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This selection is performed using a target detection-driven approach that leverages pixel-wise coordinate references to ensure adaptive and ","title":"MANGO: A Global Single-Date Paired Dataset for Mangrove Segmentation","url":"https://arxiv.org/abs/2601.17039","vendor":"arxiv_cs_ai"},"summary":"arXiv:2601.17039v2 Announce Type: replace-cross \nAbstract: Mangroves are critical for climate-change mitigation, requiring reliable monitoring for effective conservation. While deep learning has emerged as a powerful tool for mangrove detection, its progress is hindered by the limitations of existing datasets. In particular, many resources provide only annual map products without curated single-date image-mask pairs, limited to specific regions rather than global coverage, or remain inaccessible to the public. To address these challenges, we introduce MANGO, a large-scale global dataset comprising 42,703 labeled image-mask pairs across 124 countries. 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