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Urban Land Use Mapping and Analysis in the Big Data Era

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 84681

Special Issue Editors

Prof. Dr. Peng Gong

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Guest Editor
Department of Earth System Science, Tsinghua University, Beijing, China
Interests: land cover and land-use mapping; environmental change; public health; sustainable development; remote sensing
Prof. Dr. Shihong Du

E-Mail Website
Guest Editor
School of Earth and Space Sciences, Peking University, Beijing 100871, China
Interests: image classification; spatial analysis; deep learning; sample learning; urban landscape
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xin Huang

grade E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: high spatial and hyperspectral remote sensing image processing methods and applications
Special Issues, Collections and Topics in MDPI journals
Dr. Chong Liu

E-Mail
Guest Editor
School of Geography and Environment, Jiangxi Normal University, Nanchang, China
Interests: time series remote sensing; environmental change monitoring

Special Issue Information

Dear Colleagues,

Cities are focal points for economic, social, cultural, and recreational activities. Land use is the platform of these various human activities. The configuration and patterns of land use in cities are the basis for urban planning and management, disaster reduction, sustainable development, and human well-being. However, on-time, large-scale, frequent land-use information has been difficult to obtain in most cities due to the diversity and variation of land-use patterns, the complexity of urban scenes, and the high labor and cost involved. The rapid developments in remote sensing technologies, ground-based and wearable devices have greatly expanded our capability in the acquisition of data related to the urban environment and activities of citizens. Richer and richer volumes of data are becoming available, including remotely sensed images, social media, check-in records, taxi trajectory, videos, and street view images, as well as in-situ survey and census data. These have constituted the ever-expanding big data in urban areas. However, how to extract economic, social, cultural, environmental, and recreational information from these data and combine the extracted information to derive information on urban land-use is a problem that requires better solutions. The big data collected in cities are heterogeneous in data formats, spatial scales, temporal scales, and semantic granularity, and have complicated relationships with various economic, social, cultural, environmental, and other human-related factors. Thus, it has become a challenge to map and analyze urban land-use patterns using big data.

This Special Issue calls for innovative fusion and analysis techniques for mapping urban land-use patterns with a specific focus on the use of big data. The topics include, but not limited to, the following:

  • Urban big data processing, analysis, and management
  • Methods for urban environmental information extraction, including economic, social, cultural, environmental, and other human-related factors
  • 4D urban information collection and processing
  • Data mining and machine learning methods for data fusion
  • Time-series land-cover and land-use mapping in urban areas
  • Scale, transferability, and sample issues in urban land-use mapping
  • Urban land-use configuration and pattern analysis
  • Change detection and dynamic analysis of urban land cover and land-use
  • Urban land-use modeling
  • Applications of urban land-use information and technologies in support of urban sustainable development, improving the living quality and human well-being of urban citizens.

