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Geo-Information in Smart Societies and Environment

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 January 2023) | Viewed by 12997

Special Issue Editors


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Guest Editor
Department of Geography and Centre for Geo-computation Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
Interests: change detection and land cover modelling with remote sensing; digital terrain analysis and hydrological modeling; climate change and its impacts on water resources and ecosystems; arid zone studies
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Geography and Centre for Geo-computation Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
Interests: hydroclimatology; hydrology and water resources; climate extremes and water hazards; climate change; regional water cycle
Special Issues, Collections and Topics in MDPI journals
Division of Landscape and Architecture, The University of Hong Kong, Hong Kong, China
Interests: remote sensing; data fusion and applications; geospatial big data analysis; environmental health
Special Issues, Collections and Topics in MDPI journals
Department of Geography and Centre for Geo-computation Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
Interests: atmospheric chemistry modeling; chemistry–climate interactions; environmental health; chemical data assimilation

Special Issue Information

Dear Colleagues,

Geo-information, such as remote sensing, social sensing, and crowdsourcing geospatial big data, provides new and unique insights to advance our scientific understanding of the human–environment interaction. Geospatial technologies, geo-data and remote sensing data have been widely used to study urban and environmental health issues, such as built environment change, urbanization process, urban mobility, human behaviours, environmental exposure, and public health. Geo-information has also become an important tool to investigate pressing issues, such as unbalanced socio-economic development, air pollution control and mitigation, prediction and risk assessment of hazard, environmental monitoring and modelling, etc.

This Special Issue calls for the newest research that makes use of remote sensing, social sensing, and crowdsourcing data, methods, and geospatial techniques in social and environmental studies. The expected topics include, but are not limited to, smart societies, smart cities, environmental sustainability, urban environmental health, environmental monitoring and modelling, environmental changes, natural hazards and risks, and climate change.

Keywords

  • Geo-information
  • Smart societies
  • Smart cities
  • Sustainable environment
  • Urban environment changes
  • Environmental health
  • Environmental monitoring and modeling
  • Natural hazards and risks
  • Climate change

Published Papers (5 papers)

