Global annual urban land cover fractions (2001-2020)

Changes in urban land cover (ULC) provide critical evidence of urbanization including both urban expansion and internal structural renewal. Existing global urbanization research focused on urban expansion and neglected the dynamic ULC changes occurring inside urban areas. This study addresses this issue by developing a Global Annual Urban Land Cover Fraction (GAULCF) dataset, which encompasses six ULC categories (Non-crop Vegetation-Cropland-Building-Non-build Impervious-Surface-Soil-Water, V-C-B-I-S-W) in global urban areas and measures their dynamics from 2001 to 2020. However, V-C-B-I-S-W six kinds of ULC extraction is much more difficult than classical V-I-S extraction, especially for distinguishing non-crop vegetation and cropland, buildings and non-build impervious. Accordingly, this study proposes a novel deep learning unmixing algorithm, Normalized Non-negative Multi-objective Residual T-ConvLSTM (NNMRT) model, with strong encoding and recognition capacities for extracting GAULCF. GAULCF has undergone rigorous accuracy assessment and third-party validation, whose standard error of regression, root mean square error, and mean absolute error are 0.127, 0.113, and 0.061, respectively. The GAULCF dataset reveals significant global ULC changes over the past 20 years: the impervious surface area nearly doubled, while building areas increased from 124,589 km² to 206,603 km²; and vegetation and cropland were the predominant land cover types lost to urban expansion. GAULCF's intensive monitoring of ULC changes during urbanization aids in identifying potential issues in urban development, providing crucial insights and data support for addressing global challenges and achieving sustainable urban development.

城市土地覆被（Urban Land Cover, ULC）变化是城市化进程的关键表征，其既涵盖城市扩张，也包含城市内部的结构更新。现有全球城市化相关研究多聚焦于城市扩张，却忽视了城市内部发生的动态ULC变化。本研究针对这一研究空白，构建了全球年度城市土地覆被占比（Global Annual Urban Land Cover Fraction, GAULCF）数据集，该数据集覆盖全球城市区域内的6类ULC类型（非作物植被-农田-建筑-非建筑不透水面-土壤-水体，即V-C-B-I-S-W），并记录了2001至2020年间各类别的动态变化情况。然而，相较于经典的V-I-S（植被-不透水面-土壤，Vegetation-Impervious Surface-Soil）提取任务，V-C-B-I-S-W六类ULC的提取难度大幅提升，尤其难以区分非作物植被与农田、建筑与非建筑不透水面。据此，本研究提出了一种新型深度学习解混算法——归一化非负多目标残差T-ConvLSTM（Normalized Non-negative Multi-objective Residual T-ConvLSTM, NNMRT）模型，其具备优异的编码与识别能力，可用于GAULCF数据集的提取工作。GAULCF数据集已通过严格的精度评估与第三方验证，其回归标准误差、均方根误差与平均绝对误差分别为0.127、0.113与0.061。GAULCF数据集揭示了过去20年间全球范围内显著的ULC变化：不透水面面积近乎翻倍，建筑用地面积从124589平方千米增长至206603平方千米；而植被与农田则是因城市扩张而流失的主要土地覆被类型。GAULCF对城市化进程中ULC变化的精细化监测，有助于识别城市发展中存在的潜在问题，可为应对全球挑战、实现可持续城市发展提供关键的认知参考与数据支撑。