面向大规模亚米级土地覆盖制图的基于掩码聚类的标注引擎
Mask Clustering-based Annotation Engine for Large-Scale Submeter Land Cover Mapping
AI总结：
针对亚米级遥感影像大规模高质量标注难题，提出MCAE掩码聚类标注引擎，构建140亿像素HiCity-LC基准并支持五城市85%以上准确率制图。
摘要：
遥感技术的最新进展使亚米级分辨率影像日益普及，为细粒度土地覆盖分析提供了显著的细节。然而，由于缺乏充足、高质量的标注数据集，其全部潜力仍未得到充分利用，尤其是在大规模土地覆盖制图方面。现有标签通常来自既有产品或人工标注，往往不可靠或成本高昂，鉴于亚米级影像丰富的视觉细节和庞大的数据量更是如此。受空间自相关原理启发，即同一类别的对象往往在局部邻域中与相似视觉特征共同出现，我们提出了基于掩码聚类的标注引擎（Mask Clustering-based Annotation Engine，MCAE），该引擎将语义一致的掩码组视为最小标注单元，以实现多个实例的高效同步标注。它将标注效率显著提升了一到两个数量级，同时保持了标签质量、语义多样性和空间代表性。借助MCAE，我们构建了一个包含约140亿个标注像素的高质量标注数据集，称为HiCity-LC，该数据集支持生成中国五个主要城市的城市级土地覆盖图，分类准确率超过85%。这是首个公开可用的亚米级分辨率城市级土地覆盖基准，凸显了MCAE在大规模、亚米级分辨率制图中的可扩展性和实用价值。该数据集可在https://github.com/chenhaocs/MCAE获取。
英文摘要：
Recent advances in remote sensing technology have made submeter resolution imagery increasingly accessible, offering remarkable detail for fine-grained land cover analysis. However, its full potential remains underutilized - particularly for large-scale land cover mapping - due to the lack of sufficient, high-quality annotated datasets. Existing labels are typically derived from pre-existing products or manual annotation, which are often unreliable or prohibitively expensive, particularly given the rich visual detail and massive data volumes of submeter imagery. Inspired by the spatial autocorrelation principle, which suggests that objects of the same class tend to co-occur with similar visual features in local neighborhoods, we propose the Mask Clustering-based Annotation Engine (MCAE), which treats semantically consistent mask groups as the minimal annotating units to enable efficient, simultaneous annotation of multiple instances. It significantly improves annotation efficiency by one to two orders of magnitude, while preserving label quality, semantic diversity, and spatial representativeness. With MCAE, we build a high-quality annotated dataset of about 14 billion labeled pixels, referred to as HiCity-LC, which supports the generation of city-scale land cover maps across five major Chinese cities with classification accuracies above 85%. It is the first publicly available submeter resolution city-level land cover benchmark, highlighting the scalability and practical utility of MCAE for large-scale, submeter resolution mapping. The dataset is available at https://github.com/chenhaocs/MCAE
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
