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

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.

全球城市化趋势在地理层面体现为城市扩张与内部城市结构、功能的更新。时序城市土地利用（Time-series Urban Land Use, ULU）地图对于捕捉城市化进程中的动态土地变化至关重要，能够为城市发展及其环境影响提供宝贵的研究视角。现有部分研究通过统一模型对部分城市的ULU进行了制图，但忽略了城市间的区域差异；同时虽逐年生成ULU地图，却未考虑年份间的时序相关性，因此在大规模、长时序ULU监测方面存在局限性。为此，我们提出了一种时空语义协同（Temporal-Spatial-Semantic Collaborative, TSS）制图框架，以在兼顾区域差异与时序相关性的前提下生成高精度ULU地图。首先，为支撑模型训练，我们基于开放街道地图（OpenStreetMap, OSM）与哨兵2号（Sentinel-2）遥感影像自动构建了大规模ULU样本数据集，共生成56412张尺寸为512×512的样本，这些样本被划分为中国境内6个子区域，用于训练不同的分类模型。随后，我们提出了城市土地利用制图网络（Urban Land Use Mapping Network, ULUNet）以实现ULU识别。该模型采用主编码器与辅助编码器处理带噪声的OSM样本，能够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：通过时序协同分割统一地块边界，并借助知识-数据驱动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。

搜集汇总

背景与挑战

背景概述

该数据集是一个2016-2022年中国城市土地利用的高分辨率(10米)时间序列地图，采用创新的时空语义协作框架生成，覆盖全国所有城市区域，总体准确率达82.42%，优于现有同类数据集。数据集包含两个压缩文件(总大小2.38GB)，可用于土地利用变化监测、生态环境演变分析和城市可持续发展研究。