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

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.

Tackling Few-Shot Segmentation in Remote Sensing via Inpainting Diffusion Model

Limited data is a common problem in remote sensing due to the high cost of obtaining annotated samples. In the few-shot segmentation task, models are typically trained on base classes with abundant annotations and later adapted to novel classes with limited examples. However, this often necessitates specialized model architectures or complex training strategies. Instead, we propose a simple approach that leverages diffusion models to generate diverse variations of novel-class objects within a given scene, conditioned by the limited examples of the novel classes. By framing the problem as an image inpainting task, we synthesize plausible instances of novel classes under various environments, effectively increasing the number of samples for the novel classes and mitigating overfitting. The generated samples are then assessed using a cosine similarity metric to ensure semantic consistency with the novel classes. Additionally, we employ Segment Anything Model (SAM) to segment the generated samples and obtain precise annotations. By using high-quality synthetic data, we can directly fine-tune off-the-shelf segmentation models. Experimental results demonstrate that our method significantly enhances segmentation performance in low-data regimes, highlighting its potential for real-world remote sensing applications.

Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine Translation

Employing extensive datasets enables the training of multilingual machine translation models; however, these models often fail to accurately translate sentences within specialized domains. Although obtaining and translating domain-specific data incurs high costs, it is inevitable for high-quality translations. Hence, finding the most 'effective' data with an unsupervised setting becomes a practical strategy for reducing labeling costs. Recent research indicates that this effective data could be found by selecting 'properly difficult data' based on its volume. This means the data should not be excessively challenging or overly simplistic, especially if the amount of data is limited. However, we found that establishing a criterion for unsupervised data selection remains challenging, as the 'proper difficulty' might vary based on the data domain being trained on. We introduce a novel unsupervised data selection method, 'Capturing Perplexing Named Entities', which adopts the maximum inference entropy in translated named entities as a selection measure. The motivation was that named entities in domain-specific data are considered the most complex portion of the data and should be predicted with high confidence. When verified with the 'Korean-English Parallel Corpus of Specialized Domains,' our method served as a robust guidance for unsupervised data selection, in contrast to existing methods.