Similarity based city data transfer framework in urban digitization

Abstract Cross-city transfer learning aims to apply the knowledge and model from data-rich cities to data-poor cities to solve the cold start problem. Existing methods directly transfer the model constructed from developed cities to underdeveloped cities without considering the similarity between th...

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主要な著者:	Haoxiang Wang, Xiaoping Che, Enyao Chang, Chenxin Qu, Ganghua Zhang, Zihan Zhou, Zhenlin Wei, Gengyu Lyu, Pengfei Li
フォーマット:	Artigo
言語:	Inglês
出版事項:	Nature Portfolio 2025-03-01
シリーズ:	Scientific Reports
主題:	Urban Digitization Transfer Learning City Similarity
オンライン･アクセス:	https://doi.org/10.1038/s41598-025-94987-y
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