用掩码聚类自动标注亚米级遥感图像，效率提升10~100倍
Mask Clustering-based Annotation Engine for Large-Scale Submeter Land Cover Mapping
- 基于空间自相关原理，将语义一致的掩码组作为最小标注单元
- 构建含140亿像素的高质量数据集，五城地图分类准确率超85%
- 首个公开的亚米级城市级地表覆盖基准数据集，适合大规模遥感研究
近年来遥感技术进步使亚米级影像日益普及，为精细地表覆盖分析提供了丰富细节。然而，由于缺乏足够且高质量的标注数据，其潜力尚未充分释放。现有标签多来自预存产品或人工标注，可靠性差或成本过高，尤其在亚米级影像的海量数据背景下更为突出。受空间自相关原理启发——同类物体倾向于在局部邻域内共现并具有相似视觉特征——我们提出掩码聚类标注引擎（MCAE），将语义一致的掩码组视为最小标注单位，实现多个实例的高效同步标注。该方法使标注效率提升一到两个数量级，同时保持标签质量、语义多样性和空间代表性。基于MCAE，我们构建了约140亿像素的高质量标注数据集，命名为HiCity-LC，支持中国五大城市的城级地表覆盖制图，分类准确率均超过85%。这是首个公开的亚米级分辨率城市级地表覆盖基准数据集，凸显MCAE在大规模亚米级制图中的可扩展性与实用性。数据集已开放：https://github.com/chenhaocs/MCAE
原文摘要 · Abstract (English)
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
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