UGS-1m: Fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework

Urban green space (UGS) is an important component in the urban ecosystem and has great significance to the urban ecological environment. The UGS-1m product provides the fine-grained UGS maps of 31 major cities in China, which is generated based on a deep learning (DL) framework.The DL framework consists of a generator and a discriminator. The generator is a fully convolutional network designed for UGS extraction (UGSNet), which integrates attention mechanisms to improve the discrimination to UGS, and employs a point rending strategy for edge recovery. The discriminator is a fully connected network aiming to deal with the domain shift between images. To support the model training, an urban green space dataset (UGSet) with a total number of 4,554 samples of size 512×512 is provided. Code for the UGSet and the UGSet will be available at: https://liumency.github.io/UGS-1m/. The main steps to obtain UGS-1m can be summarized as follows: a) Firstly, the UGSNet will be pre-trained on the UGSet in order to get a good starting training point for the generator; b) After pre-training on the UGSet, the discriminator is responsible to adapt the pre-trained UGSNet to different cities through adversarial training; c) Finally, the UGS results of the 31 major cities in China (UGS-1m) are obtained using 2,179 Google Earth images with a data frame of 7'30" in longitude and 5'00" in latitude, and a spatial resolution of nearly 1.1 meters. Evaluating the performance of the proposed framework on samples from five sample cities shows the validity of the UGS-1m products, with an average overall accuracy (OA) of 87.56% and an F1 score of 74.86%.Dataset Descriptions1) UGS-1m.zip: the fine-grained UGS map product of 31 major cities in China2) UGSet.zip: the large benchmark dataset to support and foster the UGS research3) GUB_Data.zip: the Global Urban Boundary data of each city4) GE_Imagery_DataFrame.zip: the grid data of the Google Earth images in “.shp” format, providing the image composition of each city5) other Zip files named by city names: the Google Earth images of each city