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Remote Sensing Image and Urban Information Visualization

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

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 22808

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Prof. Dr. Lin Li

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Guest Editor

School of Resource and Environmental Sciences, Wuhan university, 129 Luoyu Road, Wuhan 430079, Hubei, China
Interests: 3D spatial modeling; geo-visualization; image classification; spatial representation of property

Prof. Dr. Mengjun Kang

E-Mail Website
Guest Editor

School of Resource and Environmental Sciences, Wuhan university, 129 Luoyu Road, Wuhan 430079, Hubei, China
Interests: address analyses system; geo-visualization; integration of GIS service

Prof. Dr. Min Weng

E-Mail Website
Guest Editor

School of Resource and Environmental Sciences, Wuhan university, 129 Luoyu Road, Wuhan 430079, Hubei, China
Interests: spatial analysis; urban cartography; visualization and map design

Special Issue Information

Dear Colleagues,

Remote sensing image and urban information visualization provide an effective means in terms of vision to identify an interesting urban phenomenon, discover the unseen pattern and reveal potential mechanisms of urban evolution from urban images and urban information. They have gained increasing attention from a wide range of fields covering geographical information systems, remote sensing, urban planning and management, information visualization and computer version. This Special Issue aims to present the state-of-the-art research in data capturing and processing, geo-data analyses, information engineering and modeling regarding urban studies and applications by means of visualization. Those papers will provide readers of Remote Sensing and Geoscience with a wide range of GIS, remote sensing, earth science, computer science, information management and data mining, to explore the principles and mechanisms of urban dynamics behind the images and data. Some of the prospective/encouraged topics for this Issue include, but are not limited to:

3D modeling and visualization
Remote sensing of urban images
Visual explorative analyses of urban systems
Urban population estimation using urban information
Visual analytics for urban traffic
Visualization methods for urban planning and design
Urban expansion and change
Urban information visualization algorithms
Urban image map design.
Spatial representation of owership of property
Smart sensing of urban

Related References:

Calos A Vanegas, Daniel G. Aliaga, Bendrich Benes, Paul Waddell. Visualization of Simulated Urban Spaces: Inferring Parameterized Generation of Streets, Parcels, and Aerial Imagery [J]. IEEE Transactions on Visualization and Computer Graphics. 2009,15(3):424-35.
Tingling Shen, Tinghua Ai, Hao Chen, Jingzhong Li. Multilevel Mapping From Remote Sensing Images: A Case Study of Urban Buildings [J]. IEEE Transactions on Geoscience and Remote Sensing. 2021. Doi: 10.1109/TGRS.2021.3062751.
Kang Liu, Ling Yin, Feng Lu, Naixia Mou. Visualizing and exploring POI configurations of urban regions on POI-type semantic space [J]. Cities. 99(2020) 102610: 1-10.
Shushing Wu, Xiaoming Qiu, Le Wang. Population Estimation Methods in GIS and Remote Sensing: A Review [J]. GIScience & Remote Sensing. 2005, 42(1):80-96.
Xiaojun Yang, Zhi Liu. Using satellite imagery and GIS for land-use and land-cover change mapping in an estuarine watershed [J]. International Journal of Remote Sensing. 2005, 26(23): 5275-5296.
Pauline Collon, Wendy Steckiewicz-Laurent, .etc. 3D geo-modelling combining implicit surfaces and Voronoi-based remeshing: A case study in the Lorraine Coal Basin (France) [J]. Computers & Geosciences. 77(2015):29-43.
Xinhao, Wang. Integrating GIS, simulation models, and visualization in traffic impact analysis [J]. Computers, Environment and Urban Systems. 2005, 29(4):471-496.

Prof. Dr. Lin Li
Prof. Dr. Mengjun Kang
Prof. Dr. Min Weng
Guest Editors

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Keywords

Visualization
Urban social sensing
Big data
Remote sensing image
Urban monitoring
Urban pattern
Smart city

Published Papers (8 papers)

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Research

16 pages, 4265 KiB

Open AccessArticle

Salt Stockpile Inventory Management Using LiDAR Volumetric Measurements

by Justin Anthony Mahlberg, Raja Manish, Yerassyl Koshan, Mina Joseph, Jidong Liu, Timothy Wells, Jeremy McGuffey, Ayman Habib and Darcy M. Bullock

Remote Sens. 2022, 14(19), 4802; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194802 - 26 Sep 2022

