{"ID":2842427,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10604","arxiv_id":"2511.10604","title":"Multitask GLocal OBIA-Mamba for Sentinel-2 Landcover Mapping","abstract":"Although Sentinel-2 based land use and land cover (LULC) classification is critical for various environmental monitoring applications, it is a very difficult task due to some key data challenges (e.g., spatial heterogeneity, context information, signature ambiguity). This paper presents a novel Multitask Glocal OBIA-Mamba (MSOM) for enhanced Sentinel-2 classification with the following contributions. First, an object-based image analysis (OBIA) Mamba model (OBIA-Mamba) is designed to reduce redundant computation without compromising fine-grained details by using superpixels as Mamba tokens. Second, a global-local (GLocal) dual-branch convolutional neural network (CNN)-mamba architecture is designed to jointly model local spatial detail and global contextual information. Third, a multitask optimization framework is designed to employ dual loss functions to balance local precision with global consistency. The proposed approach is tested on Sentinel-2 imagery in Alberta, Canada, in comparison with several advanced classification approaches, and the results demonstrate that the proposed approach achieves higher classification accuracy and finer details that the other state-of-the-art methods.","short_abstract":"Although Sentinel-2 based land use and land cover (LULC) classification is critical for various environmental monitoring applications, it is a very difficult task due to some key data challenges (e.g., spatial heterogeneity, context information, signature ambiguity). This paper presents a novel Multitask Glocal OBIA-Ma...","url_abs":"https://arxiv.org/abs/2511.10604","url_pdf":"https://arxiv.org/pdf/2511.10604v1","authors":"[\"Zack Dewis\",\"Yimin Zhu\",\"Zhengsen Xu\",\"Mabel Heffring\",\"Saeid Taleghanidoozdoozan\",\"Kaylee Xiao\",\"Motasem Alkayid\",\"Lincoln Linlin Xu\"]","published":"2025-11-13T18:40:22Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}