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Remote Sensing
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Geospatial Big Data and AI/Deep Learning for the Sustainable Planet
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Geospatial Big Data and AI/Deep Learning for the Sustainable Planet

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

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 12034

Special Issue Editors

Prof. Dr. Bin Jiang

grade E-Mail Website
Guest Editor
Division of GIScience, Faculty of Engineering and Sustainable Development, University of Gävle, SE-801 76 Gävle, Sweden
Interests: city science; big data analytics; sustainable urban planing; city structure and dynamics; the livingness of space
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qingfeng (Gene) Guan

E-Mail Website
Guest Editor
School of Geography and Information Engineering & National Engineering Research Center of GIS, China University of Geosciences, Wuhan, China
Interests: big geospatial data; GeoAI; high-performance GeoComputation; spatiotemporal modeling; land-use and land-cover change; urban informatices
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Songnian Li

E-Mail Website
Guest Editor
Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON, Canada
Interests: big geospatial data analytics; data mining and knowledge discovery; human mobility; spatio-temporal dynamics; citizen science; digital twins for smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geospatial big data and AI/deep learning provide a new means for researching the Earth’s surface at a variety of scales, for example, land use changes or geographic forms and processes in general. Different from conventional geospatial small data, geospatial big data is characterized by its higher resolution in both space and time and individual-based rather than aggregated and can therefore be studied in its entirety rather than samples. It is these unique properties of big data that have further empowered AI or deep learning in various fields. Recently, the living structure has emerged as a new paradigm not only for better understanding geographic forms and processes (or city structure and dynamics in particular), but also for effectively transforming cities and communities towards a sustainable society or a sustainable planet in general. The notion of living structure is defined as a mathematical structure in which there are numerous recursively defined substructures at different levels of hierarchy, and it can be used to characterize the livingness of space: the more substructures the more living, and the higher hierarchy of the substructures the more living. This outlined research on living structure is not intended to delineate the scope of the Special Issue, but servers as an example of diverse research for the Special Issue papers or submissions. Any submissions that touch one or two of the keywords listed below will be welcome.

Prof. Dr. Bin Jiang
Prof. Dr. Qingfeng (Gene) Guan
Prof. Dr. Songnian Li
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.

Keywords

  • remote sensing big data
  • social media big data
  • nighttime imagery
  • artificial intelligence
  • deep learning
  • land use changes
  • climate change
  • sustainability
  • living structure
  • city structure and dynamics
  • head/tail breaks
  • natural cities
  • scaling law