Cited by 5 | Viewed by 2473

Abstract

Transportation agencies in northern environments spend a considerable amount of their budget on salt for winter operations. For example, in the state of Indiana, there are approximately 140 salt storage facilities distributed throughout the state and the state expends between USD 30 M and USD 60 M on inventory and delivery each year. Historical techniques of relying on visual estimates of salt stockpiles can be inaccurate and do not scale well for managing the supply chain during the winter or planning for re-supply during summer months. This paper describes the implementation of a portable pole mounted LiDAR system that can be used to inventory a large barn in under 15 min and describes how this system has been deployed over 90 times at 30 facilities. A quick and easy accuracy test, based upon conservation of volume, was used to provide an independent check on the system performance by repositioning portions of the salt pile. Those tests indicated stockpile volumes can be estimated with an accuracy of approximately 0.1%. The paper concludes by discussing how this technology can be permanently installed near the roof for systematic monitoring throughout the year. Full article

(This article belongs to the Special Issue Remote Sensing Image and Urban Information Visualization)

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25 pages, 6748 KiB

Open AccessArticle

Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

by Shaofu Lin, Chang Zhang, Lei Ding, Jing Zhang, Xiliang Liu, Guihong Chen, Shaohua Wang and Jinchuan Chai

Remote Sens. 2022, 14(13), 3144; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133144 - 30 Jun 2022

Cited by 7 | Viewed by 2721

Abstract

Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Effective assessment of carbon emission reduction has become an urgent challenge for the government and for business enterprises. In this study, we propose a method to assess accurately the potential reduction of long-term carbon emission by installing solar PV on rooftops. This is achieved using the joint action of GF-2 satellite images, Point of Interest (POI) data, and meteorological data. Firstly, we introduce a building extraction method that extends the DeepLabv3+ by fusing the contextual information of building rooftops in GF-2 images through multi-sensory fields. Secondly, a ridgeline detection algorithm for rooftop classification is proposed, based on the Hough transform and Canny edge detection. POI semantic information is used to calculate the usable area under different subsidy policies. Finally, a multilayer perceptron (MLP) is constructed for long-term PV electricity generation series with regional meteorological data, and carbon emission reduction is estimated for three scenarios: the best, the general, and the worst. Experiments were conducted with GF-2 satellite images collected in Daxing District, Beijing, China in 2021. Final results showed that: (1) The building rooftop recognition method achieved overall accuracy of 95.56%; (2) The best, the general and the worst amount of annual carbon emission reductions in the study area were 7,705,100 tons, 6,031,400 tons, and 632,300 tons, respectively; (3) Multi-source data, such as POIs and climate factors play an indispensable role for long-term estimation of carbon emission reduction. The method and conclusions provide a feasible approach for quantitative assessment of carbon reduction and policy evaluation. Full article

(This article belongs to the Special Issue Remote Sensing Image and Urban Information Visualization)

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19 pages, 41563 KiB

Open AccessArticle

High-Resolution Boundary-Constrained and Context-Enhanced Network for Remote Sensing Image Segmentation

by Yizhe Xu and Jie Jiang

Remote Sens. 2022, 14(8), 1859; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081859 - 12 Apr 2022

Cited by 5 | Viewed by 1837

Abstract

The technology of remote sensing image segmentation has made great progress in recent years. However, there are still several challenges which need to be addressed (e.g., ground objects blocked by shadows, higher intra-class variance and lower inter-class variance). In this paper, we propose a novel high-resolution boundary-constrained and context-enhanced network (HBCNet), which combines boundary information to supervise network training and utilizes the semantic information of categories with the regional feature presentations to improve final segmentation accuracy. On the one hand, we design the boundary-constrained module (BCM) and form the parallel boundary segmentation branch, which outputs the boundary segmentation results and supervises the network training simultaneously. On the other hand, we also devise a context-enhanced module (CEM), which integrates the self-attention mechanism to advance the semantic correlation between pixels of the same category. The two modules are independent and can be directly embedded in the main segmentation network to promote performance. Extensive experiments were conducted using the ISPRS Vahingen and Potsdam benchmarks. The mean F1 score (m-F1) of our model reached 91.32% and 93.38%, respectively, which exceeds most existing CNN-based models and represents state-of-the-art results. Full article

(This article belongs to the Special Issue Remote Sensing Image and Urban Information Visualization)

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18 pages, 72314 KiB

Open AccessArticle

A Population Spatialization Model at the Building Scale Using Random Forest

by Mengqi Wang, Yinglin Wang, Bozhao Li, Zhongliang Cai and Mengjun Kang

Remote Sens. 2022, 14(8), 1811; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081811 - 08 Apr 2022

