scholarly journals Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method

2021 ◽  
Vol 13 (3) ◽  
pp. 477
Author(s):  
Xiaoting Li ◽  
Tengyun Hu ◽  
Peng Gong ◽  
Shihong Du ◽  
Bin Chen ◽  
...  
Keyword(s):  
Land Use ◽  
Urban Land ◽  
Urban Land Use ◽  
Fast Method ◽  
Sample Collection ◽  
Point Of Interest ◽  
Area Of Interest ◽  
Mapping Process ◽  
Mapping Result ◽  
Use Categories

Urban land use mapping is critical to understanding human activities in space. The first national mapping result of essential urban land use categories of China (EULUC-China) was released in 2019. However, the overall accuracies in some of the plain cities such as Beijing, Chengdu, and Zhengzhou were lower than 50% because many parcel-based mapping units are large with mixed land uses. To address this shortcoming, we proposed an area of interest (AOI)-based mapping approach, choosing Beijing as our study area. The mapping process includes two major steps. First, grids with different sizes (i.e., 300 m, 200 m, and 100 m) were derived from original land parcels to obtain classification units with a suitable size. Then, features within these grids were extracted from Sentinel-2 spectral data, point of interest (POI), and Tencent Easygo crowdedness data. These features were classified using a random forest (RF) classifier with AOI data, resulting in a 10-category map of EULUC. Second, we superimposed the AOIs layer on classified units to do some rectification and offer more details at the building scale. The overall accuracy of the AOI layer reached 98%, and the overall accuracy of the mapping results reached 77%. This study provides a fast method for accurate geographic sample collection, which substantially reduces the amount of fieldwork for sample collection and improves the classification accuracy compared to previous EULUC mapping. The detailed urban land use map could offer more support for urban planning and environmental policymaking.

scholarly journals Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach

2020 ◽  
Vol 12 (7) ◽  
pp. 1058 ◽  
Author(s):  
Ying Tu ◽  
Bin Chen ◽  
Tao Zhang ◽  
Bing Xu
Keyword(s):  
Land Use ◽  
Learning Algorithm ◽  
Accuracy Assessment ◽  
Regional Analysis ◽  
Urban Land ◽  
Urban Land Use ◽  
Level Ii ◽  
Segmentation Approach ◽  
Level I ◽  
Use Categories

Understanding distributions of urban land use is of great importance for urban planning, decision support, and resource allocation. The first mapping results of essential urban land use categories (EULUC) in China for 2018 have been recently released. However, such kind of national maps may not sufficiently meet the growing demand for regional analysis. To address this shortcoming, here we proposed a segmentation-based framework named EULUC-seg to improve the mapping results of EULUC at the city scale. An object-based segmentation approach was first applied to generate the basic mapping units within urban parcels. Multiple features derived from high-resolution remotely sensed and social sensing data were updated and then recalculated within each unit. Random forest was adopted as the machine learning algorithm for classifying urban land use into five Level I classes and twelve Level II classes. Finally, an accuracy assessment was carried out based on a collection of manually interpreted samples. Results showed that our derived map achieved an overall accuracy of 87.58% for Level I, and 73.53% for Level II. The accurate and refined map of EULUC-seg is expected to better support various applications in the future.

Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

2020 ◽  
Vol 65 (3) ◽  
pp. 182-187 ◽  
Author(s):  
Peng Gong ◽  
Bin Chen ◽  
Xuecao Li ◽  
Han Liu ◽  
Jie Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Urban Land ◽  
Urban Land Use ◽  
Preliminary Results ◽  
Use Categories

scholarly journals Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou

2020 ◽  
Vol 12 (12) ◽  
pp. 1987 ◽  
Author(s):  
Leli Zong ◽  
Sijia He ◽  
Jiting Lian ◽  
Qiang Bie ◽  
Xiaoyun Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Road Network ◽  
Population Distribution ◽  
Land Development ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Classification ◽  
Point Of Interest ◽  
Source Data ◽  
Population Information

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.

scholarly journals Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2386 ◽  
Author(s):  
Jing Sun ◽  
Hong Wang ◽  
Zhenglin Song ◽  
Jinbo Lu ◽  
Pengyu Meng ◽  
...  
Keyword(s):  
Land Use ◽  
Classification Accuracy ◽  
Urban Land ◽  
Urban Land Use ◽  
Rapid Urbanization ◽  
Level Ii ◽  
Training Samples ◽  
Level I ◽  
Use Categories ◽  
Land Use Maps

High-spatial-resolution (HSR) urban land use maps are very important for urban planning, traffic management, and environmental monitoring. The rapid urbanization in China has led to dramatic urban land use changes, however, so far, there are no such HSR urban land use maps based on unified classification frameworks. To fill this gap, the mapping of 2018 essential urban land use categories in China (EULUC-China) was jointly accomplished by a group of universities and research institutes. However, the relatively lower classification accuracy may not sufficiently meet the application demands for specific cities. Addressing these challenges, this study took Nanjing city as the case study to further improve the mapping practice of essential urban land use categories, by refining the generation of urban parcels, resolving the problem of unbalanced distribution of point of interest (POI) data, integrating the spatial dependency of POI data, and evaluating the size of training samples on the classification accuracy. The results revealed that (1) the POI features played the most important roles in classification performance, especially in identifying administrative, medical, sport, and cultural land use categories, (2) compared with the EULUC-China, the overall accuracy for Level I and Level II in EULUC-Nanjing has increased by 11.1% and 5%, to 86.1% and 80% respectively, and (3) the classification accuracy of Level I and Level II would be stable when the number of training samples was up to 350. The methods and findings in this study are expected to better inform the regional to continental mappings of urban land uses.

scholarly journals Mining point-of-interest data from social networks for urban land use classification and disaggregation

2015 ◽  
Vol 53 ◽  
pp. 36-46 ◽  
Author(s):  
Shan Jiang ◽  
Ana Alves ◽  
Filipe Rodrigues ◽  
Joseph Ferreira ◽  
Francisco C. Pereira
Keyword(s):  
Social Networks ◽  
Land Use ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Classification ◽  
Point Of Interest

Physical, Chemical, and Biological Methods and Data from the Urban Land-Use-Gradient Study, Des Plaines and Fox River Basins, Illinois, 1999-2001

2001 ◽  
Author(s):  
Debbie L. Adolphson ◽  
Terri L. Arnold ◽  
Faith A. Fitzpatrick ◽  
Mitchell A. Harris ◽  
Kevin D. Richards ◽  
...  
Keyword(s):  
Land Use ◽  
River Basins ◽  
Urban Land ◽  
Urban Land Use ◽  
Physical Chemical ◽  
Biological Methods ◽  
Fox River
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