Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e.g., single cities or regions). Yet these AI models tend to meet the performance bottleneck in...

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Vydané v:	Remote sensing of environment Ročník 299; s. 113856
Hlavní autori:	Hong, Danfeng, Zhang, Bing, Li, Hao, Li, Yuxuan, Yao, Jing, Li, Chenyu, Werner, Martin, Chanussot, Jocelyn, Zipf, Alexander, Zhu, Xiao Xiang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 15.12.2023
Predmet:	artificial intelligence China class Cross-city data collection Deep learning Dice loss domain Domain adaptation environment Germany High-resolution network Land cover Multimodal benchmark datasets Remote sensing Segmentation topology China Germany Deep learning High-resolution network Segmentation Multimodal benchmark datasets Domain adaptation Dice loss Land cover Remote sensing Cross-city
ISSN:	0034-4257, 1879-0704
On-line prístup:	Získať plný text
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