scholarly journals The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China

2017 ◽  
Vol 9 (9) ◽  
pp. 865 ◽  
Author(s):  
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◽  
◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Land ◽  
Urban Land Use ◽  
Social Sensing ◽  
Sensing Data ◽  
Fine Grained ◽  
Combined Use ◽  
Beijing China

scholarly journals Urban Residential Land Suitability Analysis Combining Remote Sensing and Social Sensing Data: A Case Study in Beijing, China

2019 ◽  
Vol 11 (8) ◽  
pp. 2255 ◽  
Author(s):  
Huiping Huang ◽  
Qiangzi Li ◽  
Yuan Zhang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Suitability ◽  
Residential Land ◽  
Social Sensing ◽  
Sensing Data ◽  
Beijing China

With the degradation of the environment and the acceleration of urbanization, urban residential land has been undergoing rapid changes and has attracted great attention worldwide. Meanwhile, the quantitative evaluation of the suitability of urban residential land is essential for a better and more powerful understanding of urban residential land planning and improvement. Most urban land suitability studies rely solely on remote sensing data and GIS data to evaluate natural suitability, and few studies have focused on urban land suitability from a socioeconomic perspective. Consequently, this paper integrates remote sensing data (GaoFen-2 satellite image) and social sensing data (Tencent User Density data, Point-of-interest data and OpenStreetMap data) to establish an evaluation framework for analyzing the suitability of urban residential land in the Haidian District, Beijing, China, in which, ecological comfortability, locational livability and overall suitability were evaluated according to five attributes extracted from urban residential land via the factor analysis method. The evaluation results of this case study show that, compared with the suburban area in the northwest, the urban area tends to have lower ecological comfortability and higher locational livability. The overall suitability increases from southeast to northwest, consistent with the spatial distribution of ecological comfortability. This framework can potentially assist with the sustainable development of residential lands and urban land use planning.

scholarly journals Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3597
Author(s):  
Xuanyan Dong ◽  
Yue Xu ◽  
Leping Huang ◽  
Zhigang Liu ◽  
Yi Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Land ◽  
Urban Land Use ◽  
Remote Sensing Images ◽  
Social Sensing ◽  
Sensing Data ◽  
Object Based ◽  
Land Use Types ◽  
Functional Areas

The ability to precisely map urban land use types can significantly aid urban planning and urban system understanding. In recent years, remote sensing images and social sensing data have been frequently used for urban land use mapping. However, there still remains a problem: what is the best basic unit for fusing remote sensing images with social sensing data? The aim of this study is to explore the impact of spatial units on urban land use mapping, with remote sensing images and social sensing data of Shenzhen City, China. Three different basic units were first applied to delineate urban land use types, and for each unit, a word dictionary was built by fusing natural–physical features from high spatial resolution (HSR) remote sensing images and the socioeconomic semantic features from point of interest (POI) data. The latent Dirichlet allocation (LDA) algorithm and random forest methods were then applied to map the land use of the Futian district—the core region of Shenzhen. The experiment demonstrates that: (1) No matter what kind of spatial unit, it is beneficial to fuse multisource data to improve the performance. However, when using different spatial units, the importances of features are different. (2) Using block-based spatial units results in the final map looking the best. However, a great challenge of this approach is that the scale is too coarse to handle mixed functional areas. (3) Using grid- and object-based units, the problem of mixed functional areas can be better solved. Additionally, the object-based land use map looks better from our visual interpretation. Accordingly, the results of this study could give other researchers references and advice for future studies.

scholarly journals LAND USE CLASSIFICATION FROM COMBINED USE OF REMOTE SENSING AND SOCIAL SENSING DATA

2018 ◽  
Vol XLII-4 ◽  
pp. 33-39 ◽  
Author(s):  
A. S. Anugraha ◽  
H.-J. Chu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Human Behavior ◽  
Remote Sensing Data ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Classification ◽  
Social Sensing ◽  
Sensing Data ◽  
The City

<p><strong>Abstract.</strong> Large amounts of data can be sensed and analyzed to discover patterns of human behavior in cities for the benefit of urban authorities and citizens, especially in the areas of traffic forecasting, urban planning, and social science. In New York, USA, social sensing, remote sensing, and urban land use information support the discovery of patterns of human behavior. This research uses two types of openly accessible data, namely, social sensing data and remote sensing data. Bike and taxi data are examples of social sensing data, whereas sentinel remote sensed imagery is an example of remote sensing data. This research aims to sense and analyze the patterns of human behavior and to classify land use from the combination of remote sensing data and social sensing data. A decision tree is used for land use classification. Bike and taxi density maps are generated to show the locations of people around the city during the two peak times. On the basis of a geographic information system, the maps also reflect the residential and office areas in the city. The overall accuracy of land use classification after the consideration of social sensing data is 85.3%. The accuracy assessment shows that the combination of remote sensing data and social sensing data facilitates accurate urban land use classification.</p>

scholarly journals An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3254
Author(s):  
Zhou Huang ◽  
Houji Qi ◽  
Chaogui Kang ◽  
Yuelong Su ◽  
Yu Liu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Ensemble Learning ◽  
Urban Land ◽  
Urban Land Use ◽  
Learning Approach ◽  
Land Use Classification ◽  
Remote Sensing Imagery ◽  
Sensing Data

Urban land use mapping is crucial for effective urban management and planning due to the rapid change of urban processes. State-of-the-art approaches rely heavily on the socioeconomic, topographical, infrastructural and land cover information of urban environments via feeding them into ad hoc classifiers for land use classification. Yet, the major challenge lies in the lack of a universal and reliable approach for the extraction and combination of physical and socioeconomic features derived from remote sensing imagery and social sensing data. This article proposes an ensemble-learning-approach-based solution of integrating a rich body of features derived from high resolution satellite images, street-view images, building footprints, points-of-interest (POIs) and social media check-ins for the urban land use mapping task. The proposed approach can statistically differentiate the importance of input feature variables and provides a good explanation for the relationships between land cover, socioeconomic activities and land use categories. We apply the proposed method to infer the land use distribution in fine-grained spatial granularity within the Fifth Ring Road of Beijing and achieve an average classification accuracy of 74.2% over nine typical land use types. The results also indicate that our model outperforms several alternative models that have been widely utilized as baselines for land use classification.

Urban land use mapping and zoning of Bombay Metropolitan Region using remote sensing data

1989 ◽  
Vol 17 (3) ◽  
pp. 11-22 ◽  
Author(s):  
S. K. Pathan ◽  
P. Jothimahi ◽  
D. Sampat Kumar ◽  
S. P. Pendharkar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Urban Land ◽  
Urban Land Use ◽  
Metropolitan Region ◽  
Sensing Data

scholarly journals Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics

2018 ◽  
Vol 7 (7) ◽  
pp. 246 ◽  
Author(s):  
Taïs Grippa ◽  
Stefanos Georganos ◽  
Soukaina Zarougui ◽  
Pauline Bognounou ◽  
Eric Diboulo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Urban Land ◽  
Urban Land Use ◽  
Spatial Metrics ◽  
Sensing Data ◽  
Block Level
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