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Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Deepak Kumar
Deepak Kumar
, 
Nick P. Bassill
Nick P. Bassill
 et 
Sukanya Ghosh
Sukanya Ghosh
   | 23 mai 2024
International Journal on Smart Sensing and Intelligent Systems's Cover Image
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Article Category: Review
Publié en ligne: 23 mai 2024
Pages: -
Reçu: 28 déc. 2023
DOI: https://doi.org/10.2478/ijssis-2024-0014
Mots clés
© 2024 Deepak Kumar et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Methodology for analysis through deep-learning approach.
Methodology for analysis through deep-learning approach.

Figure 2:

Methodology for augmenting deep-learning approach in remote-sensing applications.
Methodology for augmenting deep-learning approach in remote-sensing applications.

Figure 3:

Framework for systematic/bibliometric review.
Framework for systematic/bibliometric review.

Figure 4:

Trends of yearly publications “Machine Learning” AND “Remote Sensing”.
Trends of yearly publications “Machine Learning” AND “Remote Sensing”.

Figure 5:

Trends of yearly publications in “Deep Learning” AND “Remote Sensing”.
Trends of yearly publications in “Deep Learning” AND “Remote Sensing”.

Figure 6:

Subject-wise research contributions in “Deep Learning” AND “Remote Sensing.” and “Machine Learning” AND “Remote Sensing”.
Subject-wise research contributions in “Deep Learning” AND “Remote Sensing.” and “Machine Learning” AND “Remote Sensing”.

Figure 7:

Global research outcomes in the “Machine Learning” AND “Remote Sensing” domain.
Global research outcomes in the “Machine Learning” AND “Remote Sensing” domain.

Figure 8:

Global research outcomes in the “Deep Learning” AND “Remote Sensing” domain.
Global research outcomes in the “Deep Learning” AND “Remote Sensing” domain.

Publications as per the funding sponsor in “Deep Learning” AND “Remote Sensing” and “Machine Learning” AND “Remote Sensing”

Selected funding agency “Deep Learning” AND “Remote Sensing” “Machine Learning” AND “Remote Sensing”
National Natural Science Foundation of China 3121 1903
National Key Research and Development Program of China 882 603
Fundamental Research Funds for the Central Universities 424 222
China Postdoctoral Science Foundation 221 113
Chinese Academy of Sciences 210 224
NSF 191 324
Ministry of Science and Technology of the People's Republic of China 167 126
Horizon 2020 Framework Programme 163 220
Ministry of Education of the People's Republic of China 135 82
National Aeronautics and Space Administration 128 376
Natural Science Foundation of Shandong Province 108 50
European Commission 105 175
China Scholarship Council 102 86
Conselho Nacional de Desenvolvimento Científico e Tecnológico 95 152
National Basic Research Program of China (973 Program) 93 65
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 81 138
Natural Science Foundation of Jiangsu Province 79 41
ESA 73 170
Natural Science Foundation of Beijing Municipality 72 35
National Research Foundation of Korea 68 63
European Research Council 67 82
Ministry of Finance 67 36
Natural Sciences and Engineering Research Council of Canada 66 93
Nvidia 66 24
Natural Science Foundation of Hubei Province 62 29
Horizon 2020 58 71
Sichuan Province Science and Technology Support Program 58 26
Deutsche Forschungsgemeinschaft 56 85
European Regional Development Fund 56 106
Japan Society for the Promotion of Science 49 67
Bundesministerium für Bildung und Forschung 46 72
U.S. DOE 44 80
U.S. Geological Survey 42 115
Natural Science Foundation of Guangdong Province 39 22
Higher Education Discipline Innovation Project 38 26
Key Technology Research and Development Program of Shandong 38 15
*Sub-Total is 7370 *Sub-Total is 6117
Other Funding Agencies 2155 2775
Total Funded Research Publications 9525 8892
Total Research Publications 11,381 11,289
Total Non-Funded Research Publications 1856 2397

Summary of exhaustive publications with “Remote Sensing” AND “Urban Environment Studies”

Authors Publications title Year Citations Funding details Document type
Barros et al. “Urban land use pattern identification using variogram on image” 2016 2 Polytechnic School; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Universidade de São Paulo, USP Article
Rau et al. “Analysis of oblique aerial images for land cover and point cloud classification in an Urban environment” 2015 73 National Science Council Taiwan, (102-2119-M-006-002) Article
Li et al. “WRF environment assessment in Guangzhou city with an extracted land-use map from the remote sensing data in 2000 as an example” 2014 2 National Natural Science Foundation of China, (51278262) Article
He et al. “Urban local climate zone mapping and apply in urban environment study” 2018 5 Ministry of Science and Technology, MOST; National Natural Science Foundation of China, NSFC, (51508458); Ministry of Science and Technology of the People's Republic of China, MOST, (SB2013FY112500) Conference paper
Sun et al. “Desert heat island study in winter by mobile transect and remote sensing techniques” 2009 47 Architecture and Building Institute; Ministère de l’Intérieur; National Science Council, NSC, (096-2917-I-006-011, NSC94-2211-E-006-069) Article

Search keywords and significant published articles

Selected keywords for search Number of published articles
Deep Learning AND “Urban Environment Studies” 1
Deep Learning AND “Urban Environmental Hazards Studies” 0
Deep Learning AND “Urban Environmental Disaster Studies” 0
Deep Learning AND “Urban Environmental Hazards and Disaster Studies” 0
Machine Learning AND “Urban Environment Studies” 1
Machine Learning AND “Urban Environmental Hazards Studies” 0
Machine Learning AND “Urban Environmental Disaster Studies” 0
Machine Learning AND “Urban Environmental Hazards and Disaster Studies” 0
Remote Sensing AND “Urban Environment Studies” 5
Remote Sensing AND “Urban Environmental Hazards Studies” 0
Remote Sensing AND “Urban Environmental Disaster Studies” 0
Remote Sensing AND “Urban Environmental Hazards and Disaster Studies” 0
Deep Learning AND “Remote Sensing” 11,381
Machine Learning AND “Remote Sensing” 11,289
Total 22,677
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Langue:
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