{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:47:50Z","timestamp":1765234070337,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031490101"},{"type":"electronic","value":"9783031490118"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-49011-8_30","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T16:03:27Z","timestamp":1702569807000},"page":"376-387","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Tree Stem Segmentation for Robotic Eucalyptus Selective Thinning Operations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9999-1550","authenticated-orcid":false,"given":"Daniel Queir\u00f3s","family":"da Silva","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1860-5474","authenticated-orcid":false,"given":"Tiago Ferreira","family":"Rodrigues","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0317-4714","authenticated-orcid":false,"given":"Armando Jorge","family":"Sousa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-6113","authenticated-orcid":false,"given":"Filipe Neves","family":"dos Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3747-6577","authenticated-orcid":false,"given":"V\u00edtor","family":"Filipe","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"30_CR1","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin, Heidelberg (2006)"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: Yolact: real-time instance segmentation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00925"},{"issue":"2","key":"30_CR3","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1109\/TPAMI.2020.3014297","volume":"44","author":"D Bolya","year":"2022","unstructured":"Bolya, D., Zhou, C., Xiao, F., Lee, Y.J.: Yolact++ better real-time instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 1108\u20131121 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2020.3014297","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"30_CR4","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1109\/TPAMI.2019.2956516","volume":"43","author":"Z Cai","year":"2021","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: high quality object detection and instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1483\u20131498 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2019.2956516","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR5","doi-asserted-by":"publisher","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1280\u20131289 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00135","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"30_CR6","doi-asserted-by":"publisher","unstructured":"da Silva, D.Q., dos Santos, F.N., Filipe, V., Sousa, A.J.: Tree trunks cross-platform detection using deep learning strategies for forestry operations. In: Tardioli, D., Matell\u00e1n, V., Heredia, G., Silva, M.F., Marques, L. (eds.) ROBOT2022: fifth Iberian Robotics Conference, pp. 40\u201352. Springer International Publishing, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-21065-5_4","DOI":"10.1007\/978-3-031-21065-5_4"},{"key":"30_CR7","doi-asserted-by":"publisher","unstructured":"da Silva, D.Q., dos Santos, F.N., Filipe, V., Sousa, A.J., Oliveira, P.M.: Edge AI-based tree trunk detection for forestry monitoring robotics. Robotics 11(6) (2022). https:\/\/doi.org\/10.3390\/robotics11060136","DOI":"10.3390\/robotics11060136"},{"key":"30_CR8","doi-asserted-by":"publisher","unstructured":"da Silva, D.Q., dos Santos, F.N., Sousa, A.J., Filipe, V., Boaventura-Cunha, J.: Unimodal and multimodal perception for forest management: review and dataset. Computation 9(12) (2021). https:\/\/doi.org\/10.3390\/computation9120127","DOI":"10.3390\/computation9120127"},{"key":"30_CR9","doi-asserted-by":"publisher","unstructured":"da Silva, D.Q., dos Santos, F.N., Sousa, A.J., Filipe, V.: Visible and thermal image-based trunk detection with deep learning for forestry mobile robotics. J. Imaging 7(9) (2021). https:\/\/doi.org\/10.3390\/jimaging7090176","DOI":"10.3390\/jimaging7090176"},{"key":"30_CR10","unstructured":"FAO: The State of the World\u2019s Forests 2022, p. 28. FAO, Rome (2022)"},{"key":"30_CR11","unstructured":"Ferreira, D., Morais, S.: Sele\u00e7\u00e3o de varas - Manual t\u00e9cnico de apoio \u00e1 gest\u00e3o de talhadias (2022)"},{"key":"30_CR12","doi-asserted-by":"publisher","unstructured":"Fortin, J.M., Gamache, O., Grondin, V., Pomerleau, F., Gigu\u00e8re, P.: Instance segmentation for autonomous log grasping in forestry operations. In: 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6064\u20136071 (2022). https:\/\/doi.org\/10.1109\/IROS47612.2022.9982286","DOI":"10.1109\/IROS47612.2022.9982286"},{"key":"30_CR13","doi-asserted-by":"publisher","unstructured":"Fritz, A., Kattenborn, T., Koch, B.: UAV-based photogrammetric point clouds\u2014Tree stem mapping in open stands in comparison to terrestrial laser scanner point clouds. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1\/W2(September), pp. 141\u2013146 (2013). https:\/\/doi.org\/10.5194\/isprsarchives-xl-1-w2-141-2013","DOI":"10.5194\/isprsarchives-xl-1-w2-141-2013"},{"key":"30_CR14","unstructured":"Gomide, L., Mello, J., Acerbi J\u00fanior, F., Scolforo, J.: Automated selective thinning via multicriteria maetaheuristic procedure. Scientia Forestalis\/Forest Sci. 42, 299\u2013306 (2014)"},{"key":"30_CR15","doi-asserted-by":"crossref","unstructured":"Grondin, V., Fortin, J.M., Pomerleau, F., Gigu\u00e8re, P.: Tree detection and diameter estimation based on deep learning. Forest.: Int. J. Forest Res. (2022)","DOI":"10.1093\/forestry\/cpac043"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"30_CR17","doi-asserted-by":"publisher","unstructured":"Jocher, G.: YOLOv5 by Ultralytics (2020). https:\/\/doi.org\/10.5281\/zenodo.3908559","DOI":"10.5281\/zenodo.3908559"},{"key":"30_CR18","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics (2023)"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"30_CR20","doi-asserted-by":"publisher","unstructured":"Mokro\u0161, M., Mikita, T., Singh, A., Toma\u0161t\u00edk, J., Chud\u00e1, J., W\u0229\u017cyk, P., Ku\u017eelka, K., Surov\u00fd, P., Klim\u00e1nek, M., Zi\u0229ba-Kulawik, K., Bobrowski, R., Liang, X.: Novel low-cost mobile mapping systems for forest inventories as terrestrial laser scanning alternatives. Int. J. Appl. Earth Observ. Geoinf. 104 (2021). https:\/\/doi.org\/10.1016\/j.jag.2021.102512","DOI":"10.1016\/j.jag.2021.102512"},{"issue":"2","key":"30_CR21","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/S0168-1699(03)00022-X","volume":"39","author":"I S\u00f6derbergh","year":"2003","unstructured":"S\u00f6derbergh, I., Ledermann, T.: Algorithms for simulating thinning and harvesting in five European individual-tree growth simulators: a review. Comput. Electron. Agric. 39(2), 115\u2013140 (2003). https:\/\/doi.org\/10.1016\/S0168-1699(03)00022-X","journal-title":"Comput. Electron. Agric."},{"issue":"4","key":"30_CR22","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/j.jterra.2005.07.001","volume":"43","author":"K Vestlund","year":"2006","unstructured":"Vestlund, K., Hellstr\u00f6m, T.: Requirements and system design for a robot performing selective cleaning in young forest stands. J. Terrramech. 43(4), 505\u2013525 (2006). https:\/\/doi.org\/10.1016\/j.jterra.2005.07.001","journal-title":"J. Terrramech."},{"key":"30_CR23","doi-asserted-by":"publisher","unstructured":"Vestlund, K., Nordfjell, T., Eliasson, L.: Comparison of human and computer-based selective cleaning. Silva Fennica Monogr. 39(4), 509\u2013523 (2005). https:\/\/doi.org\/10.14214\/sf.363","DOI":"10.14214\/sf.363"},{"key":"30_CR24","doi-asserted-by":"publisher","unstructured":"Vestlund, K., Nordfjell, T., Eliasson, L., Karlsson, A.: A decision support system for selective cleaning. Silva Fennica 40(2), 271\u2013289 (2006). https:\/\/doi.org\/10.14214\/sf.343","DOI":"10.14214\/sf.343"},{"key":"30_CR25","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022). arXiv:2207.02696","DOI":"10.1109\/CVPR52729.2023.00721"},{"issue":"4","key":"30_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/rs8040333","volume":"8","author":"Z Zhen","year":"2016","unstructured":"Zhen, Z., Quackenbush, L.J., Zhang, L.: Trends in automatic individual tree crown detection and delineation-evolution of LiDAR data. Remote Sens. 8(4), 1\u201326 (2016). https:\/\/doi.org\/10.3390\/rs8040333","journal-title":"Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49011-8_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T16:10:21Z","timestamp":1702570221000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49011-8_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031490101","9783031490118"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49011-8_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Faial Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2023.inesctec.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"163","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":"85","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":"52% - 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":"4","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}