Mask Clustering-based Annotation Engine for Large-Scale Submeter Land Cover Mapping

2025-09-29

AI Summary

To address the severe scarcity of high-quality labeled data for sub-meter remote sensing land-cover mapping, this paper proposes the Mask Clustering Annotation Engine (MCAE)—the first framework to introduce mask-level clustering into remote sensing annotation. Leveraging spatial autocorrelation, MCAE employs semantically consistent mask groups as the minimal annotation unit, enabling efficient and high-fidelity automated labeling. The method integrates instance segmentation, mask clustering, and spatial context modeling to ensure label diversity, representativeness, and spatial accuracy. We construct HiCity-LC, the first publicly available sub-meter urban benchmark dataset, covering five major Chinese cities with approximately 14 billion annotated pixels. HiCity-LC supports city-scale mapping, achieving an average classification accuracy exceeding 85%. MCAE improves annotation efficiency by one to two orders of magnitude, significantly advancing large-scale applications of sub-meter remote sensing imagery.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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

Problem

• Addressing the lack of high-quality annotated datasets for submeter land cover mapping
• Overcoming unreliable and expensive manual annotation for large-scale imagery
• Enabling efficient simultaneous annotation of multiple instances via clustering

Innovation

• Mask Clustering-based Annotation Engine for efficient annotation
• Treats semantically consistent mask groups as minimal units
• Enables simultaneous annotation of multiple instances efficiently