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

37 pages, 3241 KiB  
Article
Impact of Tile Size and Tile Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification
by Calimanut-Ionut Cira, Miguel-Ángel Manso-Callejo, Naoto Yokoya, Tudor Sălăgean and Ana-Cornelia Badea
Remote Sens. 2024, 16(15), 2818; https://doi.org/10.3390/rs16152818 - 31 Jul 2024
Cited by 1 | Viewed by 1167
Abstract
Popular geo-computer vision works make use of aerial imagery, with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack [...] Read more.
Popular geo-computer vision works make use of aerial imagery, with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack of information near the image boundaries. In this work, the impact of three tile sizes (256 × 256, 512 × 512, and 1024 × 1024 pixels) and two overlap levels (no overlap and 12.5% overlap) on the performance of road classification models was statistically evaluated. For this, two convolutional neural networks used in various tasks of geospatial object extraction were trained (using the same hyperparameters) on a large dataset (containing aerial image data covering 8650 km2 of the Spanish territory that was labelled with binary road information) under twelve different scenarios, with each scenario featuring a different combination of tile size and overlap. To assess their generalisation capacity, the performance of all resulting models was evaluated on data from novel areas covering approximately 825 km2. The performance metrics obtained were analysed using appropriate descriptive and inferential statistical techniques to evaluate the impact of distinct levels of the fixed factors (tile size, tile overlap, and neural network architecture) on them. Statistical tests were applied to study the main and interaction effects of the fixed factors on the performance. A significance level of 0.05 was applied to all the null hypothesis tests. The results were highly significant for the main effects (p-values lower than 0.001), while the two-way and three-way interaction effects among them had different levels of significance. The results indicate that the training of road classification models on images with a higher tile size (more semantic context) and a higher amount of tile overlap (additional border context and continuity) significantly impacts their performance. The best model was trained on a dataset featuring tiles with a size of 1024 × 1024 pixels and a 12.5% overlap, and achieved a loss value of 0.0984, an F1 score of 0.8728, and an ROC-AUC score of 0.9766, together with an error rate of 3.5% on the test set. Full article
(This article belongs to the Special Issue Geospatial Big Data and AI/Deep Learning for the Sustainable Planet)
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31 pages, 15059 KiB  
Article
Multi-Scenario Simulation of Land System Change in the Guangdong–Hong Kong–Macao Greater Bay Area Based on a Cellular Automata–Markov Model
by Chao Yang, Han Zhai, Meijuan Fu, Que Zheng and Dasheng Fan
Remote Sens. 2024, 16(9), 1512; https://doi.org/10.3390/rs16091512 - 25 Apr 2024
Cited by 5 | Viewed by 1105
Abstract
As one of the four major bay areas in the world, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a highly integrated mega urban agglomeration and its unparalleled urbanization has induced prominent land contradictions between humans and nature, which hinders its sustainability and [...] Read more.
As one of the four major bay areas in the world, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a highly integrated mega urban agglomeration and its unparalleled urbanization has induced prominent land contradictions between humans and nature, which hinders its sustainability and has become the primary concern in this region. In this paper, we probed the historical characteristics of land use and land cover change (LUCC) in the GBA from 2005 to 2015, and forecasted its future land use pattern for 2030, 2050, and 2070, using a cellular automata–Markov (CA–Markov) model, under three typical tailored scenarios, i.e., urban development (UD), cropland protection (CP), and ecology security (ES), for land use optimization. The major findings are as follows: (1) The encroachments of build-up land on the other land uses under rapid urbanization accounted for the leading forces of LUCCs in the past decade. Accordingly, the urban sprawl was up to 1441.73 km2 (23.47%), with cropland, forest land, and water areas reduced by 570.77 km2 (4.38%), 526.05 km2 (1.76%), and 429.89 km2 (10.88%), respectively. (2) Based on the validated CA–Markov model, significant differences are found in future land use patterns under multiple scenarios, with the discrepancy magnified over time and driven by different orientations. (3) Through comprehensive comparisons and tradeoffs, the ES scenario mode seems optimal for the GBA in the next decades, which optimizes the balance between socio-economic development and ecological protection. These results serve as an early warning for future land problems and can be applied to land use management and policy formulation to promote the sustainable development of the GBA. Full article
(This article belongs to the Special Issue Geospatial Big Data and AI/Deep Learning for the Sustainable Planet)
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22 pages, 4372 KiB  
Article
Exploring the Spatial Relationship between Urban Vitality and Urban Carbon Emissions
by Hui Yang, Qingping He, Liu Cui and Abdallah M. Mohamed Taha
Remote Sens. 2023, 15(8), 2173; https://doi.org/10.3390/rs15082173 - 20 Apr 2023
Cited by 8 | Viewed by 3609
Abstract
Urbanization profoundly impacts the global carbon cycle and climate change. Many studies have shown that both urban vitality and urban carbon emissions are deeply affected by spatial planning and city structure. However, the specific relationship between urban vitality and urban carbon emissions is [...] Read more.