Prof. Dr. Peng Gong
Prof. Dr. Shihong Du
Prof. Dr. Xin Huang
Dr. Chong Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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22 pages, 18121 KiB  
Article
Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method
by Xiaoting Li, Tengyun Hu, Peng Gong, Shihong Du, Bin Chen, Xuecao Li and Qi Dai
Remote Sens. 2021, 13(3), 477; https://doi.org/10.3390/rs13030477 - 29 Jan 2021
Cited by 20 | Viewed by 4648
Abstract
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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19 pages, 6289 KiB  
Article
Mapping an Urban Boundary Based on Multi-Temporal Sentinel-2 and POI Data: A Case Study of Zhengzhou City
by Zhe Wang, Haiying Wang, Fen Qin, Zhigang Han and Changhong Miao
Remote Sens. 2020, 12(24), 4103; https://doi.org/10.3390/rs12244103 - 16 Dec 2020
Cited by 10 | Viewed by 2872
Abstract
Accurately identifying and delineating urban boundaries are the premise for and foundation of the control of disorderly urban sprawl, which is helpful for us to accurately grasp the scale and form of cities, optimize the internal spatial structure and pattern of cities, and [...] Read more.
Accurately identifying and delineating urban boundaries are the premise for and foundation of the control of disorderly urban sprawl, which is helpful for us to accurately grasp the scale and form of cities, optimize the internal spatial structure and pattern of cities, and guide the expansion of urban spaces in the future. At present, the concept and delineation of urban boundaries do not follow a unified method or standard. However, many scholars have made use of multi-source remote sensing images of various scales and social auxiliary data such as point of interest (POI) data to achieve large-scale, high-resolution, and high-precision land cover mapping and impermeable water surface mapping. The accuracy of small- and medium-scale urban boundary mapping has not been improved to an obvious extent. This study uses multi-temporal Sentinel-2 high-resolution images and POI data that can reflect detailed features of human activities to extract multi-dimensional features and use random forests and mathematical morphology to map the urban boundaries of the city of Zhengzhou. The research results show that: (1) the urban construction land extraction model established with multi-dimensional features has a great improvement in accuracy; (2) when the training sample accounts for 65% of the sample data set, the urban construction land extraction model has the highest accuracy, reaching 96.25%, and the Kappa coefficient is 0.93; (3) the optimized boundary of structural elements with a size of 13 × 13 is selected, which is in good agreement in terms of scope and location with the boundary of FROM-GLC10 (Zhengzhou) and visual interpretations. The results from the urban boundary delineation in this paper can be used as an important database for detailed basic land use mapping within cities. Moreover, the method in this paper has some reference value for other cities in terms of delineating urban boundaries. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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23 pages, 8236 KiB  
Article
Analysing the Driving Forces and Environmental Effects of Urban Expansion by Mapping the Speed and Acceleration of Built-Up Areas in China between 1978 and 2017
by Lan Wang, Yinghui Jia, Xinhu Li and Peng Gong
Remote Sens. 2020, 12(23), 3929; https://doi.org/10.3390/rs12233929 - 30 Nov 2020
Cited by 16 | Viewed by 2505
Abstract
Abundant data sets produced from long-term series of high-resolution remote sensing data have made it possible to explore urban issues across different spatiotemporal scales. Based on a 40-year impervious area data set released by Tsinghua University, a method was developed to map the [...] Read more.
Abundant data sets produced from long-term series of high-resolution remote sensing data have made it possible to explore urban issues across different spatiotemporal scales. Based on a 40-year impervious area data set released by Tsinghua University, a method was developed to map the speed and acceleration of urban built-up areas. With the mapping results of the two indices, we characterised the spatiotemporal dynamics of built-up area expansion and captured different types of expansion. Combined with socioeconomic data, we examined the temporal changes and spatial heterogeneity of driving forces with an ordinary least square (OLS) model and a panel data model, as well as exploring the environmental effects of the expansion. Our results reveal that China has experienced drastic urban expansion over the last four decades. Among all cities, megacities and large cities in eastern China, as well as megacities in central and northeast China have experienced the most dramatic urban expansion. A growing number of cities are categorised as thriving, which means that they have both high expansion speed and acceleration. The overall driving force of urban expansion has significantly increased. More specifically, it was associated with population increase in the early stages; however, since 2000, it has been substantially associated with increases in GDP and fixed asset investments. The major driving factors also differ between regions and urban sizes. Urban expansion is identified as being closely associated with environmental deterioration; thus, speed and acceleration should be included as key indicators in exploring the environmental effects of urban expansion. In summary, the results of the presented case study, based on a data set of China, indicate that speed and acceleration are useful in analysing the driving forces of urban expansion and its environmental effects, and may generate more interest in related research. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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26 pages, 12276 KiB  
Article
