Time-series China urban land use mapping (2016–2022): An approach for achieving spatial-consistency and semantic-transition rationality in temporal domain

If you want to use this data, please cite our article:Xiong, S., Zhang, X., Lei, Y., Tan, G., Wang, H., &amp; Du, S. (2024). Time-series China urban land use mapping (2016–2022): An approach for achieving spatial-consistency and semantic-transition rationality in temporal domain. <i>Remote Sensing of Environment</i>, <i>312</i>, 114344.The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model, but ignored the regional differences among cities; and they generated ULU maps year by year, but ignored temporal correlations between years; thus, they could be weak in large-scale and long time-series ULU monitoring. Accordingly, we introduce an temporal-spatial-semantic collaborative (TSS) mapping framework to generating accurate ULU maps with considering regional differences and temporal correlations. Firstly, to support model training, a large-scale ULU sample dataset based on OpenStreetMap (OSM) and Sentinel-2 imagery is automatically constructed, providing a total number of 56,412 samples with a size of 512 × 512 which are divided into six sub-regions in China and used for training different classification models. Then, an urban land use mapping network (ULUNet) is proposed to recognize ULU. This model utilizes a primary and an auxiliary encoder to process noisy OSM samples and can enhance the model's robustness under noisy labels. Finally, taking the temporal correlations of ULU into consideration, the recognized ULU are optimized, whose boundaries are unified by a time-series co-segmentation, and whose categories are modified by a knowledge-data driven method. To verify the effectiveness of the proposed method, we consider all urban areas in China (254,566 km2), and produce a time-series China urban land use dataset (CULU) at a 10-m resolution, spanning from 2016 to 2022, with an overall accuracy of CULU is 82.42%. Through comparison, it can be found that CULU outperforms existing datasets such as EULUC-China and UFZ-31cities in data accuracies, spatial boundaries consistencies and land use transitions logicality. The results indicate that the proposed method and generated dataset can play important roles in land use change monitoring, ecological-environmental evolution analysis, and also sustainable city development.

若需使用本数据集，请引用以下文献：Xiong, S., Zhang, X., Lei, Y., Tan, G., Wang, H., & Du, S. (2024). 时序中国城市土地利用制图（2016–2022）：一种实现时域空间一致性与语义转换合理性的方法。《环境遥感》（Remote Sensing of Environment），第312卷，论文编号114344。

全球城市化趋势在地理层面体现为城市扩张以及城市内部结构与功能的更新。时序城市土地利用（Urban Land Use, ULU）制图对于捕捉城市化进程中的动态土地变化至关重要，可为城市发展及其环境影响提供宝贵的洞察。现有相关研究多采用统一模型对部分城市开展ULU制图，但忽略了城市间的区域差异；且逐年生成ULU制图结果，却未考虑年份间的时序相关性，因此在大规模长时序ULU监测中存在局限。

为此，我们提出了一种时空语义协同（Temporal-Spatial-Semantic Collaborative, TSS）制图框架，在兼顾区域差异与时序相关性的前提下生成高精度ULU制图结果。首先，为支撑模型训练，我们基于开放街道地图（OpenStreetMap, OSM）与哨兵二号（Sentinel-2）卫星影像自动构建了大规模ULU样本数据集，共包含56412幅尺寸为512×512的样本，将其划分为中国境内6个分区，用于训练不同的分类模型。随后，我们提出了城市土地利用映射网络（Urban Land Use Mapping Network, ULUNet）以实现ULU识别。该模型采用主辅编码器架构处理含噪声的OSM样本，可提升模型在噪声标签下的鲁棒性。最后，我们将ULU的时序相关性纳入考量，对识别得到的ULU结果进行优化：通过时序共分割统一制图边界，并结合知识与数据驱动的方法修正土地利用类别。

为验证所提方法的有效性，我们以中国全境城市区域（总面积254566平方千米）为研究对象，生成了分辨率为10米、覆盖2016至2022年的中国城市土地利用数据集（China Urban Land Use Dataset, CULU），其总体精度达82.42%。经对比可知，CULU在数据精度、空间边界一致性以及土地利用转换逻辑性上均优于现有数据集，如EULUC-China与UFZ-31cities。研究结果表明，所提方法与生成的数据集可在土地利用变化监测、生态环境演化分析以及可持续城市发展等领域发挥重要作用。