This page has only limited features, please log in for full access.

Unclaimed
Leli Zong
College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000, China

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 20 June 2020 in Remote Sensing
Reads 0
Downloads 0

Detailed urban land use information is the prerequisite and foundation for implementing urban land policies and urban land development, and is of great importance for solving urban problems, assisting scientific and rational urban planning. The existing results of urban land use mapping have shortcomings in terms of accuracy or recognition scale, and it is difficult to meet the needs of fine urban management and smart city construction. This study aims to explore approaches that mapping urban land use based on multi-source data, to meet the needs of obtaining detailed land use information and, taking Lanzhou as an example, based on the previous study, we proposed a process of urban land use classification based on multi-source data. A combination road network dataset of Gaode and OpenStreetMap (OSM) was synthetically applied to divide urban parcels, while multi-source features using Sentinel-2A images, Sentinel-1A polarization data, night light data, point of interest (POI) data and other data. Simultaneously, a set of comparative experiments were designed to evaluate the contribution and impact of different features. The results showed that: (1) the combination utilization of Gaode and OSM road network could improve the classification results effectively. Specifically, the overall accuracy and kappa coefficient are 83.75% and 0.77 separately for level I and the accuracy of each type reaches more than 70% for level II; (2) the synthetic application of multi-source features is conducive to the improvement of urban land use classification; (3) Internet data, such as point of interest (POI) information and multi-time population information, contribute the most to urban land use mapping. Compared with single-moment population information, the multi-time population distribution makes more contributions to urban land use. The framework developed herein and the results derived therefrom may assist other cities in the detailed mapping and refined management of urban land use.

ACS Style

Leli Zong; Sijia He; Jiting Lian; Qiang Bie; Xiaoyun Wang; Jingru Dong; Yaowen Xie. Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou. Remote Sensing 2020, 12, 1987 .

AMA Style

Leli Zong, Sijia He, Jiting Lian, Qiang Bie, Xiaoyun Wang, Jingru Dong, Yaowen Xie. Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou. Remote Sensing. 2020; 12 (12):1987.

Chicago/Turabian Style

Leli Zong; Sijia He; Jiting Lian; Qiang Bie; Xiaoyun Wang; Jingru Dong; Yaowen Xie. 2020. "Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou." Remote Sensing 12, no. 12: 1987.