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

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: 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.