[1]GUO Maozu,WANG Sijia,WANG Pengyue,et al.Small sample block quality evaluation based on satellite images[J].CAAI Transactions on Intelligent Systems,2022,17(6):1254-1262.[doi:10.11992/tis.202111049]
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Small sample block quality evaluation based on satellite images

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 6 Page number: 1254-1262 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2022-11-05
Title:
Small sample block quality evaluation based on satellite images
Author(s):
GUO Maozu1; 2;  WANG Sijia1; 2;  WANG Pengyue2; 3;  ZHAO Lingling4
1. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
2. Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
3. School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
4. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Keywords:
block quality assessment; satellite map; few-shot learning; adaptive subspace; depth neural network; singular value decomposition; unbalanced dataset; under-sampling
CLC:
TP391.41;TP18
DOI:
10.11992/tis.202111049
Abstract:
Quantitative urban block quality evaluation is an important foundation for block design and planning, and image data is an important dimension of the block quality evaluation model. Currently, there are some problems in this field of research, such as the high cost of block quality labeling. This paper improves the small sample learning method based on subspace, performs singular decomposition on the satellite image features of the block to generate class subspace, and inherits the subspace parameters of the training set into the block quality evaluation model. The experimental results show that this method is about 30% more accurate and 15% more consistent than the traditional small sample learning method.
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