62 citations as recorded by crossref.

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2. Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China) Y. Gong et al. https://doi.org/10.3390/rs13071321
3. Emergency management through information crowdsourcing L. Grassi et al. https://doi.org/10.1016/j.ipm.2023.103386
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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. https://doi.org/10.20965/jdr.2016.p0577
6. Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake S. Xie et al. https://doi.org/10.3390/rs8090759
7. Cartographier un territoire « cycloné » : Saint-Martin après le passage de l’ouragan Irma (Antilles du Nord, sept. 2017) T. Candela et al. https://doi.org/10.4000/mappemonde.7730
8. Crowdsourcing photograph locations for debris flow hot spot mapping H. Chu & Y. Chen https://doi.org/10.1007/s11069-017-3098-6
9. Can volunteer crowdsourcing reduce disaster risk? A systematic review of the literature N. Kankanamge et al. https://doi.org/10.1016/j.ijdrr.2019.101097
10. Real-time mapping of natural disasters using citizen update streams I. Subasinghe et al. https://doi.org/10.1080/13658816.2019.1639185
11. Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach A. Vetrivel et al. https://doi.org/10.3390/rs8030231
12. G‐DIF: A geospatial data integration framework to rapidly estimate post‐earthquake damage S. Loos et al. https://doi.org/10.1177/8755293020926190
13. An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing Y. Ma et al. https://doi.org/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. https://doi.org/10.5194/hess-22-391-2018
15. Post-hurricane building damage assessment using street-view imagery and structured data: a multimodal deep learning approach X. Zhang et al. https://doi.org/10.1007/s44285-026-00070-x
16. Post-Disaster Recovery Assessment with Machine Learning-Derived Land Cover and Land Use Information M. Sheykhmousa et al. https://doi.org/10.3390/rs11101174
17. A Procedural Construction Method for Interactive Map Symbols Used for Disasters and Emergency Response G. Peng et al. https://doi.org/10.3390/ijgi6040095
18. Robotic Cross-Platform Sensor Fusion and Augmented Visualization for Large Indoor Space Reality Capture F. Xu et al. https://doi.org/10.1061/(ASCE)CP.1943-5487.0001047
19. Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images A. Vetrivel et al. https://doi.org/10.1016/j.isprsjprs.2015.03.016
20. Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data S. Ghaffarian et al. https://doi.org/10.3390/rs11202427
21. Disaster mapping by citizens is limited N. Kerle https://doi.org/10.1038/517438d
22. Mobile augmented reality in support of building damage and safety assessment W. Kim et al. https://doi.org/10.5194/nhess-16-287-2016
23. Micro-tasking as a method for human assessment and quality control in a geospatial data import A. Sveen et al. https://doi.org/10.1080/15230406.2019.1659187
24. Visualising post-disaster damage on maps: a user study T. Candela et al. https://doi.org/10.1080/13658816.2022.2063872
25. Assessing SDI Implementation Scenarios to Facilitate Emergency Mapping Operations in the Dominican Republic G. Rosario Michel et al. https://doi.org/10.3390/ijgi12050184
26. Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology J. Laso Bayas et al. https://doi.org/10.3390/rs8110905
27. Satellite image analysis using crowdsourcing data for collaborative mapping: current and opportunities W. Su et al. https://doi.org/10.1080/17538947.2018.1556352
28. 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
29. How engaging are disaster management related social media channels? The case of Australian state emergency organisations N. Kankanamge et al. https://doi.org/10.1016/j.ijdrr.2020.101571
30. Building damage assessment in natural disasters: A trans- and interdisciplinary approach combining domain knowledge, 3D machine learning, and crowdsourcing J. Kohns et al. https://doi.org/10.1016/j.pdisas.2025.100427
31. Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks D. Duarte et al. https://doi.org/10.3390/rs10101636
32. 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
