UGS-1m: Fine-grained urban green space mapping of 34 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 34 major cities/areas 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 4,454 samples of size 512×512 is provided. Code for the UGSet and the UGSet will be soon available at: https://github.com/liumency/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/areas through adversarial training; c) Finally, the UGS results of the 34 major cities/areas in China (UGS-1m) are obtained using 2,343 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 approach on samples from Guangzhou city shows the validity of the UGS-1m products, with an overall accuracy of 87.4% and an F1 score of 81.14%.