Artificial Intelligence in Post-Disaster Damage Assessment of the Built Environment: a Bibliometric Analysis

Abdi, G., Esfandiari, M., & Jabari, S. (2021). A deep transfer learning-based damage assessment on post-event very high-resolution orthophotos. Geomatica, 75 (4), 1-14, https://doi.org/10.1139/geomat-2021-0014.

AFAD. (2023). 6 Şubat Kahramanmaraş Depremleri. T.C. İçişleri Bakanlığı Afet ve Acil Durum Yönetimi Başkanlığı. Access date: 12.04.2025, https://www.afad.gov.tr/kahramanmarasta-meydana-gelen-depremler-hk-34#:~:text=06.02.2023%20tarihinde%20Kahramanmara%C5%9F%20ili,7.184%20art%C3%A7%C4%B1%20deprem%20meydana%20gelmi%C5%9Ftir.

Agbaje, T. H., Abomaye-Nimenibo, N., Ezeh, C. J., Bello, A., & Olorunnishola, A. (2024). Building damage assessment in aftermath of disaster events by leveraging GeoAI (Geospatial Artificial Intelligence): World Journal of Advanced Research and Reviews, 23(1), 667–687, https://doi.org/10.30574/wjarr.2024.23.1.2000.

Ahmadi, S. A., Mohammadzadeh, A., Yokoya, N., & Ghorbanian, A. (2024). BD-SKUNet: Selective-Kernel UNets for building damage assessment in high-resolution satellite images. Remote Sensing, 16(1), 182. https://doi.org/10.3390/rs16010182

Arciniegas, G., Bijker, W., Kerle, N., & Tolpekin, V. (2007). Coherence- and amplitude-based analysis of seismogenic damage in Bam, Iran, using ENVISAT ASAR data. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1571–1581. https://doi.org/10.1109/TGRS.2006.883149

Arumala, J. (2012). Impact of large-scale disasters on the built environment. Journal of Architectural Engineering, 12(3), 147–150. https://doi.org/10.1061/(ASCE)LM.1943-5630.0000175

Barrington, L., Ghosh, S., Greene, M., Har-Noy, S., Berger, J., Gill, S., Lin, A. Y. M., & Huyck, C. (2011). Crowdsourcing earthquake damage assessment using remote sensing imagery. Annals of Geophysics, 54(6). https://doi.org/10.4401/ag-5324

BBC. (2025). Fotoğraflar ve tanıklıklarla Los Angeles yangını: 'Geride dönecek hiçbir şey kalmadı', Access date: 12.04.2025, https://www.bbc.com/turkce/articles/cy0n0wvzqqko

Bihari, A., & Tripathi, S. (2015). Key author analysis in research professionals’ relationship network using citation ındices and centrality. Procedia Computer Science, 57, 606-613.

Bornmann, L., Marx, W., & Barth, A. (2013). The normalization of citation counts based on classification systems. Journal of Informetrics, 7(1), 158–165. https://doi.org/10.1016/j.joi.2012.10.001

Brunner, D., Lemoine, G., & Bruzzone, L. (2010). Earthquake damage assessment of buildings using VHR optical and SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 48(5), 2403–2420. https://doi.org/10.1109/TGRS.2009.2038274

Calantropio, A., Chiabrando, F., Codastefano, M., & Bourke, E. (2021). Deep learning for automatic building damage assessment: Application in post-disaster scenarios using UAV data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-1-2021, 113–120.

Chen, X., Zou, D., Kohnke, L., Xie, H., & Cheng, G. (2021). Affective states in digital game-based learning: Thematic evolution and social network analysis. PLoS ONE, 16(7), e0255184.

Cheng, C.-S., Behzadan, A. H., & Noshadravan, A. (2021). Deep learning for post-hurricane aerial damage assessment of buildings. Computer‐Aided Civil and Infrastructure Engineering, 36(6), 695–710. https://doi.org/10.1111/mice.12658

Cheng, C.-S., Behzadan, A. H., & Noshadravan, A. (2022). Uncertainty‐aware convolutional neural network for explainable artificial intelligence‐assisted disaster damage assessment. Struct Control Health Monit. 29(10):e3019. doi:10.1002/stc.3019

Corbane, C., Saito, K., Dell’Oro, L., & Gill, S. P. D. (2011). A comprehensive analysis of building damage in the 12 January 2010 Mw7 Haiti earthquake using high-resolution satellite and aerial imagery. Photogrammetric Engineering & Remote Sensing, 77(10), 997–1009. https://doi.org/10.14358/PERS.77.10.0997

Dong, L., & Shan, J. (2013). A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 84, 85–99. https://doi.org/10.1016/j.isprsjprs.2013.06.011

Duarte, D., Nex, F., Kerle, N., & Vosselman, G. (2018). Multi-resolution feature fusion for image classification of building damages with convolutional neural networks. Remote Sensing, 10(10), 1636. https://doi.org/10.3390/rs10101636

Dubey, P., Dubey, P., Agrawal, P. K., Chourasia, H., Nayak, M., & Gehani, H. (2023). Bibliometric analysis of data science research: A decade of insights from Web of Science. 2023 Fourth International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), 1–6. https://doi.org/10.1109/ICSTCEE60504.2023.10585030