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24 pages, 11679 KiB  
Article
Grid-Based Essential Urban Land Use Classification: A Data and Model Driven Mapping Framework in Xiamen City
by Xi Wang, Bin Chen, Xuecao Li, Yuxin Zhang, Xianyao Ling, Jie Wang, Weimin Li, Wu Wen and Peng Gong
Remote Sens. 2022, 14(23), 6143; https://doi.org/10.3390/rs14236143 - 3 Dec 2022
Cited by 2 | Viewed by 3026
Abstract
Accurate and timely mapping of essential urban land use categories (EULUC) is vital to understanding urban land use distribution, pattern, and composition. Recent advances in leveraging big open data and machine learning algorithms have demonstrated the possibility of large-scale mapping of EULUC in [...] Read more.
Accurate and timely mapping of essential urban land use categories (EULUC) is vital to understanding urban land use distribution, pattern, and composition. Recent advances in leveraging big open data and machine learning algorithms have demonstrated the possibility of large-scale mapping of EULUC in a new cost-effective way. However, they are still limited by the transferability of samples, models, and classification results across space, particularly across different cities. Given the heterogeneities of environmental and socioeconomic conditions among cities, in-depth studies of data and model adaptation towards city-specific EULUC mappings are highly required to support policy making, and urban renewal planning and management practices. In addition, the trending need for timely and detailed small land unit data processing with finer data granularity becomes increasingly important. We proposed a City Meta Unit (CMU) data model and classification framework driven by multisource data and artificial intelligence (AI) algorithms to address these challenges. The CMU Framework was innovatively applied to systematically set up a grid-based data model and classify urban land use with an improved AI algorithm by applying Moore neighborhood correlations. Specifically, we selected Xiamen, Fujian, in China, a coastal city, as the typical testbed to implement this proposed framework and apply an AI transfer learning technique for grid and parcel land-use study. Experimental results with our proposed CMU framework showed that the grid-based land use classification performance achieves overall accuracies of 81.17% and 76.55% for level I (major classes) and level II (minor classes), which is much higher than the parcel-based land use classification (overall accuracies of 72.37% for level I, and 68.99% for level II). We further investigated the relationship between training sample size and classification performance and quantified the contribution of different data sources to urban land use classifications. The CMU framework makes data collections and processing intelligent and efficient, with finer granularity, saving time and cost by using existing open social data. Incorporating the CMU framework with the proposed grid-based model is an effective and new approach for urban land use classification, which can be flexibly extended and applied to various cities. Full article
(This article belongs to the Special Issue Geo-Information in Smart Societies and Environment)
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27 pages, 68396 KiB  
Article
A Method with Adaptive Graphs to Constrain Multi-View Subspace Clustering of Geospatial Big Data from Multiple Sources
by Qiliang Liu, Weihua Huan and Min Deng
Remote Sens. 2022, 14(17), 4394; https://doi.org/10.3390/rs14174394 - 3 Sep 2022
Cited by 3 | Viewed by 1579
Abstract
Clustering of multi-source geospatial big data provides opportunities to comprehensively describe urban structures. Most existing studies focus only on the clustering of a single type of geospatial big data, which leads to biased results. Although multi-view subspace clustering methods are advantageous for fusing [...] Read more.
Clustering of multi-source geospatial big data provides opportunities to comprehensively describe urban structures. Most existing studies focus only on the clustering of a single type of geospatial big data, which leads to biased results. Although multi-view subspace clustering methods are advantageous for fusing multi-source geospatial big data, exploiting a robust shared subspace in high-dimensional, non-uniform, and noisy geospatial big data remains a challenge. Therefore, we developed a method with adaptive graphs to constrain multi-view subspace clustering of multi-source geospatial big data (agc2msc). First, for each type of data, high-dimensional and noisy original features were projected into a low-dimensional latent representation using autoencoder networks. Then, adaptive graph constraints were used to fuse the latent representations of multi-source data into a shared subspace representation, which preserved the neighboring relationships of data points. Finally, the shared subspace representation was used to obtain the clustering results by employing a spectral clustering algorithm. Experiments on four benchmark datasets showed that agc2msc outperformed nine state-of-the-art methods. agc2msc was applied to infer urban land use types in Beijing using the taxi GPS trajectory, bus smart card transaction, and points of interest datasets. The clustering results may provide useful calibration and reference for urban planning. Full article
(This article belongs to the Special Issue Geo-Information in Smart Societies and Environment)
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21 pages, 3064 KiB  
Article
Sensing the Nighttime Economy–Housing Imbalance from a Mobile Phone Data Perspective: A Case Study in Shanghai
by Mingxiao Li, Wei Tu and Feng Lu
Remote Sens. 2022, 14(12), 2738; https://doi.org/10.3390/rs14122738 - 7 Jun 2022
Cited by 4 | Viewed by 1988
Abstract
Sensing the nighttime economy–housing imbalance is of great importance for urban planning and commerce. As an efficient tool of social sensing and human observation, mobile phone data provides an effective way to address this issue. In this paper, an indicator, mobile phone data-based [...] Read more.
