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arXiv · 2601.17039

MANGO: A Global Single-Date Paired Dataset for Mangrove Segmentation

Junhyuk Heo·Beomkyu Choi·Hyunjin Shin·Darongsae Kwon
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Abstract

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. To construct this dataset, we retrieve all available Sentinel-2 imagery within the year 2020 for mangrove regions and select the best single-date observations that align with the mangrove annual mask. This selection is performed using a target detection-driven approach that leverages pixel-wise coordinate references to ensure adaptive and representative image-mask pairings. We also provide a benchmark across diverse semantic segmentation architectures under a country-disjoint split, establishing a foundation for scalable and reliable global mangrove monitoring.

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Junhyuk Heo, Beomkyu Choi, Hyunjin Shin, Darongsae Kwon. 2026-07-27. MANGO: A Global Single-Date Paired Dataset for Mangrove Segmentation. https://arxiv.org/abs/2601.17039

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