Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami

References

• Aber JS, Marzolff I, Ries JB. 2010. Chapter 3 - photogrammetry. In: Aber JS, Marzolff I, Ries JB BT-S-FAP, editors. Amsterdam: Elsevier; p. 23–39.
   Google Scholar
• Adriano B, Yokoya N, Xia J, Miura H, Liu W, Matsuoka M, Koshimura S. 2021. Learning from multimodal and multitemporal earth observation data for building damage mapping. ISPRS J Photogramm Remote Sens. 175:132–143.
   Web of Science ®Google Scholar
• Adriano B, Xia J, Baier G, Yokoya N, Koshimura S. 2019. Multi-source data fusion based on ensemble learning for rapid building damage mapping during the 2018 sulawesi earthquake and tsunami in Palu, Indonesia. Remote Sens. 11(7):886.
   Google Scholar
• Adriano B, Mas E, Koshimura S, Estrada M, Jimenez C. 2014. Scenarios of earthquake and tsunami damage probability in Callao region, Peru using tsunami fragility functions. J. Disaster Res. 9(6):968–975.
   Google Scholar
• Afida BA, Kamal M, Hadmoko DS. 2020. Identifikasi Kerusakan Bangunan Pasca Gempa Bumi Menggunakan Citra Satelit Worldview-2. JPK. 8(1):67–77.
   Google Scholar
• Aggarwal S. 2004. Principles of remote sensing. Satell Remote Sens GIS Appl Agric Meteorol. 23(2):23–28.
   Google Scholar
• Al-Abadi AM. 2018. Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study. Arab J Geosci. 11(9):1–19.
   Web of Science ®Google Scholar
• Belgiu M, Drăguţ L. 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens. 114:24–31.
   Web of Science ®Google Scholar
• Breiman L. 2001. Random forests. Mach Learn. 45(1):5–32.
   Web of Science ®Google Scholar
• Burrough PA, McDonnell RA, Lloyd CD. 2015. Principles of geographical information systems. United Kingdom: Oxford University Press.
   Google Scholar
• Cartwright JHE, Nakamura H. 2008. Tsunami: a history of the term and of scientific understanding of the phenomenon in Japanese and Western culture. Notes Rec R Soc Lond. 62(2):151–166.
   PubMedGoogle Scholar
• Cooner AJ, Shao Y, Campbell JB. 2016. Detection of urban damage using remote sensing and machine learning algorithms: revisiting the 2010 Haiti earthquake. Remote Sens. 8(10):868.
   Web of Science ®Google Scholar
• Copernicus 2019. Copernicus Emergency Management Service [Internet]. [accessed 2022 Apr 1]. https://emergency.copernicus.eu/mapping/ems/damage-assessment.
   Google Scholar
• Corbett-Davies S, Goel S. 2018. The measure and mismeasure of fairness: a critical review of fair machine learning. arXiv Prepr arXiv180800023; New York: Cornell University; p. 1–25.
   Google Scholar
• Cutler A, Cutler DR, Stevens JR. 2012. Random forests. In: ensemble Mach Learn. Boston, MA: Springer; p. 157–175.
   Google Scholar
• DeFries R, Levin SABT-E of B (Second E 2013. Remote Sensing and Image Processing [Internet]. In:, editor. Waltham: academic Press; p. 389–399.
   Google Scholar
• Department of Communications, Informatics P and SP 2022. Pandeglang One Data [Internet]. [accessed 2022 Apr 2]. https://satudata.pandeglangkab.go.id/index.php/kecamatan/detail/labuan.
   Google Scholar
• Dong L, Shan J. 2013. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS J Photogramm Remote Sens. [Internet]84:85–99.
   Web of Science ®Google Scholar
• Ebert JI. 2015. Photogrammetry, Photointerpretation, and Digital Imaging and Mapping in Environmental Forensics. Introd to Environ Forensics Third Ed. 39–64.
   Google Scholar
• European Space Agency 2018. User Guides - Sentinel-2 MSI - Level-2A Product - Sentinel Online - Sentinel Online [Internet]. [accessed 2022 Apr 1]. https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/product-types/level-2a.
   Google Scholar
• Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L. 2007. The shuttle radar topography mission. Rev Geophys. 45(2):1–33
   Web of Science ®Google Scholar
• Feng Q, Liu J, Gong J. 2015. UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sens. 7(1):1074–1094.
   Google Scholar
• Fernández-Delgado M, Cernadas E, Barro S, Amorim D. 2014. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 15(1):3133–3181.
   Google Scholar
• Goshtasby AA, Nikolov SG. 2007. Guest editorial: image fusion: advances in the state of the art. Inf Fusion Spec Issue Image Fusion Adv State Art. 8:114–118.
