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，114344号论文。

全球城市化趋势在地理层面表现为城市扩张与城市内部结构及功能的更新。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科学见解。现有研究多采用统一模型对部分城市的ULU进行制图，但忽略了不同城市间的区域差异；且逐年生成ULU制图，却未考虑年份间的时序相关性，因此难以支撑大规模长时序ULU监测。为此，本文提出时空语义协同（Temporal-Spatial-Semantic, 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。

首先，为支撑模型训练，本研究基于开放街道地图（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，共包含56412幅尺寸为512×512的样本，将其划分为中国境内6个子区域，用于训练不同分类模型。随后，本文提出城市土地利用制图网络（Urban Land Use Mapping Network, ULUNet）以实现ULU识别。该模型采用主辅编码器处理含噪声的OSM样本，可提升模型在噪声标签条件下的鲁棒性。最后，考虑到ULU的时序相关性，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，采用知识-数据驱动方法修正地物类别。

为验证所提方法的有效性，本研究覆盖中国全境城市区域（总面积254566 km²），生成了分辨率为10米、时间跨度为2016至2022年的时序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%。对比实验表明，CULU在数据精度、空间边界一致性与土地利用转换逻辑性上均优于EULUC-China、UFZ-31cities等现有公开数据集。研究结果显示，本文提出的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支撑作用。

搜集汇总

背景与挑战

背景概述

该数据集是一个时间序列的中国城市土地利用地图数据集，覆盖2016年至2022年，以10米分辨率生成，总体准确率为82.42%。它基于OpenStreetMap和Sentinel-2影像自动构建样本，采用时空语义协作框架，考虑了区域差异和时间相关性，旨在提高空间一致性和语义转换合理性，适用于土地利用变化监测和城市可持续发展分析。