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 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

scholarly journals Social Sensing for Urban Land Use Identification

2020 ◽  
Vol 9 (9) ◽  
pp. 550
Author(s):  
Adindha Anugraha ◽  
Hone-Jay Chu ◽  
Muhammad Ali
Keyword(s):  
Land Use ◽  
Accuracy Assessment ◽  
Urban Land ◽  
Demographic Characteristics ◽  
Urban Land Use ◽  
Land Uses ◽  
Land Use Classification ◽  
Residential Areas ◽  
Social Sensing ◽  
Land Use Maps

The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses.

scholarly journals Urban Land-Use Dynamics in the Niger Delta: The Case of Greater Port Harcourt Watershed

Urban Science ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 108 ◽  
Author(s):  
Nimi Dan-Jumbo ◽  
Marc Metzger ◽  
Andrew Clark
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Niger Delta ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Urban Land ◽  
Urban Land Use ◽  
Hard Copy ◽  
Land Use Classification ◽  
Depth Analysis

Cities in developing countries are urbanising at a rapid rate, resulting in substantial pressures on environmental systems. Among the main factors that lead to flooding, controlling land-use change offers the greatest scope for the management of risk. However, traditional analysis of a “from–to” change matrix is not adequate to provide information of all the land-use changes that occur in a watershed. In this study, an in-depth analysis of land-use change enabled us to quantify the bulk of the changes accumulating from swap changes in a tropical watershed. This study assessed the historical and future land-use/land-cover (LULC) dynamics in the River State region of the Niger Delta. Land-use classification and change detection analysis was conducted using multi-source (Landsat TM, ETM, polygon map, and hard copy) data of the study area for 1986, 1995, and 2003, and projected conditions in 2060. The key findings indicate that historical urbanisation was rapid; urban expansion could increase by 80% in 2060 due to planned urban development; and 95% of the conversions to urban land occurred chiefly at the expense of agricultural land. Urban land was dominated by net changes rather than swap changes, which in the future could amplify flood risk and have other severe implications for the watershed.

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