{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:13:50Z","timestamp":1780391630055,"version":"3.54.1"},"reference-count":72,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001775","name":"University of Technology Sydney","doi-asserted-by":"publisher","award":["Centre for Advanced Modelling & Geospatial Information Systems (CAMGIS)"],"award-info":[{"award-number":["Centre for Advanced Modelling & Geospatial Information Systems (CAMGIS)"]}],"id":[{"id":"10.13039\/501100001775","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Building-damage mapping using remote sensing images plays a critical role in providing quick and accurate information for the first responders after major earthquakes. In recent years, there has been an increasing interest in generating post-earthquake building-damage maps automatically using different artificial intelligence (AI)-based frameworks. These frameworks in this domain are promising, yet not reliable for several reasons, including but not limited to the site-specific design of the methods, the lack of transparency in the AI-model, the lack of quality in the labelled image, and the use of irrelevant descriptor features in building the AI-model. Using explainable AI (XAI) can lead us to gain insight into identifying these limitations and therefore, to modify the training dataset and the model accordingly. This paper proposes the use of SHAP (Shapley additive explanation) to interpret the outputs of a multilayer perceptron (MLP)\u2014a machine learning model\u2014and analyse the impact of each feature descriptor included in the model for building-damage assessment to examine the reliability of the model. In this study, a post-event satellite image from the 2018 Palu earthquake was used. The results show that MLP can classify the collapsed and non-collapsed buildings with an overall accuracy of 84% after removing the redundant features. Further, spectral features are found to be more important than texture features in distinguishing the collapsed and non-collapsed buildings. Finally, we argue that constructing an explainable model would help to understand the model\u2019s decision to classify the buildings as collapsed and non-collapsed and open avenues to build a transferable AI model.<\/jats:p>","DOI":"10.3390\/s21134489","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T02:44:39Z","timestamp":1625107479000},"page":"4489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)"],"prefix":"10.3390","volume":"21","author":[{"given":"Sahar S.","family":"Matin","sequence":"first","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea"},{"name":"Center of Excellence for Climate Change Research, King Abdulaziz University, P.O. Box 80234, Jeddah 21589, Saudi Arabia"},{"name":"Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"ref_1","first-page":"9","article-title":"San Francisco in ruins: The 1906 serial photographs of George R. Lawrence","volume":"30","author":"Baker","year":"1989","journal-title":"Landscape"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Duan, F., Gong, H., and Zhao, W. (2010, January 18\u201320). Collapsed houses automatic identification based on texture changes of post-earthquake aerial remote sensing image. Proceedings of the 2010 18th International Conference on Geoinformatics, Beijing, China.","DOI":"10.1109\/GEOINFORMATICS.2010.5567622"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kerle, N., Nex, F., Gerke, M., Duarte, D., and Vetrivel, A. (2020). UAV-based structural damage mapping: A review. ISPRS Int. J Geo-Inf., 9.","DOI":"10.3390\/ijgi9010014"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1109\/JSTARS.2015.2458582","article-title":"Building damage detection using object-based image analysis and ANFIS from high-resolution image (Case study: BAM earthquake, Iran)","volume":"9","author":"Janalipour","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ma, H., Liu, Y., Ren, Y., Wang, D., Yu, L., and Yu, J. (2020). Improved CNN classification method for groups of buildings damaged by earthquake, based on high resolution remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12020260"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3369","DOI":"10.1080\/01431161003727671","article-title":"Building-damage detection using post-seismic high-resolution SAR satellite data","volume":"31","author":"Balz","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ma, Y., Chen, F., Liu, J., He, Y., Duan, J., and Li, X. (2016). An automatic procedure for early disaster change mapping based on optical remote sensing. Remote Sens., 8.","DOI":"10.3390\/rs8040272"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1108\/DPM-12-2012-0148","article-title":"Towards a rapid automatic detection of building damage using remote sensing for disaster management: The 2010 Haiti earthquake","volume":"23","author":"Pham","year":"2014","journal-title":"Disaster Prev. