Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks

By Danfeng Hong, Bing Zhang, Hao Li, Yuxuan Li, Jing Yao, Chenyu Li, Martin Werner, Jocelyn Chanussote, Alexander Zipf, Xiaoxiang Zhu. Remote Sensing of Environment, 2023

511 citations recorded by the source corpus.

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 the case studies across cities or regions, due to the lack of diverse RS information and cutting-edge solutions with high generalization ability. To this end, we build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task (called C2Seg dataset), which consists of two cross-city scenes, i.e., Berlin-Augsburg (in Germany) and Beijing-Wuhan (in China). Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN for short, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion but also closing the gap derived from enormous differences of RS image representations between different cities by means of adversarial learning. In addition, the Dice loss is considered in HighDAN to alleviate the class imbalance issue caused by factors across cities. Extensive experiments conducted on the C2Seg dataset show the superiority of our HighDAN in terms of segmentation performance and generalization ability, compared to state-of-the-art competitors. The C2Seg dataset and the semantic segmentation toolbox (involving the proposed HighDAN) will be available publicly at https://github.com/danfenghong.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/556505a06b7d09fd4e8b415d56561d950a6790c2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Danfeng Hong
  2. Jocelyn Chanussot
  3. Jing Yao
  4. Antonio J. Plaza
  5. Bing Zhang
  6. Xiuping Jia
  7. Jon Atli Benediktsson
  8. Chenyu Li

In Computer Vision and Pattern Recognition

  1. Jocelyn Chanussot
  2. Antonio J. Plaza
  3. Danfeng Hong
  4. Pedram Ghamisi
  5. Jon Atli Benediktsson
  6. Jing Yao
  7. Naoto Yokoya
  8. Bing Zhang

In Media Technology

  1. Jocelyn Chanussot
  2. Antonio J. Plaza
  3. Pedram Ghamisi
  4. Danfeng Hong
  5. Jon Atli Benediktsson
  6. Shutao Li
  7. Jing Yao
  8. Bing Zhang

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