scholarly journals A hierarchical approach for fine-grained urban villages recognition fusing remote and social sensing data

2022 ◽  
Vol 106 ◽  
pp. 102661
Author(s):  
Dongsheng Chen ◽  
Wei Tu ◽  
Rui Cao ◽  
Yatao Zhang ◽  
Biao He ◽  
...  
Keyword(s):  
Hierarchical Approach ◽  
Social Sensing ◽  
Sensing Data ◽  
Fine Grained ◽  
Urban Villages

scholarly journals Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference Around Mainland China via Attention-Augmented CNN from Daytime Satellite Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2067
Author(s):  
Haoyu Liu ◽  
Xianwen He ◽  
Yanbing Bai ◽  
Xing Liu ◽  
Yilin Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Chinese Mainland ◽  
Economic Research ◽  
Official Method ◽  
County Level ◽  
Socioeconomic Indicators ◽  
Sensing Data ◽  
Fine Grained ◽  
The Chinese Mainland

The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research.

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 Social functional mapping of urban green space using remote sensing and social sensing data

2018 ◽  
Vol 146 ◽  
pp. 436-452 ◽  
Author(s):  
Wei Chen ◽  
Huiping Huang ◽  
Jinwei Dong ◽  
Yuan Zhang ◽  
Yichen Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Green Space ◽  
Urban Green Space ◽  
Functional Mapping ◽  
Urban Green ◽  
Social Sensing ◽  
Sensing Data

scholarly journals Quality Utilization Aware Based Data Gathering for Vehicular Communication Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-25 ◽  
Author(s):  
Yingying Ren ◽  
Anfeng Liu ◽  
Ming Zhao ◽  
Changqin Huang ◽  
Tian Wang
Keyword(s):  
Data Collection ◽  
Communication Networks ◽  
Data Gathering ◽  
Quality Data ◽  
Vehicular Communication ◽  
Sensing Data ◽  
Fine Grained ◽  
Vehicular Communication Networks ◽  
The Cost

The vehicular communication networks, which can employ mobile, intelligent sensing devices with participatory sensing to gather data, could be an efficient and economical way to build various applications based on big data. However, high quality data gathering for vehicular communication networks which is urgently needed faces a lot of challenges. So, in this paper, a fine-grained data collection framework is proposed to cope with these new challenges. Different from classical data gathering which concentrates on how to collect enough data to satisfy the requirements of applications, a Quality Utilization Aware Data Gathering (QUADG) scheme is proposed for vehicular communication networks to collect the most appropriate data and to best satisfy the multidimensional requirements (mainly including data gathering quantity, quality, and cost) of application. In QUADG scheme, the data sensing is fine-grained in which the data gathering time and data gathering area are divided into very fine granularity. A metric named “Quality Utilization” (QU) is to quantify the ratio of quality of the collected sensing data to the cost of the system. Three data collection algorithms are proposed. The first algorithm is to ensure that the application which has obtained the specified quantity of sensing data can minimize the cost and maximize data quality by maximizing QU. The second algorithm is to ensure that the application which has obtained two requests of application (the quantity and quality of data collection, or the quantity and cost of data collection) could maximize the QU. The third algorithm is to ensure that the application which aims to satisfy the requirements of quantity, quality, and cost of collected data simultaneously could maximize the QU. Finally, we compare our proposed scheme with the existing schemes via extensive simulations which well justify the effectiveness of our scheme.

scholarly journals Spatial Mismatch between the Supply and Demand of Urban Leisure Services with Multisource Open Data

2020 ◽  
Vol 9 (8) ◽  
pp. 466
Author(s):  
Yue Deng ◽  
Jiping Liu ◽  
An Luo ◽  
Yong Wang ◽  
Shenghua Xu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Evaluation Method ◽  
Spatial Scales ◽  
Supply And Demand ◽  
Open Data ◽  
Spatial Mismatch ◽  
Short Supply ◽  
Social Sensing ◽  
Sensing Data ◽  
Leisure Services

Understanding the balance between the supply and demand of leisure services (LSs) in urban areas can benefit urban spatial planning and improve the quality of life of residents. In cities in developing countries, the pursuit of rapid economic growth has ignored residents’ demand for LSs, thereby leading to a high demand for and short supply of these services. However, due to the lack of relevant research data, few studies have focused on the spatial mismatch in the supply and demand of LSs in urban areas. As typical representatives of multisource geographic data, social sensing data are readily available at various temporal and spatial scales, thus making social sensing data ideal for quantitative urban research. The objectives of this study are to use openly accessible datasets to explore the spatial pattern of the supply and demand of LSs in urban areas and then to depict the relationship between the supply and demand by using correlation analysis. Therefore, taking Beijing, China, as an example, the LS supply index (SI) and societal needs index (SNI) are proposed based on open data to reflect the supply and demand of LSs. The results show that the spatial distribution of the LS supply and demand in Beijing varies with a concentric pattern from the urban center to suburban areas. There is a strong correlation between the supply and demand of commercial and multifunctional services in Chaoyang, Fengtai, Haidian and Shijingshan, but there is no obvious correlation between the supply and demand of ecological and cultural services in Beijing. Especially in Dongcheng and Xicheng, there is no obvious correlation between the supply and demand of all services. The proposed approach provides an effective urban LS supply and demand evaluation method. In addition, the research results can provide a reference for the construction of “happy cities” in China.

scholarly journals A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions

2020 ◽  
Vol 12 (6) ◽  
pp. 1032
Author(s):  
Shengyu Xu ◽  
Linbo Qing ◽  
Longmei Han ◽  
Mei Liu ◽  
Yonghong Peng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Image Data ◽  
Urban Region ◽  
Remote Sensing Images ◽  
Social Sensing ◽  
Sensing Data ◽  
Functional Regions ◽  
Point Of Interest ◽  
Wide Range

For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas.

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