Multimodal Semantic Segmentation in Yangtze River Economic Belt: Outcome of the 2024 IEEE WHISPERS MMSeg-YREB Challenge

With the growing availability of remote sensing (RS) data from diverse platforms, multimodal RS techniques have emerged as a transformative solution for large-scale semantic segmentation. In response, we developed MMSeg-YREB, a specialized framework that integrates complementary RS modalities, such as multispectral and synthetic aperture radar (SAR) data from Sentinel-1/2 sources, to enhance the accuracy and robustness of land use and land cover (LULC) mapping across urban and regional landscapes within the Yangtze River Economic Belt (YREB). By leveraging extensive geographic coverage and heterogeneous data sources, MMSeg-YREB supports a wide range of applications, from precise urban planning to comprehensive environmental monitoring. Utilizing state-of-the-art artificial intelligence (AI) methodologies, this framework aims to develop highly generalizable and scalable semantic segmentation models, driving methodological advancements and accelerating the adoption of Earth observation (EO) technologies across diverse regions. As part of this initiative, the Multimodal Semantic Segmentation challenge, i.e., MMSeg-YREB, is organized in conjunction with the 14th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2024. To foster further research and innovation, all datasets and code will be publicly released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/danfenghong/Outcome-of-the-2024-IEEE-WHISPERS-MMSeg-YREB-Challenge</uri> for the sake of reproducibility, contributing to the broader EO and RS communities.

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Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/jstars.2025.3583442
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Multimodal Semantic Segmentation in Yangtze River Economic Belt: Outcome of the 2024 IEEE WHISPERS MMSeg-YREB Challenge

A. K. Qin, Sung‐Bin Son, Wei He, Yinhe Liu et al.
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Traffic Prediction and Management Techniques
article

Multimodal Semantic Segmentation in Yangtze River Economic Belt: Outcome of the 2024 IEEE WHISPERS MMSeg-YREB Challenge

A. K. Qin, Sung‐Bin Son, Wei He, Yinhe Liu, Renwei Dian, Naoto Yokoya, Gemine Vivone, Jiepan Li, Shutao Li, Danfeng Hong, Jaejun Yoo, Chenyu Li, Haipeng Wang, Hyeonjin Kim, Han Huang, Xian Xu, Guo Qi, Wei Kan, Jeongho Min, Yu Xia, Jocelyn Chanussot, Hao Liu, Yanfei Zhong, Yingxin Wu
article en

Abstract

With the growing availability of remote sensing (RS) data from diverse platforms, multimodal RS techniques have emerged as a transformative solution for large-scale semantic segmentation. In response, we developed MMSeg-YREB, a specialized framework that integrates complementary RS modalities, such as multispectral and synthetic aperture radar (SAR) data from Sentinel-1/2 sources, to enhance the accuracy and robustness of land use and land cover (LULC) mapping across urban and regional landscapes within the Yangtze River Economic Belt (YREB). By leveraging extensive geographic coverage and heterogeneous data sources, MMSeg-YREB supports a wide range of applications, from precise urban planning to comprehensive environmental monitoring. Utilizing state-of-the-art artificial intelligence (AI) methodologies, this framework aims to develop highly generalizable and scalable semantic segmentation models, driving methodological advancements and accelerating the adoption of Earth observation (EO) technologies across diverse regions. As part of this initiative, the Multimodal Semantic Segmentation challenge, i.e., MMSeg-YREB, is organized in conjunction with the 14th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2024. To foster further research and innovation, all datasets and code will be publicly released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/danfenghong/Outcome-of-the-2024-IEEE-WHISPERS-MMSeg-YREB-Challenge</uri> for the sake of reproducibility, contributing to the broader EO and RS communities.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 18
Institut polytechnique de Grenoble (FR), Centre National de la Recherche Scientifique (FR), Hunan University (CN), Shanghai Jiao Tong University (CN), Chinese Academy of Sciences (CN), Harbin Institute of Technology (CN), Wuhan University (CN), Centre Inria de l'Université Grenoble Alpes (FR), CNR-IMIOT – Consiglio Nazionale delle Ricerche - Istituto di Metodologie Integrate per l’Osservazione della Terra (IT), State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN), Peng Cheng Laboratory (CN), Aerospace Information Research Institute (CN), Laboratoire Jean Kuntzmann (FR), National Research Council (IT), Ulsan National Institute of Science and Technology (KR), Swinburne University of Technology (AU), The University of Tokyo (JP), Southeast University (CN), Université Grenoble Alpes (FR)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 84%
Traffic Prediction and Management Techniques
DOIOpen Access PDFOpenAlex
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