Updating of Land Cover Maps and Change Analysis Using GlobeLand30 Product: A Case Study in Shanghai Metropolitan Area, China
by Haiyan Pan, Xiaohua Tong, Xiong Xu, Xin Luo, Yanmin Jin, Huan Xie and Binbin Li
Remote Sens. 2020, 12(19), 3147; https://doi.org/10.3390/rs12193147 - 24 Sep 2020
Cited by 10 | Viewed by 3123
Abstract
Accurate land cover mapping and change analysis is essential for natural resource management and ecosystem monitoring. GlobeLand30 is a global land cover product from China with 30 m resolution that provides reliable data for many international scientific programs. Few studies have focused on [...] Read more.
Accurate land cover mapping and change analysis is essential for natural resource management and ecosystem monitoring. GlobeLand30 is a global land cover product from China with 30 m resolution that provides reliable data for many international scientific programs. Few studies have focused on systematically implementing this global land cover product in regional studies. Therefore, this paper presents an object-based extended change vector analysis (ECVA_OB) and transfer learning method to update the reginal land cover map using GlobeLand30 product. The method is designed to highlight small and subtle changes through the concept of uncertain area analysis. Updating is carried out by classifying changed objects using a change-detection-based transfer learning method. Land cover changes are analyzed and the factors affecting updating results are explored. The method was tested with data from Shanghai, China, a city that has experienced significant changes in the past decade. The experimental results show that: (1) the change detection and classification accuracy of the proposed method are 83.30% and 78.77%, respectively, which are significantly better than the values obtained for the multithreshold change vector analysis (MCVA) and the multithreshold change vector analysis and support vector machine (MCVA + SVM) methods; (2) the updated results agree well with GlobeLand30 2010, especially for cultivated land and artificial surfaces, indicating the effectiveness of the proposed method; (3) the most significant changes over the past decade in Shanghai were from cultivated land to artificial surfaces, and the total area containing artificial surfaces in Shanghai increased by about 55% from 2000 to 2011. The factors affecting the updating results are also discussed, which be attributed to the classification accuracy of the base image, extended change vector analysis, and object-based image analysis. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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21 pages, 15598 KiB  
Article
Recognition of Urban Functions and Mixed Use Based on Residents’ Movement and Topic Generation Model: The Case of Wuhan, China
by Haifu Cui, Liang Wu, Sheng Hu, Rujuan Lu and Shanlin Wang
Remote Sens. 2020, 12(18), 2889; https://doi.org/10.3390/rs12182889 - 6 Sep 2020
Cited by 14 | Viewed by 3837
Abstract
The rapid evolution of cities has brought new challenges to urban planning and management. The accurate evaluation of urban functional structure and mixed use is critical, especially at a fine scale such as by blocks. The composition and mixing of urban spatial functions [...] Read more.
The rapid evolution of cities has brought new challenges to urban planning and management. The accurate evaluation of urban functional structure and mixed use is critical, especially at a fine scale such as by blocks. The composition and mixing of urban spatial functions calculated by remote sensing and statistics are non-quantitative and undetailed. The text topic models are often applied to process text data, but are rarely used to mine semantic information in quantitative data. Therefore, this paper attempts to carry out research on the recognition of urban functions and mixed use using a text topic generation model based on resident mobile data. First, the area within Wuhan Third Ring Road was divided into 2451 units at a grid size of 500 m × 500 m. The histogram-latent Dirichlet allocation (H-LDA) and information entropy were applied to assign different grid units to correct the functional topics and topic information entropy (TIE). Second, the functional categories of different analysis units were calculated using the point of interest (POI), frequency density (FD) and category proportion (CP) indicators, while the functional information entropy (FIE) based on the POI was calculated. Then, the urban functions and mixtures identified by the two kinds of data were compared and analyzed. Finally, referring to the geographic information in streetscape map and applying correlation analysis, the function and mixing results obtained from the experiment were verified. Studies have shown that the H-LDA model can identify bridges, which the POI data have shown is challenging to identify without attributes such as length. The function recognition accuracy of the H-LDA model is 89.3%, which is higher than K-means algorithm and Word2vec models. The correlation coefficient between FIE and TIE is 0.587, indicating that both are highly correlated. These explain the accuracy and rationality of identifying city functions and mixtures based on the H-LDA model. The H-LDA model can be applied to functional computing and fine-scale urban mixed function planning. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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17 pages, 6582 KiB  
Article
Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China
by Wanliu Mao, Debin Lu, Li Hou, Xue Liu and Wenze Yue
Remote Sens. 2020, 12(17), 2817; https://doi.org/10.3390/rs12172817 - 31 Aug 2020
Cited by 42 | Viewed by 5423
Abstract
Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of [...] Read more.
Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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18 pages, 17068 KiB  
Article
Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China