Ehrlich, D., Guo, H. D., Molch, K., Ma, J. W., & Pesaresi, M. (2009). Identifying damage caused by the 2008 Wenchuan earthquake from VHR remote sensing data. International Journal of Digital Earth, 2(4), 309–326. https://doi.org/10.1080/17538940902767401

Fan, G., Li, J., Hao, H., & Xin, Y. (2021). Data driven structural dynamic response reconstruction using segment based generative adversarial networks. Engineering Structures, 234, 111970. https://doi.org/10.1016/j.engstruct.2021.111970

Fernandez Galarreta, J., Kerle, N., & Gerke, M. (2015). UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning. Natural Hazards and Earth System Sciences, 15(6), 1087–1101. https://doi.org/10.5194/nhess-15-1087-2015

Geiß, C., & Taubenböck, H. (2013). Remote sensing contributing to assess earthquake risk: From a literature review towards a roadmap. Natural Hazards, 68(1), 7–48. https://doi.org/10.1007/s11069-012-0322-2

Ghaffarian, S., Kerle, N., & Filatova, T. (2018). Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review. Remote Sensing, 10(11), 1760. https://doi.org/10.3390/rs10111760

Ghosh, S., Huyck, C., Greene, M., Gill, S. P., Bevington, J., Svekla, W., DesRoches, R., & Eguchi, R. T. (2011). Crowdsourcing for rapid damage assessment: The Global Earth Observation Catastrophe Assessment Network (GEO-CAN). Earthquake Spectra, 27(S1), S179–S198. https://doi.org/10.1193/1.3636416

Gong, L., Wang, C., Wu, F., Zhang, J., Zhang, H., & Li, Q. (2016). Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery. Remote Sensing, 8(11), 887. https://doi.org/10.3390/rs8110887

Janalipour, M., & Mohammadzadeh, A. (2016). Building damage detection using object-based image analysis and ANFIS from high-resolution image (Case study: BAM earthquake, Iran). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(5), 1937–1945. https://doi.org/10.1109/JSTARS.2015.2458582

Ji, M., Liu, L., Du, R., & Buchroithner, M. F. (2019). A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery. Remote Sensing, 11(10), 1202. https://doi.org/10.3390/rs11101202

Kakooei, M., & Baleghi, Y. (2017). Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment. International Journal of Remote Sensing, 38(8–10), 2511–2534. https://doi.org/10.1080/01431161.2017.1294780.

Kerle, N. (2013). Remote sensing based post-disaster damage mapping with collaborative methods. In S. Zlatanova, R. Peters, A. Dilo, & H. Scholten (Eds.), Intelligent systems for crisis management (pp. 121–133). Springer. Lecture Notes in Geoinformation and Cartography. https://doi.org/10.1007/978-3-642-33218-0_10

Kerle, N., Nex, F., Gerke, M., Duarte, D., & Vetrivel, A. (2020). UAV-Based Structural Damage Mapping: A Review. ISPRS International Journal of Geo-Information, 9(1), 14. https://doi.org/10.3390/ijgi9010014

Khankeshizadeh, E., Mohammadzadeh, A., Arefi, H., Mohsenifar, A., Pirasteh, S., Fan, E., Li, H., & Li, J. (2024). A novel weighted ensemble transferred U-Net based model (WETUM) for postearthquake building damage assessment from UAV data: A comparison of deep learning- and machine learning-based approaches. IEEE Transactions on Geoscience and Remote Sensing, 62, 4701317, 1–17. https://doi.org/10.1109/TGRS.2024.3354737

Kwiek, M. (2018). International Research Collaboration and International Research Orientation: Comparative Findings About European Academics. Journal of Studies in International Education, 22(2), 136-160.

Lee, I. S., Lee, H., Chen, Y. H., & Chae, Y. (2020). Bibliometric analysis of research assessing the use of acupuncture for pain treatment over the past 20 years. Journal of Pain Research, 367–376.

Ma, H., Liu, Y., Ren, Y., & Yu, J. (2020). Detection of collapsed buildings in post-earthquake remote sensing images based on the improved YOLOv3. Remote Sensing, 12(1), 44. https://doi.org/10.3390/rs12010044

Mangalathu, S., & Burton, H. V. (2019). Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions. International Journal of Disaster Risk Reduction, 36, 101111. https://doi.org/10.1016/j.ijdrr.2019.101111

Marin, C., Bovolo, F., & Bruzzone, L. (2015). Building change detection in multitemporal very high resolution SAR images. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2664–2682. https://doi.org/10.1109/TGRS.2014.2363548

Nex, F., Duarte, D., Steenbeek, A., & Kerle, N. (2019). Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions. Remote Sensing, 11(3), 287. https://doi.org/10.3390/rs11030287

Nikolentzos, G., Panagopoulos, G., Evdaimon, I., Vazirgiannis, M. (2021). Can Author Collaboration Reveal Impact? The Case of h-index. In: Manolopoulos, Y., Vergoulis, T. (eds) Predicting the Dynamics of Research Impact. Springer, Cham. https://doi.org/10.1007/978-3-030-86668-6_8

