{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:43:45Z","timestamp":1778345025829,"version":"3.51.4"},"reference-count":70,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011011","name":"Junta de Andaluc\u00eda","doi-asserted-by":"publisher","award":["QUAL21 010UMA"],"award-info":[{"award-number":["QUAL21 010UMA"]}],"id":[{"id":"10.13039\/501100011011","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Future Generation Computer Systems"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1016\/j.future.2024.107499","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T16:20:46Z","timestamp":1724862046000},"page":"107499","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":11,"special_numbering":"C","title":["Big Data-driven MLOps workflow for annual high-resolution land cover classification models"],"prefix":"10.1016","volume":"163","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3202-5782","authenticated-orcid":false,"given":"Antonio M.","family":"Burgue\u00f1o-Romero","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Crist\u00f3bal","family":"Barba-Gonz\u00e1lez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 F.","family":"Aldana-Montes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.future.2024.107499_b1","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"issue":"15","key":"10.1016\/j.future.2024.107499_b2","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.3390\/rs12152411","article-title":"Land cover classification using google earth engine and random forest classifier\u2014The role of image composition","volume":"12","author":"Phan","year":"2020","journal-title":"Remote Sens."},{"issue":"8","key":"10.1016\/j.future.2024.107499_b3","doi-asserted-by":"crossref","first-page":"979","DOI":"10.3390\/rs11080979","article-title":"Evaluation of sentinel-1 and 2 time series for land cover classification of forest\u2013agriculture mosaics in temperate and tropical landscapes","volume":"11","author":"Mercier","year":"2019","journal-title":"Remote Sens"},{"key":"10.1016\/j.future.2024.107499_b4","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.jag.2018.10.005","article-title":"Spatial-temporal impacts of urban land use land cover on land surface temperature: Case studies of two Canadian urban areas","volume":"75","author":"Zhang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinformation"},{"key":"10.1016\/j.future.2024.107499_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2019.136449","article-title":"Effects of dynamic land use\/land cover change on water resources and sediment yield in the Anzali wetland catchment, Gilan, Iran","volume":"712","author":"Aghsaei","year":"2020","journal-title":"Sci. Total Environ."},{"issue":"8","key":"10.1016\/j.future.2024.107499_b6","doi-asserted-by":"crossref","first-page":"3668","DOI":"10.1109\/TCYB.2019.2950779","article-title":"A survey of optimization methods from a machine learning perspective","volume":"50","author":"Sun","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.future.2024.107499_b7","series-title":"Machine learning","author":"Zhou","year":"2021"},{"issue":"3","key":"10.1016\/j.future.2024.107499_b8","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine learning: Algorithms, real-world applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"10.1016\/j.future.2024.107499_b9","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.neucom.2017.01.026","article-title":"Machine learning on big data: Opportunities and challenges","volume":"237","author":"Zhou","year":"2017","journal-title":"Neurocomputing"},{"issue":"12","key":"10.1016\/j.future.2024.107499_b10","doi-asserted-by":"crossref","first-page":"8927","DOI":"10.1109\/TPAMI.2021.3126648","article-title":"Fine-grained image analysis with deep learning: A survey","volume":"44","author":"Wei","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.future.2024.107499_b11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","article-title":"Deep learning applications and challenges in big data analytics","volume":"2","author":"Najafabadi","year":"2015","journal-title":"J. Big Data"},{"key":"10.1016\/j.future.2024.107499_b12","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: Theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.future.2024.107499_b13","first-page":"39","article-title":"Accelerating the machine learning lifecycle with mlflow.","volume":"41","author":"Zaharia","year":"2018","journal-title":"IEEE Data Eng. Bull."},{"issue":"3","key":"10.1016\/j.future.2024.107499_b14","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/MS.2016.68","article-title":"DevOps","volume":"33","author":"Ebert","year":"2016","journal-title":"Ieee Softw."