Sensing the nighttime economy–housing imbalance is of great importance for urban planning and commerce. As an efficient tool of social sensing and human observation, mobile phone data provides an effective way to address this issue. In this paper, an indicator, mobile phone data-based nighttime economy–housing imbalance intensity, is proposed to measure the degree of the nighttime economy–housing imbalance. This indicator can distinguish vitality variations between sleep periods and nighttime activity periods, which are highly related to the nighttime economy–housing imbalance. The spatial pattern of the nighttime economy–housing imbalance was explored, and its association with the built environment was investigated through city-scale geographical regression analysis in Shanghai, China. The results showed that the sub-districts of Shanghai with high-positive-imbalance intensities displayed structures with superimposed rings and striped shapes, and the sub-districts with negative imbalance intensities were distributed around high positive-intensity areas. There were significant linear correlations between imbalance intensity and the built environment. The multiple influences of built environment factors and related mechanisms were explored from a geographical perspective. Our study utilized the social sensing data to provide a more comprehensive understanding of the nighttime economy–housing imbalance. These findings will be useful for fostering the nighttime economy and supporting urban renewal. Full article
(This article belongs to the Special Issue Geo-Information in Smart Societies and Environment)
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19 pages, 6411 KiB  
Article
Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning
by Ying Tu, Bin Chen, Wei Lang, Tingting Chen, Miao Li, Tao Zhang and Bing Xu
Remote Sens. 2021, 13(21), 4241; https://doi.org/10.3390/rs13214241 - 22 Oct 2021
Cited by 13 | Viewed by 2684
Abstract
Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic [...] Read more.
Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping framework that is compatible with different end users’ demands. Moreover, land use mix has evolved to be a key component in modern urban settings, but few have explicitly measured the spatial complexity of land use or quantitively uncovered its driving forces. Addressing these challenges, here we developed a novel two-stage bottom-up scheme for mapping essential urban land use categories. In the first stage, we conducted object-based land use classification using crowdsourcing features derived from multi-source open big data and an automated ensemble learning approach. In the second stage, we identified parcel-based land use attributes, including the dominant type and mixture mode, by spatially correlating land parcels with the object-based results. Furthermore, we investigated the potential influencing factors of land use mix using principal components analysis and multiple linear regression. Experimental results in Ningbo, a coastal city in China, showed that the proposed framework could accurately depict the distribution and composition of urban land uses. At the object scale, the highest classification accuracy was as high as 86% and 78% for the major (Level I) and minor (Level II) categories, respectively. At the parcel scale, the generated land use maps were spatially consistent with the object-based maps. We found larger parcels were more likely to be mixed in land use, and industrial lands were characterized as the most complicated category. We also identified multiple factors that had a collective impact on land use mix, including geography, socioeconomy, accessibility, and landscape metrics. Altogether, our proposed framework offered an alternative to investigating urban land use composition, which could be applied in a broad range of implications in future urban studies. Full article
(This article belongs to the Special Issue Geo-Information in Smart Societies and Environment)
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18 pages, 4703 KiB  
Technical Note
False Detections Revising Algorithm for Millimeter Wave Radar SLAM in Tunnel
by Yang Li, Yonghui Wei, Yanping Wang, Yun Lin, Wenjie Shen and Wen Jiang
Remote Sens. 2023, 15(1), 277; https://doi.org/10.3390/rs15010277 - 3 Jan 2023
Cited by 4 | Viewed by 2193
Abstract
Millimeter wave (MMW) radar simultaneous localization and mapping (SLAM) technology is an emerging technology in a tunnel vehicle accident rescue scene. It is a powerful tool for statistic-trapped vehicle detection with limited vision caused by darkness, heat, and smoke. A variety of SLAM [...] Read more.
Millimeter wave (MMW) radar simultaneous localization and mapping (SLAM) technology is an emerging technology in a tunnel vehicle accident rescue scene. It is a powerful tool for statistic-trapped vehicle detection with limited vision caused by darkness, heat, and smoke. A variety of SLAM frameworks have been proven to be able to obtain 3-degree-of-freedom (3-dof) pose estimation results using 2-dimention (2D) MMW radar in open space. In the application of millimeter wave radar for pose estimation and mapping in a closed environment, closed space structures and artificial targets together constitute high-intensity multi-path scattering measurement data, leading to radar false detections. Radar false detections caused by multi-path scattering are generally considered to be detrimental to radar applications, such as multi-target tracking. However, few studies analyze the mechanism of multi-path effects on radar SLAM, especially in closed spaces. In order to address the problem, this paper first presents a radar multi-path scattering theory to study the generation mechanism difference of false and radar true detection and their influences on radar SLAM 2D pose estimation and mapping in tunnel. According to the scattering mechanism differences on SLAM, a radar azimuth scattering angle signature is proposed, which allows distinguishing radar false detections from real ones. This is useful in avoiding using unreliable radar false detections to solve a radar SLAM problem. In addition, two different radar false detection revising methods combined with the CSM (correlative scan matching) algorithm are proposed in this paper. The HTMR-CSM (hard-threshold-multi-path-revised correlative scan matching) algorithm only depends on a hard threshold of radar azimuth scattering angle signature to eliminate all radar false detections as much as possible before CSM. Another idea is the STMR-CSM (soft-threshold-multi-path-revised correlative scan matching) algorithm. All the radar false detections are classified according to the distribution model of radar azimuth accuracy, and part of more reliable radar false detections are retained to estimate a more accurate pose. All the ideas in this paper are validated by using an MMW 2D radar mounted on a rail-guided robot in a tunnel. Two cars on fire were set as the targets. The experimental results show that the STMR-CSM algorithm that keeps the reliable radar false detections improves the positioning accuracy by 20% compared with CSM. Full article
(This article belongs to the Special Issue Geo-Information in Smart Societies and Environment)
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