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

TL;DR

MANGO is a comprehensive global dataset of over 42,700 single-date mangrove images and masks from Sentinel-2, enabling improved deep learning-based mangrove detection and monitoring.

Contribution

It introduces the first large-scale, publicly accessible global dataset with curated single-date image-mask pairs for mangrove segmentation, facilitating scalable monitoring.

Findings

Benchmark results across multiple segmentation architectures.

Demonstrated the dataset's utility for global mangrove monitoring.

Provided a novel selection method for representative single-date images.

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.