding architectures and roads, verifying three-dimensional spatial information via an unmanned aerial vehicle (UAV) oblique photography, and generating a 3D model by stretching architectures based on storey quantities and forming roads based on centerline and width data; (b) scanning regulatory plan maps to obtain index data comprising land properties, entrance and exits locations, a development intensity, discount rates, and setback lines; and (c) inputting regulatory parameters and spatially linking to the 3D model; (d) acquiring an urban design case database, extracting a characteristic index system, and selecting similar cases via a decision tree; (e) generating road network plans by: (i) forming a hidden grid between entrances and exits, (ii) connecting shortest paths iteratively, (iii) widening roads to comply with urban branch road specifications; and (iv) verifying compliance with regulatory codes, and (v) generating parameters for road network density, uniformity, accessibility, and connectivity, and (f) generating landscape system plans by: (i) setting a starting point in non-residential blocks, (ii) configuring a basic unit with predefined open angle range α and length range li; (iii) growing units via an L-System evolutionary algorithm until connectivity is achieved; (iv) verifying separation from vehicle roads, and (v) generating parameters for block quantity, area, and shape index; (g) generating architectural combination plans by: (i) constructing a sample database via vectorization; (ii) matching cases with ≥90% similarity to select samples; (iii) training a CVAE-GAN model to generate combinations; (iv) verifying compliance with regulatory parameters and sunshine spacing; (v) generating parameters for skyline profile volatility; and (h) merging plans, into GIS layers, generating a report, and simulating scenes via virtual reality glasses. 2 . The method of claim 1 , wherein the hidden grid in step (e)(i) has a size of 30 m×30 m.
characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD · CPC title
using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title
using virtual or augmented reality · CPC title
Data derived from aerial or satellite images · CPC title
Mixed reality (object pose determination, tracking or camera calibration for mixed reality G06T7/00) · CPC title
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