},{"key":"10.1016\/j.future.2024.107499_b15","series-title":"DevOps: A software architect\u2019s perspective","author":"Bass","year":"2015"},{"key":"10.1016\/j.future.2024.107499_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106222","article-title":"Mlops in freight rail operations","volume":"123","author":"Pineda-Jaramillo","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"6","key":"10.1016\/j.future.2024.107499_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3359981","article-title":"A survey of DevOps concepts and challenges","volume":"52","author":"Leite","year":"2019","journal-title":"ACM Comput. Surv."},{"issue":"19","key":"10.1016\/j.future.2024.107499_b18","doi-asserted-by":"crossref","first-page":"9851","DOI":"10.3390\/app12199851","article-title":"From DevOps to MLOps: Overview and application to electricity market forecasting","volume":"12","author":"Subramanya","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.future.2024.107499_b19","first-page":"359","article-title":"AIOps: predictive analytics & machine learning in operations","author":"Masood","year":"2019","journal-title":"Cogn. Comput. Recipes Artif. Intell. Solut. Microsoft Cogn. Serv. TensorFlow"},{"key":"10.1016\/j.future.2024.107499_b20","article-title":"Automatic land cover classification of multi-resolution dualpol data using convolutional neural network (CNN)","volume":"22","author":"Memon","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"10.1016\/j.future.2024.107499_b21","series-title":"Geospatial Informatics XIII","article-title":"Automated global-scale detection and characterization of anthropogenic activity using multi-source satellite-based remote sensing imagery","volume":"12525","author":"Goldberg","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b22","series-title":"Kubernetes in Action","author":"Luksa","year":"2017"},{"issue":"1","key":"10.1016\/j.future.2024.107499_b23","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1109\/TNSM.2021.3052837","article-title":"Machine learning-based scaling management for kubernetes edge clusters","volume":"18","author":"Toka","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"10.1016\/j.future.2024.107499_b24","series-title":"2023 Fourteenth International Conference on Ubiquitous and Future Networks","first-page":"652","article-title":"An efficient microservices architecture for mlops","author":"Roh","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b25","series-title":"Practitioners guide to mlops: A framework for continuous delivery and automation of machine learning","author":"Salama","year":"2021"},{"issue":"1","key":"10.1016\/j.future.2024.107499_b26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-023-00770-z","article-title":"Scalable approach for high-resolution land cover: a case study in the Mediterranean basin","volume":"10","author":"Burgue\u00f1o","year":"2023","journal-title":"J. Big Data"},{"key":"10.1016\/j.future.2024.107499_b27","series-title":"2019 IEEE\/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","first-page":"4","article-title":"Aiops: real-world challenges and research innovations","author":"Dang","year":"2019"},{"key":"10.1016\/j.future.2024.107499_b28","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.jss.2015.06.063","article-title":"Continuous software engineering: A roadmap and agenda","volume":"123","author":"Fitzgerald","year":"2017","journal-title":"J. Syst. Softw."},{"issue":"6","key":"10.1016\/j.future.2024.107499_b29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3483424","article-title":"A survey of AIOps methods for failure management","volume":"12","author":"Notaro","year":"2021","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"6","key":"10.1016\/j.future.2024.107499_b30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MNET.001.2100227","article-title":"Quality monitoring and assessment of deployed deep learning models for network AIOps","volume":"35","author":"Yang","year":"2021","journal-title":"IEEE Netw."},{"key":"10.1016\/j.future.2024.107499_b31","doi-asserted-by":"crossref","unstructured":"H.B.r. Christensen, Teaching DevOps and cloud computing using a cognitive apprenticeship and story-telling approach, in: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education, 2016, pp. 174\u2013179.","DOI":"10.1145\/2899415.2899426"},{"key":"10.1016\/j.future.2024.107499_b32","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2023.3262138","article-title":"Machine learning operations (mlops): Overview, definition, and architecture","author":"Kreuzberger","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.future.2024.107499_b33","article-title":"DevOps and software quality: A systematic mapping","volume":"38","author":"Mishra","year":"2020","journal-title":"Comp. Sci. Rev."