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Syifa, M., Kadavi, P.R., and Lee, C.-W. (2019). An artificial intelligence application for post-earthquake damage mapping in Palu, central Sulawesi, Indonesia. Sensors, 19.","DOI":"10.3390\/s19030542"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., and Alamri, A.M. (2020). An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images. Geocarto Int., 1\u201316.","DOI":"10.1080\/10106049.2020.1856199"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dikshit, A., Pradhan, B., and Alamri, A.M. (2020). Pathways and challenges of the application of artificial intelligence to geohazards modelling. Gondwana Res.","DOI":"10.1016\/j.gr.2020.08.007"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bai, Y., Mas, E., and Koshimura, S. (2018). Towards operational satellite-based damage-mapping using u-net convolutional network: A case study of 2011 tohoku earthquake-tsunami. Remote Sens., 10.","DOI":"10.3390\/rs10101626"},{"key":"ref_13","unstructured":"Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., and Al-Fuqaha, A. (2020). Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking inside the black-box: A survey on explainable artificial intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","unstructured":"Gunning, D. (2017). Explainable artificial intelligence (xai). Def. Adv. Res. Proj. Agency Nd Web, 2, Available online: https:\/\/www.cc.gatech.edu\/~alanwags\/DLAI2016\/(Gunning)%20IJCAI-16%20DLAI%20WS.pdf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"Farabet","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2013.06.011","article-title":"A comprehensive review of earthquake-induced building damage detection with remote sensing techniques","volume":"84","author":"Dong","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2876","DOI":"10.1109\/JPROC.2012.2196404","article-title":"Remote sensing and earthquake damage assessment: Experiences, limits, and perspectives","volume":"100","author":"Gamba","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Adriano, B., Xia, J., Baier, G., Yokoya, N., and 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.","DOI":"10.3390\/rs11070886"},{"key":"ref_21","first-page":"987","article-title":"GIS and image understanding for near-real-time earthquake damage assessment","volume":"64","author":"Gamba","year":"1998","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/BF02989909","article-title":"Damage assessment after 2001 Gujarat earthquake using Landsat-7 satellite images","volume":"29","author":"Yusuf","year":"2001","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_23","unstructured":"Sugiyama, M.I.T.G.T., and Abe, H.S.K. (2002, January 23\u201325). Detection of Earthquake Damaged Areas from Aerial Photographs by Using Color and Edge Information. Proceedings of the 5th Asian Conference on Computer Vision, Melbourne, Australia."},{"key":"ref_24","first-page":"59","article-title":"Change detection of remote sensing image for earthquake-damaged buildings and its application in seismic disaster assessment","volume":"11","author":"Zhang","year":"2002","journal-title":"J. Nat. Disasters"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1193\/1.2101047","article-title":"Damage patterns from satellite images of the 2003 Bam, Iran, earthquake","volume":"21","author":"Rathje","year":"2005","journal-title":"Earthq. Spectra"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3013","DOI":"10.1080\/01431160601094492","article-title":"Rapid damage assessment of built-up structures using VHR satellite data in tsunami-affected areas","volume":"28","author":"Pesaresi","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160512331316810","article-title":"Automatic detection of earthquake-damaged buildings using DEMs created from pre-and post-earthquake stereo aerial photographs","volume":"26","author":"Turker","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rezaeian, M., and Gruen, A. (2007). Automatic Classification of Collapsed Buildings Using Object and Image Space Features. Geomatics Solutions for Disaster Management, Springer.","DOI":"10.1007\/978-3-540-72108-6_10"},{"key":"ref_29","unstructured":"Rezaeian, M. (2010). Assessment of Earthquake Damages by Image-Based Techniques, ETH Zurich."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4433","DOI":"10.1080\/01431160600675895","article-title":"Satellite radar and optical remote sensing for earthquake damage detection: Results from different case studies","volume":"27","author":"Stramondo","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1111\/j.1477-9730.2008.00501.x","article-title":"Contribution of two plane detection algorithms to recognition of intact and damaged buildings in lidar data","volume":"23","author":"Rehor","year":"2008","journal-title":"Photogramm. Rec."