58 citations as recorded by crossref.

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5. Multi-Temporal Correlation Method for Damage Assessment of Buildings from High-Resolution SAR Images of the 2013 Typhoon Haiyan P. Nakmuenwai et al. 10.20965/jdr.2016.p0577
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7. Cartographier un territoire « cycloné » : Saint-Martin après le passage de l’ouragan Irma (Antilles du Nord, sept. 2017) T. Candela et al. 10.4000/mappemonde.7730
8. Crowdsourcing photograph locations for debris flow hot spot mapping H. Chu & Y. Chen 10.1007/s11069-017-3098-6
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13. An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing Y. Ma et al. 10.3390/rs8040272
14. Exploring the influence of citizen involvement on the assimilation of crowdsourced observations: a modelling study based on the 2013 flood event in the Bacchiglione catchment (Italy) M. Mazzoleni et al. 10.5194/hess-22-391-2018
15. Post-Disaster Recovery Assessment with Machine Learning-Derived Land Cover and Land Use Information M. Sheykhmousa et al. 10.3390/rs11101174
16. A Procedural Construction Method for Interactive Map Symbols Used for Disasters and Emergency Response G. Peng et al. 10.3390/ijgi6040095
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19. Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data S. Ghaffarian et al. 10.3390/rs11202427
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24. Assessing SDI Implementation Scenarios to Facilitate Emergency Mapping Operations in the Dominican Republic G. Rosario Michel et al. 10.3390/ijgi12050184
25. Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology J. Laso Bayas et al. 10.3390/rs8110905
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27. Rapid flood inundation mapping using social media, remote sensing and topographic data J. Rosser et al. 10.1007/s11069-017-2755-0
28. How engaging are disaster management related social media channels? The case of Australian state emergency organisations N. Kankanamge et al. 10.1016/j.ijdrr.2020.101571
29. Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcing J. Kohns et al. 10.1016/j.pdisas.2025.100427
30. Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks D. Duarte et al. 10.3390/rs10101636
31. Extraction and analysis of natural disaster-related VGI from social media: review, opportunities and challenges Y. Feng et al. 10.1080/13658816.2022.2048835
32. The Tasks of the Crowd: A Typology of Tasks in Geographic Information Crowdsourcing and a Case Study in Humanitarian Mapping J. Albuquerque et al. 10.3390/rs8100859
33. A 3D Shape Descriptor Based on Contour Clusters for Damaged Roof Detection Using Airborne LiDAR Point Clouds M. He et al. 10.3390/rs8030189
34. Enhanced urban functional land use map with free and open-source data T. Vu et al. 10.1080/17538947.2021.1970262
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36. UAV-Based Structural Damage Mapping: A Review N. Kerle et al. 10.3390/ijgi9010014
37. Disaster Ergonomics: a Human Factors approach to address escalating challenges from disasters C. Son 10.1007/s10111-023-00736-4
38. Cognitive Themes Emerging from Air Photo Interpretation Texts Published to 1960 R. Bianchetti & A. MacEachren 10.3390/ijgi4020551
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40. Geospatial Big Data-Based Geostatistical Zonation of Seismic Site Effects in Seoul Metropolitan Area H. Kim et al. 10.3390/ijgi6060174
41. Citizen Science and Crowdsourcing for Earth Observations: An Analysis of Stakeholder Opinions on the Present and Future S. Mazumdar et al. 10.3390/rs9010087
42. Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data G. Foody et al. 10.3390/ijgi7030080
43. The role of space-based observation in understanding and responding to active tectonics and earthquakes J. Elliott et al. 10.1038/ncomms13844
44. Crowdsourcing in Remote Sensing: A Review of Applications and Future Directions E. Saralioglu & O. Gungor 10.1109/MGRS.2020.2975132
45. Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning A. Vetrivel et al. 10.1016/j.isprsjprs.2017.03.001
46. Identifying Users’ Requirements for Emergency Mapping Team Operations in the Dominican Republic G. Rosario Michel et al. 10.3390/ijgi9030165
47. Towards Sustainable and Resilient Infrastructure: Hurricane-Induced Roadway Closure and Accessibility Assessment in Florida Using Machine Learning S. Takyi et al. 10.3390/su17093909
48. Evaluating Resilience-Centered Development Interventions with Remote Sensing N. Kerle et al. 10.3390/rs11212511
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51. Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review S. Matin & B. Pradhan 10.1080/10106049.2021.1933213
52. Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review S. Ghaffarian et al. 10.3390/rs10111760
53. Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation R. Ünlü & R. Kiriş 10.1007/s00371-020-02043-9
54. Factors affecting earthquake responders’ building damage information needs and use G. Bhattacharjee et al. 10.1177/87552930211030297
55. Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information L. See et al. 10.3390/ijgi5050055
56. Lahars and debris flows: Characteristics and impacts J. Thouret et al. 10.1016/j.earscirev.2019.103003
57. A Cognitive Assistance Framework for Supporting Human Workers in Industrial Tasks M. Haslgrubler et al. 10.1109/MITP.2018.053891337
58. Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions F. Nex et al. 10.3390/rs11232765