The application of deep learning and large model technology in the real reconstruction of Beijing Central Axis

doi:10.12267/j.issn.2096-5931.2024.12.012

Abstract

Abstract:

This paper refers to satellite landscape data and urban planning data published by the government, and uses large model technology to expand the sample size. Then, the sample data are used for multi-model joint learning training. Through model training, the high-definition satellite base map and multi-spectral data of Beijing urban area are measured and restored with high precision, including the urban building base, vegetation, and road. The large language model technology is used to program three-dimensional modeling, and finally automatically generates in the Unreal Engine. The process reproduces landscape assets within a 1 000 square kilometer area of central Beijing. This method not only improves the efficiency and accuracy of urban modeling, but also provides a new research tool and perspective for urban planning, historical preservation and other fields.

Key words: deep learning, large model, reconstruction

CLC Number:

TP391.9

WANG Tianzhou, ZHANG Hua, LIU Qishen, HUANG Chao. The application of deep learning and large model technology in the real reconstruction of Beijing Central Axis[J]. Information and Communications Technology and Policy, 2024, 50(12): 82-88.

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http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2024.12.012

http://ictp.caict.ac.cn/EN/Y2024/V50/I12/82

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The application of deep learning and large model technology in the real reconstruction of Beijing Central Axis

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