Spatial Feature Transformation
Spatial Feature Transformation is a methodological construct in geospatial feature engineering that formally defines the process of converting raw spatial data into structurally enhanced representations through geometric, topological, or attribute-based operations to improve spatial pattern discernibility and analytical utility.
160
Publications
16.2K
Citations
602
Authors
279
Institutions
Publications per year
2017–2026
112
160
Deformable Convolutional Networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong et al. · 2017 · 6.9K citations
Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform
Xintao Wang, Ke Yu, Chao Dong et al. · 2018 · 1.1K citations
Improving Convolutional Networks With Self-Calibrated Convolutions
Jiangjiang Liu, Qibin Hou, Ming‐Ming Cheng et al. · 2020 · 487 citations
Deformable Convolutional Networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong et al. · arXiv (Cornell University) · 2017 · 437 citations · Full text
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279

Peking University
Beijing, China
5 h-index
17 publications
1.5K citations
Dalian University of Technology
Dalian, China
4 h-index
9 publications
538 citations
Wuhan University
Wuhan, China
4 h-index
14 publications
346 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
4 h-index
8 publications
471 citations

Sun Yat-sen University
Guangzhou, China
3 h-index
4 publications
97 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
12 publications
392 citations
Nanyang Technological University
Singapore, Singapore
3 h-index
7 publications
172 citations
University of Chinese Academy of Sciences
Beijing, China
3 h-index
11 publications
317 citations
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9 publications · 186 citations
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Repository · 7 publications · 993 citations
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6 publications · 861 citations
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4 publications · 257 citations
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Conference · 4 publications · 287 citations
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