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

• Published in: Remote sensing of environment Vol. 299; p. 113856
• Main Authors: Hong, Danfeng, Zhang, Bing, Li, Hao, Li, Yuxuan, Yao, Jing, Li, Chenyu, Werner, Martin, Chanussot, Jocelyn, Zipf, Alexander, Zhu, Xiao Xiang
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.12.2023
• Subjects: 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

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...