by Wei Chen, Yuyu Zhou, Qiusheng Wu, Gang Chen, Xin Huang and Bailang Yu
Remote Sens. 2020, 12(17), 2805; https://doi.org/10.3390/rs12172805 - 29 Aug 2020
Cited by 25 | Viewed by 6602
Abstract
The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we [...] Read more.
The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we proposed a framework to derive the information of building type using geospatial data, including point-of-interest (POI) data, building footprints, land use polygons, and roads, from Gaode and Baidu Maps. First, we used natural language processing (NLP)-based approaches (i.e., text similarity measurement and topic modeling) to automatically reclassify POI categories into which can be used to directly infer building types. Second, based on the relationship between building footprints and POIs, we identified building types using two indicators of type ratio and area ratio. The proposed framework was tested using over 440,000 building footprints in Beijing, China. Our NLP-based approaches and building type identification methods show overall accuracies of 89.0% and 78.2%, and kappa coefficient of 0.83 and 0.71, respectively. The proposed framework is transferrable to other China cities for deriving the information of building types from web mapping platforms. The data products generated from this study are of great use for quantitative urban studies at the building level. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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16 pages, 2602 KiB  
Article
Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017
by Jie Zhang, Le Yu, Xuecao Li, Chenchen Zhang, Tiezhu Shi, Xiangyin Wu, Chao Yang, Wenxiu Gao, Qingquan Li and Guofeng Wu
Remote Sens. 2020, 12(16), 2615; https://doi.org/10.3390/rs12162615 - 13 Aug 2020
Cited by 46 | Viewed by 5747
Abstract
The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the largest bay areas in the world. However, the spatiotemporal characteristics and driving mechanisms of urban expansions in this region are poorly understood. Here we used the annual remote sensing images, [...] Read more.
The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the largest bay areas in the world. However, the spatiotemporal characteristics and driving mechanisms of urban expansions in this region are poorly understood. Here we used the annual remote sensing images, Geographic Information System (GIS) techniques, and geographical detector method to characterize the spatiotemporal patterns of urban expansion in the GBA and investigate their driving factors during 1986–2017 on regional and city scales. The results showed that: the GBA experienced an unprecedented urban expansion over the past 32 years. The total urban area expanded from 652.74 km2 to 8137.09 km2 from 1986 to 2017 (approximately 13 times). The annual growth rate during 1986–2017 was 8.20% and the annual growth rate from 1986 to 1990 was the highest (16.89%). Guangzhou, Foshan, Dongguan, and Shenzhen experienced the highest urban expansion rate, with the annual increase of urban areas in 51.51, 45.54, 36.76, and 23.26 km2 y−1, respectively, during 1986–2017. Gross Domestic Product (GDP), income, road length, and population were the most important driving factors of the urban expansions in the GBA. We also found the driving factors of the urban expansions varied with spatial and temporal scales, suggesting the general understanding from the regional level may not reveal detailed urban dynamics. Detailed urban management and planning policies should be made considering the spatial and internal heterogeneity. These findings can enhance the comprehensive understanding of this bay area and help policymakers to promote sustainable development in the future. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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19 pages, 5838 KiB  
Article
Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data
by Shouzhi Chang, Zongming Wang, Dehua Mao, Kehan Guan, Mingming Jia and Chaoqun Chen
Remote Sens. 2020, 12(15), 2488; https://doi.org/10.3390/rs12152488 - 3 Aug 2020
Cited by 34 | Viewed by 5052
Abstract
Understanding urban spatial pattern of land use is of great significance to urban land management and resource allocation. Urban space has strong heterogeneity, and thus there were many researches focusing on the identification of urban land use. The emergence of multiple new types [...] Read more.
Understanding urban spatial pattern of land use is of great significance to urban land management and resource allocation. Urban space has strong heterogeneity, and thus there were many researches focusing on the identification of urban land use. The emergence of multiple new types of geospatial data provide an opportunity to investigate the methods of mapping essential urban land use. The popularization of street view images represented by Baidu Maps is benificial to the rapid acquisition of high-precision street view data, which has attracted the attention of scholars in the field of urban research. In this study, OpenStreetMap (OSM) was used to delineate parcels which were recognized as basic mapping units. A semantic segmentation of street view images was combined to enrich the multi-dimensional description of urban parcels, together with point of interest (POI), Sentinel-2A, and Luojia-1 nighttime light data. Furthermore, random forest (RF) was applied to determine the urban land use categories. The results show that street view elements are related to urban land use in the perspective of spatial distribution. It is reasonable and feasible to describe urban parcels according to the characteristics of street view elements. Due to the participation of street view, the overall accuracy reaches 79.13%. The contribution of street view features to the optimal classification model reached 20.6%, which is more stable than POI features. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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17 pages, 3736 KiB  
