2026-10-13T01:23:31Z https://www.scidb.cn/oai

10.57760/sciencedb.07049 2023-02-17T16:29:22Z

2023-02-17 UGS-1m: Fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework doi:10.57760/sciencedb.07049 en 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 urban green space; deep learning; Earth Science Qian Shi Mengxi Liu Andrea Marinoni Xiaoping Liu PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/https://doi.org/10.5194/essd-15-555-2023 Science Data Bank