{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:04:49Z","timestamp":1757617489281,"version":"3.44.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031834318"},{"type":"electronic","value":"9783031834325"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-83432-5_9","type":"book-chapter","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T04:16:09Z","timestamp":1741061769000},"page":"130-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-Time Detection of\u00a0the\u00a0Colta\u2019s Lagoon Surface Contamination Using a\u00a0Unmanned Aerial Vehicle (UAV)-Based Machine Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3253-3798","authenticated-orcid":false,"given":"Paulina Sof\u00eda","family":"Valle-O\u00f1ate","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4113-0579","authenticated-orcid":false,"given":"Jose Luis","family":"J\u00ednez-Tapia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4020-5110","authenticated-orcid":false,"given":"Luis Gonzalo","family":"Santill\u00e1n-Valdiviezo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0362-3628","authenticated-orcid":false,"given":"Carlos Ramiro","family":"Pe\u00f1afiel-Ojeda","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0119-5722","authenticated-orcid":false,"given":"Giovanny","family":"Cuzco","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"issue":"2","key":"9_CR1","doi-asserted-by":"publisher","first-page":"53","DOI":"10.31185\/wjcms.148","volume":"2","author":"H Ahmad","year":"2023","unstructured":"Ahmad, H., Farhan, M., Farooq, U.: Computer vision techniques for military surveillance drones. Wasit J. Comput. Math. Sci. 2(2), 53\u201359 (2023)","journal-title":"Wasit J. Comput. Math. Sci."},{"key":"9_CR2","doi-asserted-by":"publisher","unstructured":"Al-Battbootti, M.J.H., et al.: Designing and developing an advanced drone-based pollution surveillance system for river waterways, streams, and canals using machine learning algorithms: case study in Shatt al-Arab, south east Iraq. Appl. Sci. 14(6), 2382 (2024). https:\/\/doi.org\/10.3390\/app14062382, https:\/\/doi.org\/10.3390\/app14062382","DOI":"10.3390\/app14062382"},{"key":"9_CR3","unstructured":"Bichler, O.: Convolutional neural network (2017), patent No. US12345678B2, United States Patent and Trademark Office"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Charan, G., Hredzak, A., Alkhateeb, A.: Millimeter wave drones with cameras: computer vision aided wireless beam prediction. In: 2023 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1896\u20131901. IEEE (2023)","DOI":"10.1109\/ICCWorkshops57953.2023.10283784"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Chiang, C.T., Wu, Y.H., Chao, C.H.: A real-time artificial intelligence recognition system on contaminated eggs for EGG selection (2022). https:\/\/doi.org\/10.1109\/ICMA54519.2022.9856045","DOI":"10.1109\/ICMA54519.2022.9856045"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Cremona, A., Landmann, J., Sold, L., Borner, J., Farinotti, D.: Testing drones and computer vision for acquiring glacier melt observations. In: EGU General Assembly Conference Abstracts, pp. EGU22\u2013243 (2022)","DOI":"10.5194\/egusphere-egu22-243"},{"issue":"5","key":"9_CR7","doi-asserted-by":"publisher","first-page":"2049","DOI":"10.3390\/s22052049","volume":"22","author":"A Douklias","year":"2022","unstructured":"Douklias, A., Karagiannidis, L., Misichroni, F., Amditis, A.: Design and implementation of a UAV-based airborne computing platform for computer vision and machine learning applications. Sensors 22(5), 2049 (2022)","journal-title":"Sensors"},{"key":"9_CR8","unstructured":"ElectronicWings: Xbee module (2018). https:\/\/www.electronicwings.com\/sensors-modules\/xbee-module. Accessed 05 Feb 2024"},{"key":"9_CR9","doi-asserted-by":"publisher","unstructured":"Fournier, Q., Caron, G.M., Aloise, D.: A practical survey on faster and lighter transformers. ACM Comput. Surv. 55(14s) (2023). https:\/\/doi.org\/10.1145\/3586074, https:\/\/doi.org\/10.1145\/3586074","DOI":"10.1145\/3586074"},{"key":"9_CR10","doi-asserted-by":"publisher","unstructured":"Gharbia, R.: Deep learning for automatic extraction of water bodies using satellite imagery. J. Indian Soc. Remote Sens. 51, 1511\u20131521 (2023). https:\/\/doi.org\/10.1007\/s12524-023-01705-0","DOI":"10.1007\/s12524-023-01705-0"},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Ma, J., Li, H., Xi, J., Zha, G., Yue, Y., Yang, H.: UAV target tracking and detection based on faster r-cnn improved networks. In: 2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT), pp. 751\u2013755 (2024). https:\/\/doi.org\/10.1109\/ICCECT60629.2024.10545680","DOI":"10.1109\/ICCECT60629.2024.10545680"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Manaswi, N.K., Manaswi, N.K.: Convolutional neural networks. Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras, pp. 91\u201396 (2018)","DOI":"10.1007\/978-1-4842-3516-4_6"},{"key":"9_CR13","unstructured":"Palo Alto Research Center Incorporated: Title of the patent. Patent number: 20220067908 (2022), patent Grant number: 11580634. Applicant: Palo Alto Research Center Incorporated (Palo Alto, CA). Inventor: Robert R. Price (Palo Alto, CA). Application Number: 17\/525,295"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Peng, B., Alcaide, E., Anthony, Q., et\u00a0al.: RWKV: reinventing RNNs for the transformer era. In: Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 14048\u201314077. Association for Computational Linguistics (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.936"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H.L., Vieira, S., Garcia-Dias, R., Mechelli, A.: Convolutional neural networks. In: Machine learning, pp. 173\u2013191. Elsevier (2020)","DOI":"10.1016\/B978-0-12-815739-8.00010-9"},{"key":"9_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2022.3232506","volume":"7","author":"SS Roy","year":"2022","unstructured":"Roy, S.S., Paramane, A., Singh, J., Chatterjee, S., Das, A.K.: Accurate sensing of insulator surface contamination using customized convolutional neural network. IEEE Sens. Lett. 7, 1\u20134 (2022). https:\/\/doi.org\/10.1109\/LSENS.2022.3232506","journal-title":"IEEE Sens. Lett."},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Smaoui, A., Yaddaden, Y., Cherif, R., Lamouchi, D.: Automated scanning of concrete structures for crack detection and assessment using a drone. In: 2022 IEEE 21st International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp. 56\u201361. IEEE (2022)","DOI":"10.1109\/STA56120.2022.10019224"},{"key":"9_CR18","unstructured":"Sridharan, S., Stevens, J.R., Roy, K., Raghunathan, A.: X-former: in-memory acceleration of transformers. In: Proceedings of the IEEE, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA (2023)"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Tappe, M., et al.: UAS-based autonomous visual inspection of airplane surface defects. In: NDE 4.0, Predictive Maintenance, and Communication and Energy Systems in a Globally Networked World, vol. 12049, pp. 8\u201321. SPIE (2022)","DOI":"10.1117\/12.2612579"},{"key":"9_CR20","doi-asserted-by":"publisher","unstructured":"Tian, Y., Deng, N., Xu, J., Wen, Z.: A fine-grained dataset for sewage outfalls objective detection in natural environments. Scientific Data 11, 724 (2024). https:\/\/doi.org\/10.1038\/s41597-024-03574-9, https:\/\/doi.org\/10.1038\/s41597-024-03574-9","DOI":"10.1038\/s41597-024-03574-9"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Xiao, C., Sun, J.: Convolutional neural networks (CNN). In: Introduction to Deep Learning for Healthcare, pp. 83\u2013109. Springer (2021)","DOI":"10.1007\/978-3-030-82184-5_6"}],"container-title":["Communications in Computer and Information Science","Advanced Research in Technologies, Information, Innovation and Sustainability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-83432-5_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T06:54:39Z","timestamp":1757141679000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-83432-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031834318","9783031834325"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-83432-5_9","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"5 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ARTIIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Santiago de Chile","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chile","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"artiis2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.artiis.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}