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.14358\/PERS.77.10.1011","article-title":"Building extraction and rubble mapping for city port-au-prince post-2010 earthquake with GeoEye-1 imagery and lidar data","volume":"77","author":"Ural","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_33","unstructured":"Haiyang, Y., Gang, C., and Xiaosan, G. (2010, January 4\u20136). Earthquake-collapsed building extraction from LiDAR and aerophotograph based on OBIA. Proceedings of the 2nd International Conference on Information Science and Engineering, Hangzhou, China."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.knosys.2010.07.003","article-title":"An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine","volume":"24","author":"Li","year":"2011","journal-title":"Knowl. Based Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mitomi, H., Matsuoka, M., and Yamazaki, F. (2002, January 21\u201325). Application of automated damage detection of buildings due to earthquakes by panchromatic television images. Proceedings of the 7th US National Conference on Earthquake Engineering, Boston, MA, USA.","DOI":"10.2208\/jscej.2002.703_267"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cooner, A.J., Shao, Y., and Campbell, J.B. (2016). Detection of urban damage using remote sensing and machine learning algorithms: Revisiting the 2010 Haiti earthquake. Remote Sens., 8.","DOI":"10.3390\/rs8100868"},{"key":"ref_37","unstructured":"Rasika, A., Kerle, N., and Heuel, S. (2006, January 8\u201311). Multi-scale texture and color segmentation of oblique airborne video data for damage classification. Proceedings of the ISPRS 2006: ISPRS Midterm Symposium 2006 Remote Sensing: From Pixels to Processes, Enschede, The Netherlands."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/LGRS.2011.2170657","article-title":"Postearthquake building damage assessment using multi-mutual information from pre-event optical image and postevent SAR image","volume":"9","author":"Wang","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"101505","DOI":"10.1016\/j.ijdrr.2020.101505","article-title":"Segment-by-segment comparison technique for earthquake-induced building damage map generation using satellite imagery","volume":"46","author":"Khodaverdizahraee","year":"2020","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_40","unstructured":"Tomowski, D., Klonus, S., Ehlers, M., Michel, U., and Reinartz, P. (2010, January 5\u20137). Change visualization through a texture-based analysis approach for disaster applications. Proceedings of the ISPRS Proceedings, Vienna, Austria."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4935","DOI":"10.1109\/JSTARS.2015.2493342","article-title":"A soft computing method for damage mapping using VHR optical satellite imagery","volume":"8","author":"Mansouri","year":"2015","journal-title":"Ieee J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.aei.2005.07.003","article-title":"A hybrid intelligent genetic algorithm","volume":"19","author":"Javadi","year":"2005","journal-title":"Adv. Eng. Inform."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"141582","DOI":"10.1016\/j.scitotenv.2020.141582","article-title":"Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia","volume":"749","author":"Jena","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111743","DOI":"10.1016\/j.rse.2020.111743","article-title":"Detecting urban changes using phase correlation and \u21131-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami","volume":"242","author":"Moya","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Duangsoithong, R., and Windeatt, T. (2009, January 4\u20136). Relevant and redundant feature analysis with ensemble classification. Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition, Kolkata, India.","DOI":"10.1109\/ICAPR.2009.36"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2905","DOI":"10.1007\/s10462-019-09750-3","article-title":"Feature selection in image analysis: A survey","volume":"53","author":"Remeseiro","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_47","unstructured":"Noriega, L. (2005). Multilayer perceptron tutorial. Sch. Comput. Staffs. Univ., Available online: https:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.608.2530&rep=rep1&type=pdf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TNNLS.2015.2424995","article-title":"Extreme learning machine for multilayer perceptron","volume":"27","author":"Tang","year":"2015","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.enggeo.2004.03.010","article-title":"Use of backpropagation neural network for landslide monitoring: A case study in the higher Himalaya","volume":"74","author":"Neaupane","year":"2004","journal-title":"Eng. Geol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Haralick, R.M., Shanmugam, K., and Dinstein, I.H. (1973). Textural features for image classification. IEEE Trans. Syst. ManCybern., 610\u2013621.","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"ref_51","unstructured":"Kato, L.V. (2019). Integrating Openstreetmap Data in Object Based Landcover and Landuse Classification for Disaster Recovery. [Master\u2019s Thesis, University of Twente]."