33. The Tasks of the Crowd: A Typology of Tasks in Geographic Information Crowdsourcing and a Case Study in Humanitarian Mapping J. Albuquerque et al. https://doi.org/10.3390/rs8100859
34. A 3D Shape Descriptor Based on Contour Clusters for Damaged Roof Detection Using Airborne LiDAR Point Clouds M. He et al. https://doi.org/10.3390/rs8030189
35. Enhanced urban functional land use map with free and open-source data T. Vu et al. https://doi.org/10.1080/17538947.2021.1970262
36. Construction of collaborative mapping engine for dynamic disaster and emergency response G. Peng et al. https://doi.org/10.1007/s11069-017-3045-6
37. UAV-Based Structural Damage Mapping: A Review N. Kerle et al. https://doi.org/10.3390/ijgi9010014
38. Disaster Ergonomics: a Human Factors approach to address escalating challenges from disasters C. Son https://doi.org/10.1007/s10111-023-00736-4
39. Cognitive Themes Emerging from Air Photo Interpretation Texts Published to 1960 R. Bianchetti & A. MacEachren https://doi.org/10.3390/ijgi4020551
40. UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning J. Fernandez Galarreta et al. https://doi.org/10.5194/nhess-15-1087-2015
41. Geospatial Big Data-Based Geostatistical Zonation of Seismic Site Effects in Seoul Metropolitan Area H. Kim et al. https://doi.org/10.3390/ijgi6060174
42. Citizen Science and Crowdsourcing for Earth Observations: An Analysis of Stakeholder Opinions on the Present and Future S. Mazumdar et al. https://doi.org/10.3390/rs9010087
43. 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. https://doi.org/10.3390/ijgi7030080
44. The role of space-based observation in understanding and responding to active tectonics and earthquakes J. Elliott et al. https://doi.org/10.1038/ncomms13844
45. Crowdsourcing in Remote Sensing: A Review of Applications and Future Directions E. Saralioglu & O. Gungor https://doi.org/10.1109/MGRS.2020.2975132
46. 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. https://doi.org/10.1016/j.isprsjprs.2017.03.001
47. Identifying Users’ Requirements for Emergency Mapping Team Operations in the Dominican Republic G. Rosario Michel et al. https://doi.org/10.3390/ijgi9030165
48. Trail Triage: Identifying Collaborative Workflows in Trail Management Assisted by Crowdsourced Reports Y. Cao et al. https://doi.org/10.1145/3799453
49. Towards Sustainable and Resilient Infrastructure: Hurricane-Induced Roadway Closure and Accessibility Assessment in Florida Using Machine Learning S. Takyi et al. https://doi.org/10.3390/su17093909
50. Evaluating Resilience-Centered Development Interventions with Remote Sensing N. Kerle et al. https://doi.org/10.3390/rs11212511
51. Detection of seismic façade damages with multi-temporal oblique aerial imagery D. Duarte et al. https://doi.org/10.1080/15481603.2020.1768768
52. Application and prospect of a high-resolution remote sensing and geo-information system in estimating earthquake casualties T. Feng et al. https://doi.org/10.5194/nhess-14-2165-2014
53. Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review S. Matin & B. Pradhan https://doi.org/10.1080/10106049.2021.1933213
54. Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review S. Ghaffarian et al. https://doi.org/10.3390/rs10111760
55. A Stacking Ensemble Approach for Postdisaster Building Damage Assessment S. Lin et al. https://doi.org/10.1061/JCCEE5.CPENG-7005
56. AI and big data in disaster response: Ethical and practical challenges E. Wood https://doi.org/10.1016/j.jdd.2025.100041
57. Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation R. Ünlü & R. Kiriş https://doi.org/10.1007/s00371-020-02043-9
58. Factors affecting earthquake responders’ building damage information needs and use G. Bhattacharjee et al. https://doi.org/10.1177/87552930211030297
59. Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information L. See et al. https://doi.org/10.3390/ijgi5050055
60. Lahars and debris flows: Characteristics and impacts J. Thouret et al. https://doi.org/10.1016/j.earscirev.2019.103003
61. A Cognitive Assistance Framework for Supporting Human Workers in Industrial Tasks M. Haslgrubler et al. https://doi.org/10.1109/MITP.2018.053891337
62. Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions F. Nex et al. https://doi.org/10.3390/rs11232765