86 citations as recorded by crossref.

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19. A Generic Spatial OLAP Model for Evaluating Natural Hazards in a Volunteered Geographic Information Context S. Bimonte et al. https://doi.org/10.4018/ijaeis.2014100102
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23. Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data C. Cheng et al. https://doi.org/10.3390/ijgi8080358
24. Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach H. Vahidi et al. https://doi.org/10.3390/rs10071134
25. Social media analytics for natural disaster management Z. Wang & X. Ye https://doi.org/10.1080/13658816.2017.1367003
26. Multiscale Observation Product (MOP) for Temporal Flood Inundation Mapping of the 2015 Dallas Texas Flood E. Sava et al. https://doi.org/10.3390/rs15061615
27. Investigating Road-Constrained Spatial Distributions and Semantic Attractiveness for Area of Interest H. Ma et al. https://doi.org/10.3390/su11174624
28. Social Media: New Perspectives to Improve Remote Sensing for Emergency Response J. Li et al. https://doi.org/10.1109/JPROC.2017.2684460
29. Detecting flood inundation information through Twitter: The 2015 Kinu River flood disaster in Japan Y. Shi et al. https://doi.org/10.2328/jnds.40.1
30. Sentinel-2 Satellite Imagery for Agronomic and Quality Variability Assessment of Pistachio (Pistacia vera L.) E. Barajas et al. https://doi.org/10.3390/su12208437
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33. Fusing Social Media, Remote Sensing, and Fire Dynamics to Track Wildland-Urban Interface Fire W. Zhong et al. https://doi.org/10.3390/rs15153842
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35. Real Time Estimation of the Calgary Floods Using Limited Remote Sensing Data E. Schnebele et al. https://doi.org/10.3390/w6020381
36. Multi-phase Oil Tank Recognition for High Resolution Remote Sensing Images C. Liu et al. https://doi.org/10.31209/2018.100000033
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38. Geoinformation Harvesting From Social Media Data: A community remote sensing approach X. Zhu et al. https://doi.org/10.1109/MGRS.2022.3219584
39. Joint use of remote sensing data and volunteered geographic information for exposure estimation: evidence from Valparaíso, Chile C. Geiß et al. https://doi.org/10.1007/s11069-016-2663-8
40. Integrating remote sensing and social sensing for flood mapping R. Sadiq et al. https://doi.org/10.1016/j.rsase.2022.100697
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43. Emergency Response Using Volunteered Passenger Aircraft Remote Sensing Data: A Case Study on Flood Damage Mapping C. Wang et al. https://doi.org/10.3390/s19194163
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48. Extraction and analysis of natural disaster-related VGI from social media: review, opportunities and challenges Y. Feng et al. https://doi.org/10.1080/13658816.2022.2048835
49. Rapid flood inundation mapping using social media, remote sensing and topographic data J. Rosser et al. https://doi.org/10.1007/s11069-017-2755-0
50. Social media as an information source for rapid flood inundation mapping J. Fohringer et al. https://doi.org/10.5194/nhess-15-2725-2015
51. The Synergy Between Remote Sensing and Social Sensing in Urban Studies: Review and perspectives X. Xing et al. https://doi.org/10.1109/MGRS.2023.3343968
52. Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China Y. Meng et al. https://doi.org/10.3390/su9091547
53. Remote Sensing, natural hazards and the contribution of ESA Sentinels missions D. Poursanidis & N. Chrysoulakis https://doi.org/10.1016/j.rsase.2017.02.001
54. Road assessment after flood events using non-authoritative data E. Schnebele et al. https://doi.org/10.5194/nhess-14-1007-2014
55. Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence K. Bhuyan et al. https://doi.org/10.1007/s11069-022-05612-4
56. Comprehensive risk assessment of natural disasters based on machine learning in Changzhou City, China W. Ju et al. https://doi.org/10.1007/s10668-024-05531-3
57. Related concepts, research frameworks, and development prospects of immediate remote sensing detection of potential surface anomalies X. Yang et al. https://doi.org/10.1007/s11430-024-1551-1
58. An Integrated Method Combining Remote Sensing Data and Local Knowledge for the Large-Scale Estimation of Seismic Loss Risks to Buildings in the Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China G. Su et al. https://doi.org/10.3390/rs70302543
59. Spatial Technology and Social Media in Remote Sensing: A Survey J. Li et al. https://doi.org/10.1109/JPROC.2017.2729890
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66. Integration of Crowdsourced Images, USGS Networks, Remote Sensing, and a Model to Assess Flood Depth during Hurricane Florence C. Hultquist & G. Cervone https://doi.org/10.3390/rs12050834
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70. Citizen scientists’ engagement in flood risk-related data collection: a case study in Bui River Basin, Vietnam H. Tran et al. https://doi.org/10.1007/s10661-024-12419-2
71. Automatic identification of eyewitness messages on twitter during disasters K. Zahra et al. https://doi.org/10.1016/j.ipm.2019.102107
72. Using volunteered geographical information to map the November 2012 floods in Slovenia M. Triglav-Čekada & D. Radovan https://doi.org/10.5194/nhess-13-2753-2013
73. VGI and Satellite Imagery Integration for Crisis Mapping of Flood Events A. Vavassori et al. https://doi.org/10.3390/ijgi11120611
74. Understanding social media data for disaster management Y. Xiao et al. https://doi.org/10.1007/s11069-015-1918-0
75. Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery Q. Huang & Y. Xiao https://doi.org/10.3390/ijgi4031549
76. Monitoring the environment and human sentiment on the Great Barrier Reef: Assessing the potential of collective sensing S. Becken et al. https://doi.org/10.1016/j.jenvman.2017.07.007
77. Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information Y. Lin et al. https://doi.org/10.3390/rs12040706
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82. Can the Quality of the Potential Flood Risk Maps be Evaluated? A Case Study of the Social Risks of Floods in Central Spain J. Garrote et al. https://doi.org/10.3390/w11061284
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