GAULCF (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.

城市土地覆盖（Urban Land Cover, ULC）变化是城市化进程的关键佐证，涵盖城市扩张与内部结构更新两大维度。现有全球城市化研究多聚焦于城市扩张，却忽视了城市内部发生的动态城市土地覆盖变化。本研究通过构建全球年度城市土地覆盖占比（Global Annual Urban Land Cover Fraction, GAULCF）数据集解决了这一问题，该数据集覆盖全球城市区域内的6类城市土地覆盖类型：非作物植被、耕地、建筑、非建筑不透水面、土壤与水体（V-C-B-I-S-W），并刻画了2001至2020年间这些类型的动态变化过程。然而，相较于经典的植被-不透水面-土壤（Vegetation-Impervious Surface-Soil, V-I-S）信息提取，同时提取V-C-B-I-S-W六类城市土地覆盖的难度显著提升，尤其难以区分非作物植被与耕地、建筑与非建筑不透水面。据此，本研究提出一种新型深度学习混合像元分解算法——归一化非负多目标残差T-ConvLSTM（Normalized Non-negative Multi-objective Residual T-ConvLSTM, NNMRT）模型，该模型具备强大的编码与识别能力，可用于提取全球年度城市土地覆盖占比数据集。该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。全球年度城市土地覆盖占比数据集揭示了过去20年间全球范围内显著的城市土地覆盖变化：不透水面面积近乎翻倍，建筑用地面积从124589平方千米增至206603平方千米；植被与耕地则是因城市扩张而流失的主要土地覆盖类型。该数据集对城市化进程中城市土地覆盖变化的精细化监测，有助于识别城市发展中存在的潜在问题，为应对全球挑战、实现可持续城市发展提供关键的认知支撑与数据支持。