Cited by 11 | Viewed by 2232

Abstract

Population spatialization reveals the distribution and quantity of the population in geographic space with gridded population maps. Fine-scale population spatialization is essential for urbanization and disaster prevention. Previous approaches have used remotely sensed imagery to disaggregate census data, but this approach has limitations. For example, large-scale population censuses cannot be conducted in underdeveloped countries or regions, and remote sensing data lack semantic information indicating the different human activities occurring in a precise geographic location. Geospatial big data and machine learning provide new fine-scale population distribution mapping methods. In this paper, 30 features are extracted using easily accessible multisource geographic data. Then, a building-scale population estimation model is trained by a random forest (RF) regression algorithm. The results show that 91% of the buildings in Lin’an District have absolute error values of less than six compared with the actual population data. In a comparison with a multiple linear (ML) regression model, the mean absolute errors of the RF and ML models are 2.52 and 3.21, respectively, the root mean squared errors are 8.2 and 9.8, and the R² values are 0.44 and 0.18. The RF model performs better at building-scale population estimation using easily accessible multisource geographic data. Future work will improve the model accuracy in densely populated areas. Full article

(This article belongs to the Special Issue Remote Sensing Image and Urban Information Visualization)

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19 pages, 5848 KiB

Open AccessArticle

Spatiotemporal Characterization of the Urban Expansion Patterns in the Yangtze River Delta Region

by Ziqi Yu, Longqian Chen, Long Li, Ting Zhang, Lina Yuan, Ruiyang Liu, Zhiqiang Wang, Jinyu Zang and Shuai Shi

Remote Sens. 2021, 13(21), 4484; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214484 - 08 Nov 2021

Cited by 14 | Viewed by 2649

Abstract

Characterizing urban expansion patterns is of great significance to planning and decision-making for urban agglomeration development. This study examined the urban expansion in the entire Yangtze River Delta Region (YRDR) with its land-use data of six years (1995, 2000, 2005, 2010, 2015, and 2018). On the basis of traditional methods, we comprehensively considered the four aspects of urban agglomeration: expansion speed, expansion difference, expansion direction, and landscape pattern, as well as the interconnection of and difference in the expansion process between each city. The spatiotemporal heterogeneity of urban expansion development in this region was investigated by using the speed and differentiation indices of urban expansion, gravity center migration, landscape indices, and spatial autocorrelations. The results show that: (1) over the 23 years, the expansion of built-up land in the Yangtze River Delta Region was significant, (2) the rapidly expanding cities were mainly located along the Yangtze River and coastal areas, while the slowly expanding cities were mainly located in the inland areas, (3) the expansion direction of each city varied and the gravity center of the urban agglomeration moved toward the southwest, and (4) the spatial structure of the region became more clustered, the shape of built-up land turned simpler, and fragmentation decreased. This study unravels the spatiotemporal change of urban expansion patterns in this large urban agglomeration, and more importantly, can serve as a guide for formulating urban agglomeration development plans. Full article

(This article belongs to the Special Issue Remote Sensing Image and Urban Information Visualization)

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18 pages, 26560 KiB

Open AccessArticle

A New Approach to Identify On-Ground Lamp Types from Night-Time ISS Images

by Natalia Rybnikova, Alejandro Sánchez de Miguel, Sviatoslav Rybnikov and Anna Brook

Remote Sens. 2021, 13(21), 4413; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214413 - 02 Nov 2021

Cited by 7 | Viewed by 2512

Abstract

Artificial night-time light (NTL), emitted by various on-ground human activities, has become intensive in many regions worldwide. Its adverse effects on human and ecosystem health crucially depend on the light spectrum, making the remote discrimination between different lamp types a highly important task. However, such studies remain extremely limited, and none of them exploit freely available satellite imagery. In the present analysis, the possibility to remotely assess the relative contribution of different lamp types into outdoor lighting is tested. For this sake, we match two data sources: (i) the radiometrically calibrated RGB image provided by the ISS (coarse spectral resolution data), and (ii) a set of in situ measurements with detailed spectral signatures conducted by ourselves (fine spectral resolution data). First, we analyze the fine spectral resolution data: using spectral signatures of standard lamp types from the LICA UCM library as endmembers, we perform an unmixing analysis upon NTL in situ measurements; by this, we obtain the estimates for relative contributions of the standard lamp types in each examined in situ measurement. Afterward, we focus on the coarse spectral resolution data: by using various types of statistical models, we predict the estimated relative contributions of each lamp type via RGB characteristics of spatially corresponding pixels of the ISS image. The built models predict sufficiently well (with R² reaching ~0.87) the contributions of two standard lamp types: high-pressure sodium (HPS) and metal-halide (MH) lamps, the most widespread lamp types in the study area (Haifa, Israel). The restored map for HPS allocation demonstrates high concordance with the network of municipal roads, while that for MH shows notable coincidence with the industrial facilities and the airport. Full article

