标题: An Improved Generative Adversarial Network for Generating Multi-Scale Electronic Map Tiles Considering Cartographic Requirements
作者: Zhu, W (Zhu, Wei); Guo, QS (Guo, Qingsheng); Yang, N (Yang, Nai); Tong, Y (Tong, Ying); Zheng, CB (Zheng, Chuanbang)
来源出版物: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 卷: 13 期: 11 文献号: 398 DOI: 10.3390/ijgi13110398 Published Date: 2024 NOV
摘要: Multi-scale electronic map tiles are important basic geographic information data, and an approach based on deep learning is being used to generate multi-scale map tiles. Although generative adversarial networks (GANs) have demonstrated great potential in single-scale electronic map tile generation, further research concerning multi-scale electronic map tile generation is needed to meet cartographic requirements. We designed a multi-scale electronic map tile generative adversarial network (MsM-GAN), which consisted of several GANs and could generate map tiles at different map scales sequentially. Road network data and building footprint data from OSM (Open Street Map) were used as auxiliary information to provide the MsM-GAN with cartographic knowledge about spatial shapes and spatial relationships when generating electronic map tiles from remote sensing images. The map objects which should be deleted or retained at the next map scale according to cartographic standards are encoded as auxiliary information in the MsM-GAN when generating electronic map tiles at smaller map scales. In addition, in order to ensure the consistency of the features learned by several GANs, the density maps constructed from specific map objects are used as global conditions in the MsM-GAN. A multi-scale map tile dataset was collected from MapWorld, and experiments on this dataset were conducted using the MsM-GAN. The results showed that compared to other image-to-image translation models (Pix2Pix and CycleGAN), the MsM-GAN shows average increases of 10.47% in PSNR and 9.92% in SSIM and has the minimum MSE values at all four map scales. The MsM-GAN also performs better in visual evaluation. In addition, several comparative experiments were completed to verify the effect of the proposed improvements.
作者关键词: generating adversarial networks; serial generative adversarial network; cartographic generalization; multi-scale electronic map tiles; auxiliary information
地址: [Zhu, Wei; Guo, Qingsheng; Tong, Ying; Zheng, Chuanbang] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.
[Yang, Nai] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430078, Peoples R China.
通讯作者地址: Guo, QS (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.
电子邮件地址: zhuweiwork@whu.edu.cn; guoqingsheng@whu.edu.cn; yangnai@cug.edu.cn; tongying@whu.edu.com; zhengcb@whu.edu.cn
影响因子：2.8