},{"key":"ref_52","unstructured":"Guide, U. (2009). Definiens AG. Ger. Defin. Dev. XD, 2, Available online: https:\/\/www.imperial.ac.uk\/media\/imperial-college\/medicine\/facilities\/film\/Definiens-Developer-User-Guide-XD-2.0.4.pdf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1007\/s11069-017-3085-y","article-title":"Using high-resolution satellite imagery to provide a relief priority map after earthquake","volume":"90","author":"Ranjbar","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_54","first-page":"307","article-title":"A value for n-person games","volume":"2","author":"Shapley","year":"1953","journal-title":"Contrib. Theory Games"},{"key":"ref_55","unstructured":"Lundberg, S., and Lee, S.-I. (2017). A unified approach to interpreting model predictions. arXiv."},{"key":"ref_56","unstructured":"Molnar, C. (2020). Interpretable Machine Learning, Lulu Press."},{"key":"ref_57","unstructured":"(2020, November 20). Situation Update No.15-Final 7.4 Earthquake and Tsunami. Available online: https:\/\/ahacentre.org\/situation-update\/situation-update-no-15-sulawesi-earthquake-26-october-2018\/."},{"key":"ref_58","unstructured":"(2019, July 16). Copernicus Emergency Management Service (\u00a9 2015 European Union), [EMSR 317] Palu: Grading Map. Available online: https:\/\/emergency.copernicus.eu\/mapping\/list-of-components\/EMSR317."},{"key":"ref_59","unstructured":"(2021, January 28). Charter Space and Majors Disasters. Available online: https:\/\/disasterscharter.org\/web\/guest\/activations\/-\/article\/earthquake-in-indonesia-activation-587."},{"key":"ref_60","unstructured":"(2019, July 16). Digital Globe: Satellite Imagery for Natural Disasters. Available online: https:\/\/www.digitalglobe.com\/ecosystem\/open-data."},{"key":"ref_61","unstructured":"(2020, November 20). Missing Maps. Available online: http:\/\/www.missingmaps.org\/."},{"key":"ref_62","unstructured":"(2020, November 20). MapSwipe. Available online: http:\/\/mapswipe.org\/."},{"key":"ref_63","unstructured":"(2019, July 16). OpenStreetMap Contributors. Available online: https:\/\/www.openstreetmap.org."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Scholz, S., Knight, P., Eckle, M., Marx, S., and Zipf, A. (2018). Volunteered geographic information for disaster risk reduction\u2014The missing maps approach and its potential within the red cross and red crescent movement. Remote Sens., 10.","DOI":"10.3390\/rs10081239"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TGRS.2009.2037144","article-title":"Delineation of urban footprints from TerraSAR-X data by analyzing speckle characteristics and intensity information","volume":"48","author":"Esch","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1109\/TGRS.2010.2091644","article-title":"Characterization of land cover types in TerraSAR-X images by combined analysis of speckle statistics and intensity information","volume":"49","author":"Esch","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","unstructured":"(2021, March 23). Copernicus Emergency Management Service. Available online: https:\/\/emergency.copernicus.eu\/."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-14-119","article-title":"An AUC-based permutation variable importance measure for random forests","volume":"14","author":"Janitza","year":"2013","journal-title":"BMC Bioinform."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ma, L., Fu, T., Blaschke, T., Li, M., Tiede, D., Zhou, Z., Ma, X., and Chen, D. (2017). Evaluation of feature selection methods for object-based land cover mapping of unmanned aerial vehicle imagery using random forest and support vector machine classifiers. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6020051"},{"key":"ref_71","first-page":"101895","article-title":"Optimal segmentation of high spatial resolution images for the classification of buildings using random forests","volume":"82","author":"Bialas","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4489\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:24:04Z","timestamp":1760163844000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4489"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,30]]},"references-count":72,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134489"],"URL":"https:\/\/doi.org\/10.3390\/s21134489","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,30]]}}}