Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin

Long time series (e.g. 30 years) urban land observations from remote sensing images are important for urban growth modeling as well as for the goal of sustainable urban development. However, updates to regional and even global maps are infrequent due to the cost and difficulty of collecting representative training data and the requirement for high-performance computation in processing large amounts of images. In this study, a semiautomatic large-scale and long time series (LSLTS) urban land mapping framework is demonstrated by integrating the crowdsourced OpenStreetMap (OSM) data with free Landsat images (similar to 13,218 scenes) to generate annual urban land maps in the 317,000 km(2) middle Yangtze River basin (MYRB) from 1987 to 2017 facilitated by Google Earth Engine (GEE). Random training samples for latest year were generated based on the updated OSM land use data after a manual topological conflict processing and uploaded to GEE for automatic image classification. For each historical year, training samples were obtained with a proposed transferring schema by which only the unchanged were selected through a change detection analysis. The annual spectral indices and texture feature maps acquired from the surface reflectance dataset were also added to the original bands. Finally, the classified maps were downloaded from GEE and a spatial-temporal consistency checking was further performed. Based on independent samples, the overall accuracies and kappa coefficients of all years ranged from 98% to 99% and 0.65 to 0.85, respectively. Our product when compared with current 30 m land-cover products showed similar accuracies but more spatial details. The characteristics of pattern, traces, and hotspots of urban expansion were further explored. This study provides a more convenient procedure for LSLTS urban land mapping especially for areas where large-scale field sample-collection is difficult and little historical crowdsourced datasets are available. The resultant dataset is expected to provide consistent details about the spatial distribution of urban land in MYRB. We highlight the potential use of this proposed framework to be applied and validated to other parts of the world to help better understand and quantify various aspects of urban-related problems.