scholarly journals Urban Functional Zone Recognition Integrating Multisource Geographic Data

2021 ◽  
Vol 13 (23) ◽  
pp. 4732
Author(s):  
Siya Chen ◽  
Hongyan Zhang ◽  
Hangxing Yang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Comprehensive Evaluation ◽  
Development Trend ◽  
Data Sources ◽  
Functional Zone ◽  
Geographic Data ◽  
Functional Zones ◽  
Nighttime Light ◽  
Nighttime Light Imagery

As the basic spatial unit of urban planning and management, it is necessary to understand the real development trend of urban functional zones in time and carry out reasonable planning adjustment. Because of the complexity of urban functional zones, the automatic recognition of urban functional zones has become a significant scientific problem in urban research. Urban functional zones contain natural and socioeconomic characteristics, but the existing identification methods fail to comprehensively consider these features. This paper proposes a framework that integrates multisource geographic data to recognize urban functional zone. We used high-resolution remote sensing imagery, point-of-interest (POI) data and high-spatial-resolution nighttime light imagery to extract both natural and socioeconomic features for urban functional zone accurate interpretation. Various features provide more accurate and comprehensive description for complex urban functional zone, so as to improve the recognition accuracy of urban functional zone. At present, there are few studies on urban functional zone recognition based on the combination of high-resolution remote sensing image, POI and high-resolution nighttime light imagery. The application potential of the combination of these three geographical data sources in urban function zone recognition needs to be explored. The experimental results show that the accuracy of urban functional zone recognition was obviously improved by the three data sources combination, the overall accuracy reached 80.30% and a comprehensive evaluation index reached 68.26%. This illustrate that the combination of the three data sources is beneficial to the urban functional zone recognition.

scholarly journals Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach

2019 ◽  
Vol 11 (16) ◽  
pp. 1902 ◽  
Author(s):  
Shouji Du ◽  
Shihong Du ◽  
Bo Liu ◽  
Xiuyuan Zhang
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Satellite Images ◽  
Nearest Neighbor ◽  
Context Information ◽  
Functional Zone ◽  
High Resolution Satellite Images ◽  
Segmentation Approach ◽  
Functional Zones ◽  
Very High

Urban functional-zone (UFZ) analysis has been widely used in many applications, including urban environment evaluation, and urban planning and management. How to extract UFZs’ spatial units which delineates UFZs’ boundaries is fundamental to urban applications, but it is still unresolved. In this study, an automatic, context-enabled multiscale image segmentation method is proposed for extracting spatial units of UFZs from very-high-resolution satellite images. First, a window independent context feature is calculated to measure context information in the form of geographic nearest-neighbor distance from a pixel to different image classes. Second, a scale-adaptive approach is proposed to determine appropriate scales for each UFZ in terms of its context information and generate the initial UFZs. Finally, the graph cuts algorithm is improved to optimize the initial UFZs. Two datasets including WorldView-2 image in Beijing and GaoFen-2 image in Nanchang are used to evaluate the proposed method. The results indicate that the proposed method can generate better results from very-high-resolution satellite images than widely used approaches like image tiles and road blocks in representing UFZs. In addition, the proposed method outperforms existing methods in both segmentation quality and running time. Therefore, the proposed method appears to be promising and practical for segmenting large-scale UFZs.

scholarly journals Detection of County Economic Development Using LJ1-01 Nighttime Light Imagery: A Comparison with NPP-VIIRS Data

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6633
Author(s):  
Hongliang Liu ◽  
Nianxue Luo ◽  
Chunchun Hu
Keyword(s):  
Remote Sensing ◽  
Economic Development ◽  
Remote Sensing Data ◽  
County Level ◽  
Socioeconomic Indicators ◽  
Regression Methods ◽  
Public Resources ◽  
The Status ◽  
Nighttime Light ◽  
Nighttime Light Imagery

Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicators at the national and regional scales to study regional economic development. However, most previous studies only chose a single measurement indicator (such as GDP) and adopted simple regression methods to investigate the economic development of a certain area based on DMSP-OLS or NPP-VIIRS stable NTL data. The status quo shows the problems of using a single evaluation index—it has a low evaluation precision. The LJ1-01 satellite is the first dedicated NTL remote sensing satellite in the world, launched in July 2018. The data provided by LJ1-01 have a higher spatial resolution and fewer blooming phenomena. In this paper, we compared the accuracy of the LJ1-01 data and NPP-VIIRS data in detecting county-level multidimensional economic development. In three provinces in China, namely, Hubei, Hunan and Jiangxi, 20 socioeconomic parameters were selected from the following five perspectives: economic conditions, people’s livelihood, social development, public resources and natural vulnerability. Then, a County-level Economic Index (CEI) was constructed to evaluate the level of multidimensional economic development, with the spatial pattern of the multidimensional economic development also identified across the study area. The present study adopted the random forest (RF) and linear regression (LR) algorithms to establish the regression model individually, and the results were evaluated by cross-validation. The results show that the RF algorithm greatly improves the accuracy of the model compared with the LR algorithm, and thus is suitable for the study of NTL data. In addition, a better determinate coefficient (R2) based on the LJ1-01 data (0.8168) was obtained than that from the NPP-VIIRS data (0.7245) in the RF model, which reflects that the LJ1-01 data offer better potential in the evaluation of socioeconomic parameters and can be used to identify, both accurately and efficiently, multidimensional economic development at the county level.

scholarly journals Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 data

2020 ◽  
Vol 12 (1) ◽  
pp. 1169-1184
Author(s):  
Liang Zhong ◽  
Xiaosheng Liu ◽  
Peng Yang ◽  
Rizhi Lin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Infrared Imaging ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Application Potential ◽  
Nighttime Light