Article
A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity
by Red Willow Coleman, Natasha Stavros, Vineet Yadav and Nicholas Parazoo
Remote Sens. 2020, 12(15), 2399; https://doi.org/10.3390/rs12152399 - 26 Jul 2020
Cited by 11 | Viewed by 4504
Abstract
High spatial resolution maps of Los Angeles, California are needed to capture the heterogeneity of urban land cover while spanning the regional domain used in carbon and water cycle models. We present a simplified framework for developing a high spatial resolution map of [...] Read more.
High spatial resolution maps of Los Angeles, California are needed to capture the heterogeneity of urban land cover while spanning the regional domain used in carbon and water cycle models. We present a simplified framework for developing a high spatial resolution map of urban vegetation cover in the Southern California Air Basin (SoCAB) with publicly available satellite imagery. This method uses Sentinel-2 (10–60 × 10–60 m) and National Agriculture Imagery Program (NAIP) (0.6 × 0.6 m) optical imagery to classify urban and non-urban areas of impervious surface, tree, grass, shrub, bare soil/non-photosynthetic vegetation, and water. Our approach was designed for Los Angeles, a geographically complex megacity characterized by diverse Mediterranean land cover and a mix of high-rise buildings and topographic features that produce strong shadow effects. We show that a combined NAIP and Sentinel-2 classification reduces misclassified shadow pixels and resolves spatially heterogeneous vegetation gradients across urban and non-urban regions in SoCAB at 0.6–10 m resolution with 85% overall accuracy and 88% weighted overall accuracy. Results from this study will enable the long-term monitoring of land cover change associated with urbanization and quantification of biospheric contributions to carbon and water cycling in cities. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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18 pages, 7249 KiB  
Article
Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data
by Jing Sun, Hong Wang, Zhenglin Song, Jinbo Lu, Pengyu Meng and Shuhong Qin
Remote Sens. 2020, 12(15), 2386; https://doi.org/10.3390/rs12152386 - 24 Jul 2020
Cited by 23 | Viewed by 3802
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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31 pages, 100263 KiB  
Article
Detection, Classification and Boundary Regularization of Buildings in Satellite Imagery Using Faster Edge Region Convolutional Neural Networks
by Kinga Reda and Michal Kedzierski
Remote Sens. 2020, 12(14), 2240; https://doi.org/10.3390/rs12142240 - 13 Jul 2020
Cited by 20 | Viewed by 7735
Abstract
With the development of effective deep learning algorithms, it became possible to achieve high accuracy when conducting remote sensing analyses on very high-resolution images (VHRS), especially in the context of building detection and classification. In this article, in order to improve the accuracy [...] Read more.
With the development of effective deep learning algorithms, it became possible to achieve high accuracy when conducting remote sensing analyses on very high-resolution images (VHRS), especially in the context of building detection and classification. In this article, in order to improve the accuracy of building detection and classification, we propose a Faster Edge Region Convolutional Neural Networks (FER-CNN) algorithm. This proposed algorithm is trained and evaluated on different datasets. In addition, we propose a new method to improve the detection of the boundaries of detected buildings. The results of our algorithm are compared with those of other methods, such as classical Faster Region Convolution Neural Network (Faster R-CNN) with the original VGG16 and the Single-Shot Multibox Detector (SSD). The experimental results show that our methods make it possible to obtain an average detection accuracy of 97.5% with a false positive classification rate of 8.4%. An additional advantage of our method is better resistance to shadows, which is a very common issue for satellite images of urban areas. Future research will include designing and training the neural network to detect small buildings, as well as irregularly shaped buildings that are partially obscured by shadows or other occlusions. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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18 pages, 4749 KiB  
Article
EANet: Edge-Aware Network for the Extraction of Buildings from Aerial Images
by Guang Yang, Qian Zhang and Guixu Zhang
Remote Sens. 2020, 12(13), 2161; https://doi.org/10.3390/rs12132161 - 6 Jul 2020
Cited by 58 | Viewed by 5818
Abstract
Deep learning methods have been used to extract buildings from remote sensing images and have achieved state-of-the-art performance. Most previous work has emphasized the multi-scale fusion of features or the enhancement of more receptive fields to achieve global features rather than focusing on [...] Read more.
Deep learning methods have been used to extract buildings from remote sensing images and have achieved state-of-the-art performance. Most previous work has emphasized the multi-scale fusion of features or the enhancement of more receptive fields to achieve global features rather than focusing on low-level details such as the edges. In this work, we propose a novel end-to-end edge-aware network, the EANet, and an edge-aware loss for getting accurate buildings from aerial images. Specifically, the architecture is composed of image segmentation networks and edge perception networks that, respectively, take charge of building prediction and edge investigation. The International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam segmentation benchmark and the Wuhan University (WHU) building benchmark were used to evaluate our approach, which, respectively, was found to achieve 90.19% and 93.33% intersection-over-union and top performance without using additional datasets, data augmentation, and post-processing. The EANet is effective in extracting buildings from aerial images, which shows that the quality of image segmentation can be improved by focusing on edge details. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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19 pages, 3809 KiB  