Pan, X., & Yang, T. Y. (2020). Post-disaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks. Computer‐Aided Civil and Infrastructure Engineering, 35(5), 495–510. https://doi.org/10.1111/mice.12549

Qing, Y., Ming, D., Wen, Q., Weng, Q., Xu, L., Chen, Y., Zhang, Y., & Zeng, B. (2022). Operational earthquake-induced building damage assessment using CNN-based direct remote sensing change detection on superpixel level. International Journal of Applied Earth Observation and Geoinformation, 112, 102899. https://doi.org/10.1016/j.jag.2022.102899

Rahm, E. (2008). Comparing the scientific impact of conference and journal publications in computer science. Information Services & Use, 28(2), 127-128.

Rahman, T., & Zhu, S. (2024). Prosocial responses in disasters: Zooming out to the built environment and resilience. International Sustainable Cities and Society, 103, 105245. https://doi.org/10.1016/j.scs.2024.105245.

Randolph, J. J., Julnes, G., Bednarik, R., & Sutinen, E. (2007). A comparison of the methodological quality of articles in computer science education journals and conference proceedings. Computer Science Education, 17(4), 263-274. https://doi.org/10.1080/08993400701483517

Santos-Reyes, J., & Beard, A. (2010). A systemic approach to managing natural disasters. In M. E. Jennex (Ed) Advanced ICTs for Disaster Management and Threat Detection: Collaborative and Distributed Frameworks (pp. 1–21). IGI Global.

Shen, Y., Wu, Y., Liu, H., Sun, J., Li, Y., & Du, Q. (2022). BDANet: Multiscale convolutional neural network with cross-directional attention for building damage assessment from satellite images. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5402114, 1–14. https://doi.org/10.1109/TGRS.2021.3080580

Takhtkeshha, N., Mohammadzadeh, A., & Salehi, B. (2023). A Rapid Self-Supervised Deep-Learning-Based Method for Post-Earthquake Damage Detection Using UAV Data (Case Study: Sarpol-e Zahab, Iran). Remote Sensing, 15(1), 123. https://doi.org/10.3390/rs15010123

Tong, X., Hong, Z., Liu, S., Zhang, X., Xie, H., Li, Z., Yang, S., Wang, W., & Bao, F. (2012). Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 13–27. https://doi.org/10.1016/j.isprsjprs.2011.12.004

Tong, X., Lin, X., Feng, T., Xie, H., Liu, S., Hong, Z., & Chen, P. (2013). Use of shadows for detection of earthquake-induced collapsed buildings in high-resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 79, 53–67. https://doi.org/10.1016/j.isprsjprs.2013.01.012

USGS. (2025). M 6.4 - M 7.7 - 2025 Mandalay, Burma (Myanmar) Earthquake, Access date: 12.04.2025, https://www.usgs.gov/news/featured-story/m77-mandalay-burma-myanmar-earthquake

Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

Vetrivel, A., Gerke, M., Kerle, N., Nex, F., & Vosselman, G. (2018). 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. ISPRS Journal of Photogrammetry and Remote Sensing, 140, 45–59. https://doi.org/10.1016/j.isprsjprs.2017.03.001

Vetrivel, A., Gerke, M., Kerle, N., & Vosselman, G. (2015). Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 61–78. https://doi.org/10.1016/j.isprsjprs.2015.03.016

Waltman, L., & van Eck, N. J. (2015). Field-normalized citation impact indicators and the choice of an appropriate counting method. Journal of Informetrics, 9(4), 872–894. https://doi.org/10.1016/j.joi.2015.08.001

Xie, H., Zhang, Y., Wu, Z., & Lv, T. (2020). A bibliometric analysis on land degradation: Current status, development, and future directions. Land, 9(1), 28. https://doi.org/10.3390/land9010028

Zhang, D., Zhang, Y., Li, Q., Plummer, T., & Wang, D. (2019). CrowdLearn: A crowd-AI hybrid system for deep learning-based damage assessment applications. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), Dallas, USA, 2019, pp. 1221-1232, doi: 10.1109/ICDCS.2019.00123.

Zhang, R., Liu, Y., & Sun, H. (2020). Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling. Engineering Structures, 215, 110704. https://doi.org/10.1016/j.engstruct.2020.110704

Zhang, X., Ding, Y., Wang, J., & Zhu, J. (2022) Exploring the role of ınternational research collaboration in building China’s World-Class Universities. Sustainability, 14(6), 3487. https://doi.org/10.3390/su14063487

Zhang, Y., Zong, R., Shang, L., Zeng, H., Yue, Z., Wei, N., & Wang, D. (2023). On optimizing model generality in AI-based disaster damage assessment: A subjective logic-driven crowd-AI hybrid learning approach. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2023), 6317–6325. https://doi.org/10.24963/ijcai.2023/701

Zheng, Z., Zhong, Y., Wang, J., Ma, A., & Zhang, L. (2021). Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters. Remote Sensing of Environment, 265, 112636. https://doi.org/10.1016/j.rse.2021.112636