AbstractNighttime light remote sensing images show significant application potential in marine ship monitoring, but in areas where ships are densely distributed, the detection accuracy of the current methods is still limited. This article considered the LJ1-01 data as an example, compared with the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data, and explored the application of high-resolution nighttime light images in marine ship detection. The radiation values of the aforementioned two images were corrected to achieve consistency, and the interference light sources of the ship light were filtered. Then, when the threshold segmentation and two-parameter constant false alarm rate methods are combined, the ships’ location information was with obtained, and the reliability of the results was analyzed. The results show that the LJ1-01 data can not only record more potential ship light but also distinguish the ship light and background noise in the data. The detection accuracy of the LJ1-01 data in both ship detection methods is significantly higher than that of the NPP/VIIRS data. This study analyzes the characteristics, performance, and application potential of the high-resolution nighttime light data in the detection of marine vessels. The relevant results can provide a reference for the high-precision monitoring of nighttime marine ships.

scholarly journals RELATIONSHIP MODEL BETWEEN NIGHTLIGHT DATA AND FLOOR AREA RATIO FROM HIGH RESOLUTION IMAGES

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1419-1422
Author(s):  
M. Yan ◽  
L. Xu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Development Trend ◽  
Area Ratio ◽  
Remote Sensing Images ◽  
Floor Area ◽  
Floor Area Ratio ◽  
Economic Information ◽  
Relationship Model ◽  
High Resolution Images

It is a hotpot that extraction the floor area ratio from high resolution remote sensing images. It is a development trend of using nightlight data to survey the urban social and economic information. This document aims to provide a conference relationship model for VIIRS/NPP nightlight data and floor Area Ratio from High Resolution ZY-3 Images. It shows that there is a lineal relationship between the shadow and the floor area ratio, and the R<sup>2</sup> is 0.98. It shows that there is a quadratic polynomial relationship between the floor area ratio and the nightlight, and the R<sup>2</sup> is 0.611. We can get a conclusion that, VIIRS/NPP nightlights data may show the floor area ratio in an extent at level of administrative street.

scholarly journals DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1088
Author(s):  
Hanqing Bao ◽  
Dongping Ming ◽  
Ya Guo ◽  
Kui Zhang ◽  
Keqi Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Social Sensing ◽  
Sensing Data ◽  
Functional Zone ◽  
Functional Zones

The urban functional zone, as a special fundamental unit of the city, helps to understand the complex interaction between human space activities and environmental changes. Based on the recognition of physical and social semantics of buildings, combining remote sensing data and social sensing data is an effective way to quickly and accurately comprehend urban functional zone patterns. From the object level, this paper proposes a novel object-wise recognition strategy based on very high spatial resolution images (VHSRI) and social sensing data. First, buildings are extracted according to the physical semantics of objects; second, remote sensing and point of interest (POI) data are combined to comprehend the spatial distribution and functional semantics in the social function context; finally, urban functional zones are recognized and determined by building with physical and social functional semantics. When it comes to building geometrical information extraction, this paper, given the importance of building boundary information, introduces the deeper edge feature map (DEFM) into the segmentation and classification, and improves the result of building boundary recognition. Given the difficulty in understanding deeper semantics and spatial information and the limitation of traditional convolutional neural network (CNN) models in feature extraction, we propose the Deeper-Feature Convolutional Neural Network (DFCNN), which is able to extract more and deeper features for building semantic recognition. Experimental results conducted on a Google Earth image of Shenzhen City show that the proposed method and model are able to effectively, quickly, and accurately recognize urban functional zones by combining building physical semantics and social functional semantics, and are able to ensure the accuracy of urban functional zone recognition.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market

Groundwater abstraction across the Indo-Gangetic basin (2010)

2017 ◽  
Author(s):  
Gabin Archambault
Keyword(s):  
High Resolution ◽  
Data Sources ◽  
Geological Survey ◽  
Groundwater Abstraction ◽  
British Geological Survey ◽  
Full List

This 1 km resolution grid shows the estimated mean annual groundwater abstraction in millimeters across the Indo-Gangetic basin based on data from 2010. Methodology and a full list of data sources used can be found in the peer-reviewed paper: https://www.nature.com/articles/ngeo2791.epdf?author_access_token=_2Z_fJZxRkSVmgVJ7xHTVdRgN0jAjWel9jnR3ZoTv0O07GfIlzqIVm44UgFPb1r62_FUJLao4zkJSzYpv-4gIWJorRXEpgh4iarB8vlRNY_tGV_18CAf2j-_GnADYbdp The raster and a high resolution PDF file are available for download on the website of British Geological Survey (BGS): http://www.bgs.ac.uk/research/groundwater/international/SEAsiaGroundwater/mapsDownload.html Abstraction Groundwater Stress

Groundwater storage in Africa

2017 ◽  
Author(s):  
Gabin Archambault
Keyword(s):  
High Resolution ◽  
Effective Porosity ◽  
Data Sources ◽  
Groundwater Storage ◽  
British Geological Survey ◽  
Full List ◽  
The World ◽  
Storage Type ◽  
Porosity Range ◽  
Aquifer Flow

This 5 km resolution grid presents groundwater storage in Africa (in mm). This parameter was estimated by combining the saturated aquifer thickness and effective porosity of aquifers across Africa. For each aquifer flow/storage type an effective porosity range was assigned based on a series of studies across Africa and surrogates in other parts of the world. Groundwater storage is given in millimeters. Detailed description of the methodology, and a full list of data sources used to develop the layer can be found in the peer-reviewed paper available here: http://iopscience.iop.org/article/10.1088/1748-9326/7/2/024009/pdf The raster and a high resolution PDF file are available for download on the website of British Geological Survey (BGS): http://www.bgs.ac.uk/research/groundwater/international/africanGroundwater/mapsDownload.html Groundwater Storage

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