(This article belongs to the Special Issue Remote Sensing Image and Urban Information Visualization)

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21 pages, 30080 KiB

Open AccessArticle

Understanding Urban Growth in Beijing-Tianjin-Hebei Region over the Past 100 Years Using Old Maps and Landsat Data

by Shuang Li, Zhongqiu Sun, Yafei Wang and Yuxia Wang

Remote Sens. 2021, 13(16), 3264; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163264 - 18 Aug 2021

Cited by 8 | Viewed by 2601

Abstract

Studying urban expansion from a longer-term perspective is of great significance to obtain an in-depth understanding of the process of urbanization. Remote sensing data are mostly selected to investigate the long-term expansion of cities. In this study, we selected the world-class urban agglomeration of Beijing-Tianjin-Hebei (BTH) as the study area, and then discussed how to make full use of multi-source, multi-category, and multi-temporal spatial data (old maps and remote sensing images) to study long-term urbanization. Through this study, we addressed three questions: (1) How much has the urban area in BTH expanded in the past 100 years? (2) How did the urban area expand in the past century? (3) What factors or important historical events have changed the development of cities with different functions? By comprehensively using urban spatial data, such as old maps and remote sensing images, geo-referencing them, and extracting built-up area information, a long-term series of urban built-up areas in the BTH region can be obtained. Results show the following: (1) There was clear evidence of dramatic urban expansion in this area, and the total built-up area had increased by 55.585 times, from 126.181 km² to 7013.832 km². (2) Continuous outward expansion has always been the main trend, while the compactness of the built-up land within the city is constantly decreasing and the complexity of the city boundary is increasing. (3) Cities in BTH were mostly formed through the construction of city walls during the Ming and Qing dynasties, and the expansion process was mostly highly related to important political events, traffic development, and other factors. In summary, the BTH area, similarly to China and most regions of the world, has experienced rapid urbanization and the history of such ancient cities should be further preserved with the combined use of old maps. Full article

(This article belongs to the Special Issue Remote Sensing Image and Urban Information Visualization)

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24 pages, 9830 KiB

Open AccessArticle

Impacts of Urban Expansion Forms on Ecosystem Services in Urban Agglomerations: A Case Study of Shanghai-Hangzhou Bay Urban Agglomeration

by Sinan Li, Youyong He, Hanliang Xu, Congmou Zhu, Baiyu Dong, Yue Lin, Bo Si, Jinsong Deng and Ke Wang

Remote Sens. 2021, 13(10), 1908; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101908 - 13 May 2021

Cited by 27 | Viewed by 4166

Abstract

Exploring impacts of urban expansion on ecosystem services has become a hot topic for regional sustainable development, while analyzing the ecological effects of urban expansion forms under different expansion intensities and city sizes is relatively rare. Therefore, taking a typical urban agglomeration, Shanghai-Hangzhou Bay Urban Agglomeration, as a case study, this study first analyzed the dynamics of urban expansion forms (leapfrogging, edge-expansion, and infilling) and four critical ecosystem services (carbon sequestration, food supply, habitat quality, and soil retention) in three periods from 1990 to 2019. The multiple linear regression model and zonal statistics analysis model were used to quantitatively identify the impacts of urban expansion forms on ecosystem services, taking into account different expansion intensities and city sizes. The results showed that the urban expansion trend in the study area experienced a morphological change from integration to diffusion and then to integration in 1990–2019; edge-expansion was the dominant expansion form. Food supply decreased continuously while other ecosystem services had fluctuating changes, and they all had spatial heterogeneity. The leapfrogging, edge-expansion, and infilling all had negative impacts on ecosystem services, and among them, the edge-expansion intensity had the highest influence degree in the early expansion, and the leapfrogging intensity occupied the dominant position in all influences with the expansion of urban scales. For different city sizes, the impact of edge-expansion in large-scale cities was greater than in small-scale cities in the early expansion, and the impact of leapfrogging in large-scale cities exceeded the edge-expansion in the subsequent expansion. These findings will help further understand the influential mechanisms between urban expansion and ecosystem services and provide a scientific basis for formulating reasonable urban planning. Full article

(This article belongs to the Special Issue Remote Sensing Image and Urban Information Visualization)

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