   Google Scholar
• Gupta R, Goodman B, Patel N, Hosfelt R, Sajeev S, Heim E, Doshi J, Lucas K, Choset H, Gaston M. 2019. Creating XBD: a dataset for assessing building damage from satellite imagery. IEEE Comput Soc Conf Comput Vis Pattern Recognit Work. 2019-June: 10–17.
   Google Scholar
• Hamzah L, Puspito NT, Imamura F. 2000. Tsunami Catalog and Zones in Indonesia. J Nat Disaster Sci. 22(1):25–43.
   Google Scholar
• Hancilar U, Taucer F, Corbane C. 2013. Empirical fragility functions based on remote sensing and field data after the 12 January 2010 Haiti earthquake. Earthq Spectra. 29(4):1275–1310.
   Web of Science ®Google Scholar
• Hu Z, Shi T, Wang C, Li Q, Wu G. 2021. Scale-sets image classification with hierarchical sample enriching and automatic scale selection. Int J Appl Earth Obs Geoinf. 105:102605.
   Web of Science ®Google Scholar
• Kaur H, Koundal D, Kadyan V. 2021. Image fusion techniques: a survey. Arch Comput Methods Eng. 28(7):4425–4447.
   PubMed Web of Science ®Google Scholar
• Kedzierski M, Wierzbicki D, Sekrecka A, Fryskowska A, Walczykowski P, Siewert J. 2019. Influence of lower atmosphere on the radiometric quality of unmanned aerial vehicle imagery. Remote Sens. 11(10):1214.
   Google Scholar
• Kerle N, Damen M. 2009. Guidance notes Session 2 : Obtaining spatial data for risk assessment. Guid B. 1–31.
   Google Scholar
• Kerle N, Nex F, Gerke M, Duarte D, Vetrivel A. 2019. UAV-based structural damage mapping: a review. IJGI. 9(1):14–23.
   Google Scholar
• Khodaverdizahraee N, Rastiveis H, Jouybari A. 2020. Segment-by-segment comparison technique for earthquake-induced building damage map generation using satellite imagery. Int J Disaster Risk Reduct. [Internet]46:101505.
   Web of Science ®Google Scholar
• Koshimura S, Moya L, Mas E, Bai Y. 2020. Tsunami damage detection with remote sensing: a review. Geosci. 10(5):177.
   Google Scholar
• Labombang M. 2011. Manajemen Risiko Dalam Proyek Konstruksi. Bangunan. 9(1):39–46.
   Google Scholar
• Le HQ, Hsiung B-CB. 2014. A novel mobile information system for risk management of adjacent buildings in urban underground construction. Geotech Eng J SEAGS AGSSEA. 45(3):52–63.
   Google Scholar
• Liu Y, Wang Y, Zhang J. 2012. New Machine Learning Algorithm: random Forest. BT - Information Computing and Applications. In: liu B, Ma M, Chang J, editors. Berlin, Heidelberg: springer Berlin Heidelberg; p. 246–252.
   Google Scholar
• Mangalathu S, Sun H, Nweke CC, Yi Z, Burton HV. 2020. Classifying earthquake damage to buildings using machine learning. Earthq Spectra. 36(1):183–208.
   Web of Science ®Google Scholar
• Maxar 2018. Satellite Imagery [Internet]. [accessed 2022 Apr 1]. https://www.maxar.com/imagery-leadership.
   Google Scholar
• Maxar 2019. Open Data Program Disaster Response Geospatial Analytics [Internet]. [accessed 2022 Apr 1]. https://www.maxar.com/open-data.
   Google Scholar
• Meilano I, Rahadian A, Suwardhi D, Suminar W, Atmaja F, Pratama C, Sunarti E, Haksama S. 2020. Analysis of damage to buildings affected by the tsunami in the Palu coastal area using deep learning.
   Google Scholar
• Meslem A, Yamazaki F, Maruyama Y. 2011. Accurate evaluation of building damage in the 2003 Boumerdes, Algeria Earthquake from Quickbird satellite images. J Earthquake Tsunami. 05(01):1–18.
   Google Scholar
• Ministry of Energy and Mineral Resources Republic of Indonesia 2018. Response to the Tsunami in the Sunda Strait, 22 December 2018 [Internet]. [accessed 2022 Apr 8]. https://www.esdm.go.id/id/media-center/arsip-berita/tanggapan-kejadian-tsunami-di-selat-sunda-tanggal-22-desember-2018.
   Google Scholar
• Ministry of Marine Affairs and Fisheries 2019. The Nation’s Future Sea, Let’s Keep Up Together [Internet]. [accessed 2022 Apr 2]. https://kkp.go.id/artikel/12993-laut-masa-depan-bangsa-mari-jaga-bersama.
