UGS-1m: Fine-grained urban green space mapping of 34 major cities in China based on the deep learning framework

<p>Urban green space (UGS) is an important component in the urban ecosystem and has great significance to the urban ecological environment. The UGS-1m&nbsp;product provides the fine-grained UGS maps of 34 major cities/areas in China, which is generated based on&nbsp;a deep learning (DL) framework.</p> <p>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&times;512 is provided. Code for the UGSet and the UGSet will be soon available at:&nbsp; https://github.com/liumency/UGS-1m.&nbsp;</p> <p>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&#39;30&quot; in longitude and 5&#39;00&quot; 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%.&nbsp;</p>