{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T04:36:26Z","timestamp":1759638986009,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030617042"},{"type":"electronic","value":"9783030617059"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61705-9_58","type":"book-chapter","created":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T16:03:54Z","timestamp":1604505834000},"page":"698-705","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning for House Categorisation, a Proposal Towards Automation in Land Registry"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2351-068X","authenticated-orcid":false,"given":"David","family":"Garcia-Retuerta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0198-696X","authenticated-orcid":false,"given":"Roberto","family":"Casado-Vara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2333-8405","authenticated-orcid":false,"given":"Jose L.","family":"Calvo-Rolle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0268-7999","authenticated-orcid":false,"given":"H\u00e9ctor","family":"Quinti\u00e1n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8175-2201","authenticated-orcid":false,"given":"Javier","family":"Prieto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,4]]},"reference":[{"issue":"5","key":"58_CR1","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/0305-750X(90)90016-Q","volume":"18","author":"DA Atwood","year":"1990","unstructured":"Atwood, D.A.: Land registration in africa: the impact on agricultural production. World Dev. 18(5), 659\u2013671 (1990)","journal-title":"World Dev."},{"key":"58_CR2","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.inffus.2018.12.007","volume":"49","author":"R Casado-Vara","year":"2019","unstructured":"Casado-Vara, R., Chamoso, P., De la Prieta, F., Prieto, J., Corchado, J.M.: Non-linear adaptive closed-loop control system for improved efficiency in iot-blockchain management. Inf. Fusion 49, 227\u2013239 (2019)","journal-title":"Inf. Fusion"},{"key":"58_CR3","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1016\/j.future.2019.09.042","volume":"102","author":"R Casado-Vara","year":"2020","unstructured":"Casado-Vara, R., Martin-del Rey, A., Affes, S., Prieto, J., Corchado, J.M.: IoT network slicing on virtual layers of homogeneous data for improved algorithm operation in smart buildings. Future Gener. Comput. Syst. 102, 965\u2013977 (2020)","journal-title":"Future Gener. Comput. Syst."},{"issue":"3","key":"58_CR4","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"issue":"6","key":"58_CR5","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng, L.: The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Process. Mag. 29(6), 141\u2013142 (2012)","journal-title":"IEEE Signal Process. Mag."},{"key":"58_CR6","doi-asserted-by":"publisher","first-page":"1712","DOI":"10.1016\/j.procs.2016.05.512","volume":"80","author":"M Elleuch","year":"2016","unstructured":"Elleuch, M., Maalej, R., Kherallah, M.: A new design based-svm of the cnn classifier architecture with dropout for offline arabic handwritten recognition. Procedia Comput. Sci. 80, 1712\u20131723 (2016)","journal-title":"Procedia Comput. Sci."},{"key":"58_CR7","doi-asserted-by":"publisher","unstructured":"Garc\u00eda-Retuerta, D., Bartolom\u00e9, \u00c1., Chamoso, P., Corchado, J.M., Gonz\u00e1lez-Briones, A.: Original Content Verification Using Hash-Based Video Analysis. In: Novais, P., Lloret, J., Chamoso, P., Carneiro, D., Navarro, E., Omatu, S. (eds.) ISAmI 2019. AISC, vol. 1006, pp. 120\u2013127. Springer, Cham (2020). \nhttps:\/\/doi.org\/10.1007\/978-3-030-24097-4_15","DOI":"10.1007\/978-3-030-24097-4_15"},{"issue":"5","key":"58_CR8","doi-asserted-by":"publisher","first-page":"110","DOI":"10.3390\/a12050110","volume":"12","author":"D Garc\u00eda-Retuerta","year":"2019","unstructured":"Garc\u00eda-Retuerta, D., Bartolom\u00e9, \u00c1., Chamoso, P., Corchado, J.M.: Counter-terrorism video analysis using hash-based algorithms. Algorithms 12(5), 110 (2019)","journal-title":"Algorithms"},{"key":"58_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-46484-8_45","volume-title":"Computer Vision \u2013 ECCV 2016","author":"R Garg","year":"2016","unstructured":"Garg, R., B.G., V.K., Carneiro, G., Reid, I.: Unsupervised CNN for single view depth estimation: geometry to the rescue. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 740\u2013756. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-46484-8_45"},{"key":"58_CR10","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Object detection via a multi-region and semantic segmentation-aware CNN model. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1134\u20131142 (2015)","DOI":"10.1109\/ICCV.2015.135"},{"key":"58_CR11","doi-asserted-by":"crossref","unstructured":"Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D.: Making the v in VQA matter: Elevating the role of image understanding in visual question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6904\u20136913 (2017)","DOI":"10.1109\/CVPR.2017.670"},{"key":"58_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"58_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"58_CR14","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"2","key":"58_CR15","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1080\/02693799008941538","volume":"4","author":"GJ Hunter","year":"1990","unstructured":"Hunter, G.J., Williamson, I.P.: The development of a historical digital cadastral database. Int. J. Geogr. Inf. Syst. 4(2), 169\u2013179 (1990)","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"58_CR16","first-page":"3","volume":"160","author":"SB Kotsiantis","year":"2007","unstructured":"Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 160, 3\u201324 (2007)","journal-title":"Emerg. Artif. Intell. Appl. Comput. Eng."