   Google Scholar
• Monfort D, Negulescu C, Belvaux M. 2019. Remote sensing vs. field survey data in a post-earthquake context: potentialities and limits of damaged building assessment datasets. Remote Sens Appl Soc Environ. [Internet]. 14(February):46–59.
   Google Scholar
• Moya L, Muhari A, Adriano B, Koshimura S, Mas E, Marval-Perez LR, Yokoya N. 2020. Detecting urban changes using phase correlation and ℓ1-based sparse model for early disaster response: a case study of the 2018 Sulawesi Indonesia earthquake-tsunami. Remote Sens Environ. 242:111743.
   Web of Science ®Google Scholar
• Naito S, Tomozawa H, Mori Y, Nagata T, Monma N, Nakamura H, Fujiwara H, Shoji G. 2020. Building-damage detection method based on machine learning utilizing aerial photographs of the Kumamoto earthquake. Earthq Spectra. 36(3):1166–1187.
   Web of Science ®Google Scholar
• NOAA. What is a tsunami? [Internet]. [accessed 2022 Mar 23]. https://oceanservice.noaa.gov/facts/tsunami.html.
   Google Scholar
• Pattiaratchi C. 2014. Tsunamis — their causes and effects. (January 2005).
   Google Scholar
• Radhika S, Tamura Y, Matsui M. 2015. Cyclone damage detection on building structures from pre- and post-satellite images using wavelet based pattern recognition. J Wind Eng Ind Aerodyn. [Internet]. 136:23–33.
   Web of Science ®Google Scholar
• Saha K, Frøyen YK. 2021. Learning GIS Using Open Source Software: an Applied Guide for Geo-spatial Analysis. London: Routledge India.
   Google Scholar
• Speiser JL, Miller ME, Tooze J, Ip E. 2019. A comparison of random forest variable selection methods for classification prediction modeling. Expert Syst Appl. 134(336):93–101.
   PubMedGoogle Scholar
• Suppasri A, Koshimura S, Imai K, Mas E, Gokon H, Muhari A, Imamura F. 2012. Damage characteristic and field survey of the 2011 Great East Japan tsunami in Miyagi Prefecture. Coast Eng J. [Internet]. 54(1):1250005–1250030.
   Web of Science ®Google Scholar
• Suppasri A, Mas E, Charvet I, Gunasekera R, Imai K, Fukutani Y, Abe Y, Imamura F. 2013. Building damage characteristics based on surveyed data and fragility curves of the 2011 Great East Japan tsunami. Nat Hazards. 66(2):319–341.
   Web of Science ®Google Scholar
• Tang Y, Qiu F, Jing L, Shi F, Li X. 2021. A recurrent curve matching classification method integrating within-object spectral variability and between-object spatial association. Int J Appl Earth Obs Geoinf. 101:102367.
   Web of Science ®Google Scholar
• The National Agency for Disaster Countermeasure 2016. Disasters Risk of Indonesia. Int J Disaster Risk Sci [Internet].:22.
   Google Scholar
• The Parliamentary Office of Science and Technology 2020. Remote sensing and machine learning. UK Parliam POSTNOTE No 628.(628):p. 1–7.
   Google Scholar
• USGS. What is a digital elevation model (DEM) [Internet]. [accessed 2022 Apr 1]. https://www.usgs.gov/faqs/what-digital-elevation-model-dem.
   Google Scholar
• Wang Y, Li S, Teng F, Lin Y, Wang M, Cai H. 2022. Improved mask R-CNN for rural building roof type recognition from UAV high-resolution images: a case study in Hunan Province, China. Remote Sens. 14(2):265.
   Google Scholar
• Westen CV, Kingma N, Montoya L. 2009. Guide book Session 4: elements at Risk. Guid B. 1–43.
   Google Scholar
• Widyatmanti A. 2020. Sentinel-1 and Sentinel-2 Data Fusion Based on Random Forest Algorithm for Mapping Types of Damage to Buildings in Pemenang-Lombok Earthquake in 2018. Yogyakarta. Indonesia: Gajahmada University.
   Google Scholar
• Zamani Joharestani M, Cao C, Ni X, Bashir B, Talebiesfandarani S. 2019. PM2. 5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data. Atmosphere (Basel). 10(7):373.
   Web of Science ®Google Scholar
• Zhang R, Li H, Duan K, You S, Liu K, Wang F, Hu Y. 2020. Automatic detection of earthquake-damaged buildings by integrating UAV oblique photography and infrared thermal imaging. Remote Sens. 12(16):2621.
   Google Scholar