Urbanization profoundly impacts the global carbon cycle and climate change. Many studies have shown that both urban vitality and urban carbon emissions are deeply affected by spatial planning and city structure. However, the specific relationship between urban vitality and urban carbon emissions is rarely studied. An index system of urban vitality was established from four aspects: social, economic, cultural, and environmental. After analyzing the spatial distribution characteristics of urban vitality combined with spatial syntax and the TOPSIS model, this paper further investigated the influence of urban vitality-building factors on the distribution of urban carbon emissions based on the Geodetector method. The research results show that: (1) Xuzhou shows obvious spatial differences in urban vitality, mainly decreasing from the center to the surrounding areas, with a small vitality center in the northeast. (2) The impact of different dimensions of vitality on urban carbon emissions is apparently different. (3) Facilities’ aggregation has the weakest explanatory power for urban carbon emissions, while the NDVI has the highest explanatory power. This study helps to clarify the spatial correlation and influence mechanism between urban vitality and urban carbon emissions. Finally, some suggestions are proposed to construct low-carbon and high-vitality cities. Full article
(This article belongs to the Special Issue Geospatial Big Data and AI/Deep Learning for the Sustainable Planet)
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26 pages, 11421 KiB  
Article
Urban Built Environment Assessment Based on Scene Understanding of High-Resolution Remote Sensing Imagery
by Jie Chen, Xinyi Dai, Ya Guo, Jingru Zhu, Xiaoming Mei, Min Deng and Geng Sun
Remote Sens. 2023, 15(5), 1436; https://doi.org/10.3390/rs15051436 - 3 Mar 2023
Cited by 5 | Viewed by 2395
Abstract
A high-quality built environment is important for human health and well-being. Assessing the quality of the urban built environment can provide planners and managers with decision-making for urban renewal to improve resident satisfaction. Many studies evaluate the built environment from the perspective of [...] Read more.
A high-quality built environment is important for human health and well-being. Assessing the quality of the urban built environment can provide planners and managers with decision-making for urban renewal to improve resident satisfaction. Many studies evaluate the built environment from the perspective of street scenes, but it is difficult for street-view data to cover every area of the built environment and its update frequency is low, which cannot meet the requirement of built-environment assessment under rapid urban development. Earth-observation data have the advantages of wide coverage, high update frequency, and good availability. This paper proposes an intelligent evaluation method for urban built environments based on scene understanding of high-resolution remote-sensing images. It contributes not only the assessment criteria for the built environment in remote-sensing images from the perspective of visual cognition but also an image-caption dataset applicable to urban-built-environment assessment. The results show that the proposed deep-learning-driven method can provide a feasible paradigm for representing high-resolution remote-sensing image scenes and large-scale urban-built-area assessment. Full article
(This article belongs to the Special Issue Geospatial Big Data and AI/Deep Learning for the Sustainable Planet)
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19 pages, 8360 KiB  
Article
Monitoring Land Cover Change by Leveraging a Dynamic Service-Oriented Computing Model
by Huaqiao Xing, Haihang Wang, Jinhua Zhang and Dongyang Hou
Remote Sens. 2023, 15(3), 736; https://doi.org/10.3390/rs15030736 - 27 Jan 2023
Cited by 4 | Viewed by 1888
Abstract
Land cover change (LCC) is increasingly affecting global climate change, energy cycle, carbon cycle, and water cycle, with far-reaching consequences to human well-being. Web service-based online change detection applications have bloomed over the past decade for monitoring land cover change. Currently, massive processing [...] Read more.
Land cover change (LCC) is increasingly affecting global climate change, energy cycle, carbon cycle, and water cycle, with far-reaching consequences to human well-being. Web service-based online change detection applications have bloomed over the past decade for monitoring land cover change. Currently, massive processing services and data services have been published and used over the internet. However, few studies consider both service integration and resource sharing in land cover domain, making end-users rarely able to acquire the LCC information timely. The behavior interaction between services is also growing more complex due to the increasing use of web service composition technology, making it challenging for static web services to provide collaboration and matching between diverse web services. To address the above challenges, a Dynamic Service Computing Model (DSCM) was proposed for monitoring LCC. Three dynamic computation strategies were proposed according to different users’ requirements of change detection. WMS-LCC was first developed by extending the existing WMS for ready-use LCC data access. Spatial relation-based LCC data integration was then proposed for extracting LCC information based on multi-temporal land cover data. Processing service encapsulation and service composition methods were also developed for chaining various land cover services to a complex service chain. Finally, a prototype system was implemented to evaluate the validity and feasibility of the proposed DSCM. Two walk-through examples were performed with GlobeLand30 datasets and muti-temporal Landsat imagery, respectively. The experimental results indicate that the proposed DSCM approach was more effective and applicable to a wider range of issues in land cover change detection. Full article
(This article belongs to the Special Issue Geospatial Big Data and AI/Deep Learning for the Sustainable Planet)
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