},{"issue":"10","key":"58_CR17","doi-asserted-by":"publisher","first-page":"2064","DOI":"10.1109\/LCOMM.2018.2863387","volume":"22","author":"T Li","year":"2018","unstructured":"Li, T., Prieto, J., Fan, H., Corchado, J.M.: A robust multi-sensor phd filter based on multi-sensor measurement clustering. IEEE Commun. Lett. 22(10), 2064\u20132067 (2018)","journal-title":"IEEE Commun. Lett."},{"key":"58_CR18","first-page":"434","volume":"35","author":"L Matikainen","year":"2004","unstructured":"Matikainen, L., Hyypp\u00e4, J., Kaartinen, H.: Automatic detection of changes from laser scanner and aerial image data for updating building maps. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 35, 434\u2013439 (2004)","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"issue":"B2\/W24","key":"58_CR19","first-page":"143","volume":"36","author":"S M\u00fcller","year":"2005","unstructured":"M\u00fcller, S., Zaum, D.W.: Robust building detection in aerial images. Int. Arch. Photogram. Remote Sens. 36(B2\/W24), 143\u2013148 (2005)","journal-title":"Int. Arch. Photogram. Remote Sens."},{"issue":"4","key":"58_CR20","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1016\/j.patcog.2011.09.021","volume":"45","author":"XX Niu","year":"2012","unstructured":"Niu, X.X., Suen, C.Y.: A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recogn. 45(4), 1318\u20131325 (2012)","journal-title":"Pattern Recogn."},{"key":"58_CR21","unstructured":"Ossk\u00f3, A.: Advantages of the unified multipurpose land registry system. In: International Conference on Enhancing Land Registration and Cadastre for Economic Growth in India, vol. 31 (2006)"},{"issue":"3","key":"58_CR22","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/S0197-3975(01)00014-5","volume":"25","author":"G Payne","year":"2001","unstructured":"Payne, G.: Urban land tenure policy options: titles or rights? Habitat Int. 25(3), 415\u2013429 (2001)","journal-title":"Habitat Int."},{"issue":"12","key":"58_CR23","doi-asserted-by":"publisher","first-page":"3250","DOI":"10.1109\/TSP.2016.2515065","volume":"64","author":"J Prieto","year":"2016","unstructured":"Prieto, J., Mazuelas, S., Win, M.Z.: Context-aided inertial navigation via belief condensation. IEEE Trans. Signal Process. 64(12), 3250\u20133261 (2016)","journal-title":"IEEE Trans. Signal Process."},{"key":"58_CR24","unstructured":"Ravanbakhsh, M., Mousavi, H., Rastegari, M., Murino, V., Davis, L.S.: Action recognition with image based CNN features. arXiv preprint \narXiv:1512.03980\n\n (2015)"},{"key":"58_CR25","doi-asserted-by":"crossref","unstructured":"Sagiroglu, S., Sinanc, D.: Big data: a review. In: 2013 International Conference on Collaboration Technologies and Systems (CTS), pp. 42\u201347. IEEE (2013)","DOI":"10.1109\/CTS.2013.6567202"},{"issue":"8","key":"58_CR26","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.compbiomed.2011.05.017","volume":"41","author":"WB Sampaio","year":"2011","unstructured":"Sampaio, W.B., Diniz, E.M., Silva, A.C., De Paiva, A.C., Gattass, M.: Detection of masses in mammogram images using CNN, geostatistic functions and SVM. Comput. Biol. Med. 41(8), 653\u2013664 (2011)","journal-title":"Comput. Biol. Med."},{"key":"58_CR27","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1016\/j.future.2019.06.029","volume":"101","author":"AJ S\u00e1nchez","year":"2019","unstructured":"S\u00e1nchez, A.J., Rodr\u00edguez, S., de la Prieta, F., Gonz\u00e1lez, A.: Adaptive interface ecosystems in smart cities control systems. Future Gener. Comput. Syst. 101, 605\u2013620 (2019)","journal-title":"Future Gener. Comput. Syst."},{"key":"58_CR28","unstructured":"Vitruvius, M.P.: The ten books on architecture, translated by morris hicky morgan (1960)"},{"issue":"6","key":"58_CR29","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1007\/s40846-016-0182-4","volume":"36","author":"DX Xue","year":"2016","unstructured":"Xue, D.X., Zhang, R., Feng, H., Wang, Y.L.: CNN-SVM for microvascular morphological type recognition with data augmentation. J. Med. Biol. Eng. 36(6), 755\u2013764 (2016)","journal-title":"J. Med. Biol. Eng."}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61705-9_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T16:23:51Z","timestamp":1604507031000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-61705-9_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030617042","9783030617059"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61705-9_58","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"4 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gij\u00f3n","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2020.haisconference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"106","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"61% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}