Article
Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou
by Leli Zong, Sijia He, Jiting Lian, Qiang Bie, Xiaoyun Wang, Jingru Dong and Yaowen Xie
Remote Sens. 2020, 12(12), 1987; https://doi.org/10.3390/rs12121987 - 20 Jun 2020
Cited by 26 | Viewed by 3628
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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14 pages, 3287 KiB  
Article
Where Does Nighttime Light Come From? Insights from Source Detection and Error Attribution
by Zhehao Ren, Yufu Liu, Bin Chen and Bing Xu
Remote Sens. 2020, 12(12), 1922; https://doi.org/10.3390/rs12121922 - 14 Jun 2020
Cited by 6 | Viewed by 2725
Abstract
Nighttime light remote sensing has aroused great popularity because of its advantage in estimating socioeconomic indicators and quantifying human activities in response to the changing world. Despite many advances that have been made in method development and implementation of nighttime light remote sensing [...] Read more.
Nighttime light remote sensing has aroused great popularity because of its advantage in estimating socioeconomic indicators and quantifying human activities in response to the changing world. Despite many advances that have been made in method development and implementation of nighttime light remote sensing over the past decades, limited studies have dived into answering the question: Where does nighttime light come from? This hinders our capability of identifying specific sources of nighttime light in urbanized regions. Addressing this shortcoming, here we proposed a parcel-oriented temporal linear unmixing method (POTLUM) to identify specific nighttime light sources with the integration of land use data. Ratio of root mean square error was used as the measure to assess the unmixing accuracy, and parcel purity index and source sufficiency index were proposed to attribute unmixing errors. Using the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light dataset from the Suomi National Polar-Orbiting Partnership (NPP) satellite and the newly released Essential Urban Land Use Categories in China (EULUC-China) product, we applied the proposed method and conducted experiments in two China cities with different sizes, Shanghai and Quzhou. Results of the POTLUM showed its relatively robust applicability of detecting specific nighttime light sources, achieving an rRMSE of 3.38% and 1.04% in Shanghai and Quzhou, respectively. The major unmixing errors resulted from using impure land parcels as endmembers (i.e., parcel purity index for Shanghai and Quzhou: 54.48%, 64.09%, respectively), but it also showed that predefined light sources are sufficient (i.e., source sufficiency index for Shanghai and Quzhou: 96.53%, 99.55%, respectively). The method presented in this study makes it possible to identify specific sources of nighttime light and is expected to enrich the estimation of structural socioeconomic indicators, as well as better support various applications in urban planning and management. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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18 pages, 5948 KiB  
Article
Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen
by Mo Su, Renzhong Guo, Bin Chen, Wuyang Hong, Jiaqi Wang, Yimei Feng and Bing Xu
Remote Sens. 2020, 12(9), 1497; https://doi.org/10.3390/rs12091497 - 8 May 2020
Cited by 21 | Viewed by 3857
Abstract
A heavy workload is required for sample collection for urban land use classification, and researchers are in urgent need of sampling strategies as a guide to achieve more effective work. In this paper, we make use of an urban land use survey to [...] Read more.
A heavy workload is required for sample collection for urban land use classification, and researchers are in urgent need of sampling strategies as a guide to achieve more effective work. In this paper, we make use of an urban land use survey to obtain a complete sample set of a city, test the impact of different training and validation sample sizes on the accuracy, and summarize the sampling strategy. The following conclusions are drawn based on our systematic analysis in Shenzhen. (1) For the best classification accuracy, the number of training samples should be no less than 40% of the total number of parcels or no less than 5500 parcels. For the best labor cost performance, the number should be no less than 7% or no less than 900. (2) The accuracy evaluation is stable and reliable and requires validation sample numbers of no less than 10% of the total or no less than 1200. (3) Samples with a purity of 60–90% are preferred, and the classification effectiveness is better in samples with a purity greater than 90% under the same number. (4) If spatial equilibrium sampling cannot be carried out, sampling areas with complex land use patterns should be preferred. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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26 pages, 20551 KiB  
Article
DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data
by Hanqing Bao, Dongping Ming, Ya Guo, Kui Zhang, Keqi Zhou and Shigao Du
Remote Sens. 2020, 12(7), 1088; https://doi.org/10.3390/rs12071088 - 28 Mar 2020
Cited by 60 | Viewed by 5773
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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10 pages, 3748 KiB  
Letter
Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach
by Ying Tu, Bin Chen, Tao Zhang and Bing Xu
Remote Sens. 2020, 12(7), 1058; https://doi.org/10.3390/rs12071058 - 25 Mar 2020
Cited by 31 | Viewed by 4702
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
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