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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 |