Given the ever-growing availability of remote sensing data (e.g., Gaofen in China, Sentinel in the EU, and Landsat in the USA), multimodal remote sensing techniques have been garnering increasing attention and made extraordinary progress in various Earth observation-related tasks. The data acquired by different platforms can provide diverse and complementary information. The joint exploitation of multimodal remote sensing has been proven effective in improving the existing methods of land use land cover segmentation in an urban environment. To boost technical breakthroughs and accelerate the development of Earth observation applications across cities or regions, one important task is to build novel cross-city semantic segmentation models based on modern artificial intelligence (AI) technologies and emerging multimodal remote sensing data. This drives us to develop better semantic segmentation models with high transferability among different cities or regions.
This contest is organized in conjunction with the 13th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).
To join the contest, all participants must submit:
The semantic segmentation results for test areas (e.g., in .tiff format) within the deadline by contacting Prof. Dr. Danfeng Hong (email: email@example.com). Please, also indicate: 1) the team ID, 2) the affiliation, 3) the list of members, 4) the corresponding member with the related email address. Please, remember that the list of members cannot be changed later and that there cannot be any overlap among teams.
A report descripting the developed method and model (maximum 2 pages). The organizing committee will ask to submit a full conference paper (that will be included in the WHISPERS proceedings) from the best three teams. The best three teams will present their works in a special session at IEEE WHISPERS 2023.
The winner will get a certificate and will be involved in the writing of an IEEE JSTARS paper summarizing the outcomes of this contest.