DCP-MTL: Vectorization of Agricultural Cultivation Field Parcels via Boundary-Parcel Multi-Task Learning Network in Ultra-High-Resolution Remote Sensing Images

This paper introduces the first UHR UAV dataset specifically for CFP, designed to evaluate the performance of the proposed model in identifying these parcels. The dataset offers ultra-high spatial resolution, various field parcel types, and broad geographic coverage. Figure 8 shows the spatial distribution of the study data. Jilin Province is the primary region for training and evaluating the model, while Hebei, Henan, Anhui, Zhejiang, and Hainan are auxiliary regions for testing the model's transferability.本文介绍了首个针对CFP的超高分辨率无人机（UHR UAV）数据集，该数据集专为评估所提模型识别地块的性能而设计。本数据集具备超高空间分辨率、多样的地块类型以及广泛的地理覆盖范围。图8展示了本研究数据的空间分布情况。吉林省是模型训练与评估的核心区域，河北、河南、安徽、浙江及海南则为测试模型迁移能力的辅助区域。