[73] [74] [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] [85] [29] [86] [87] 26 27 28 29 30 31 32 33 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE 34 35 36 37 38 39 40 41 Ï Ï Ï Data collection; data interpretation; classification Ï Damage assessment 0 5 Elsevier Wiley MDPI Lidar Google Maps, Bing Maps, Geo-Wiki Google Earth, GeoWiki, MSIs MSIs 395 Elsevier 4 24 MDPI MDPI 293 MDPI MDPI 17 Elsevier Google Earth 2 MDPI MDPI 12 MDPI Google Earth Google Panoramio, MSIs 0 MDPI MSIs 3 1 19 Elsevier OSM 3 Elsevier (Continued) 2016 2016 2009 2015 2010 2016 2018 2018 2019 2018 2017 2019 2018 2016 2019 2018 CITATIONS YEAR 7 PUBLISHER Elsevier MSIs SENSOR AND PLATFORM Ï Data collection; data interpretation NEW PLATFORM Ï Validate and scale plant phenology Data collection; data interpretation Review Review Ï Improve land cover Disaster risk governance Crowdsourcing geospatial data Data collection Ï Land cover Ï Pixelwise classification (unsupervised) Ï Crop mapping Ï Radiometric correction MOBILE Ï Water measurements Classification; data collection SPECTRAL Ï Ï Detection of brick kilns SPATIAL Ï Data collection Ï Grassland monitoring Pixelwise and object base classification Data collection; classification Classification Ï Classification of earthquake damage Mapping agricultural field 3D information extraction ARTICLE MSIS/HYPERSPECTRAL IMAGES Ï Rule-based classification Statistical analysis Design-based inference METHOD RED, GREEN, BLUE Ï Land cover Monitoring crop phenology Land cover area estimation and accuracy assessment APPLICATION REFERENCE FIELD PAPER AERIAL TEMPORAL TABLE 1. A SUMMARY OF PAPERS ABOUT CROWDSOURCING AND REMOTE SENSING. (CONTINUED) SAR DECEMBER 2020 Ï Ï Ï LIDAR Ï Ï Ï Ï Ï Ï 95 VIDEO RADAR