},{"key":"10.1016\/j.future.2024.107499_b34","series-title":"2022 IEEE 12th Annual Computing and Communication Workshop and Conference","first-page":"0453","article-title":"Mlops-definitions, tools and challenges","author":"Symeonidis","year":"2022"},{"key":"10.1016\/j.future.2024.107499_b35","series-title":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications","first-page":"1","article-title":"Towards mlops: A framework and maturity model","author":"John","year":"2021"},{"key":"10.1016\/j.future.2024.107499_b36","doi-asserted-by":"crossref","unstructured":"B.M. Matsui, D.H. Goya, MLOps: five steps to guide its effective implementation, in: Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, 2022, pp. 33\u201334.","DOI":"10.1145\/3522664.3528611"},{"key":"10.1016\/j.future.2024.107499_b37","series-title":"2021 IEEE\/ACM 1st Workshop on AI Engineering-Software Engineering for AI","first-page":"109","article-title":"Who needs MLOps: What data scientists seek to accomplish and how can MLOps help?","author":"M\u00e4kinen","year":"2021"},{"key":"10.1016\/j.future.2024.107499_b38","series-title":"A data quality-driven view of mlops","author":"Renggli","year":"2021"},{"issue":"19","key":"10.1016\/j.future.2024.107499_b39","doi-asserted-by":"crossref","first-page":"8861","DOI":"10.3390\/app11198861","article-title":"Demystifying mlops and presenting a recipe for the selection of open-source tools","volume":"11","author":"Ruf","year":"2021","journal-title":"Appl. Sci."},{"key":"10.1016\/j.future.2024.107499_b40","series-title":"Monitoring and explainability of models in production","author":"Klaise","year":"2020"},{"key":"10.1016\/j.future.2024.107499_b41","series-title":"2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","first-page":"17","article-title":"Sustainable mlops: Trends and challenges","author":"Tamburri","year":"2020"},{"issue":"1","key":"10.1016\/j.future.2024.107499_b42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.14513\/actatechjaur.00581","article-title":"Mlops approach in the cloud-native data pipeline design","volume":"15","author":"P\u00f6l\u00f6skei","year":"2022","journal-title":"Acta Technica Jaurinensis"},{"issue":"1","key":"10.1016\/j.future.2024.107499_b43","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1149\/10701.1191ecst","article-title":"Application of mlops in prediction of lifestyle diseases","volume":"107","author":"Reddy","year":"2022","journal-title":"ECS Trans."},{"key":"10.1016\/j.future.2024.107499_b44","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/978-94-007-7969-3_5","article-title":"CORINE land cover and land cover change products","author":"B\u00fcttner","year":"2014","journal-title":"Land Use Land Cover. Mapp. Eur. Pract. Trends"},{"issue":"2","key":"10.1016\/j.future.2024.107499_b45","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/TGRS.2015.2463689","article-title":"Improving the consistency of multitemporal land cover maps using a hidden Markov model","volume":"54","author":"Abercrombie","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.future.2024.107499_b46","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.apgeog.2019.03.010","article-title":"Insights on the historical and emerging global land cover changes: The case of ESA-CCI-LC datasets","volume":"106","author":"Mousivand","year":"2019","journal-title":"Appl. Geogr."},{"key":"10.1016\/j.future.2024.107499_b47","series-title":"2021 IEEE International Geoscience and Remote Sensing Symposium","first-page":"4704","article-title":"Global land use \/ land cover with sentinel 2 and deep learning","author":"Karra","year":"2021"},{"issue":"16","key":"10.1016\/j.future.2024.107499_b48","doi-asserted-by":"crossref","DOI":"10.3390\/rs14164101","article-title":"Global 10 m land use land cover datasets: A comparison of dynamic world, world cover and esri land cover","volume":"14","author":"Venter","year":"2022","journal-title":"Remote Sens.","ISSN":"https:\/\/id.crossref.org\/issn\/2072-4292","issn-type":"print"},{"issue":"3","key":"10.1016\/j.future.2024.107499_b49","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1002\/rse2.248","article-title":"Regional matters: On the usefulness of regional land-cover datasets in times of global change","volume":"8","author":"Tulbure","year":"2022","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"10.1016\/j.future.2024.107499_b50","series-title":"IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium","first-page":"711","article-title":"End-to-end process orchestration of earth observation data workflows with apache airflow on high performance computing","author":"Tian","year":"2023"},{"issue":"10","key":"10.1016\/j.future.2024.107499_b51","doi-asserted-by":"crossref","first-page":"278","DOI":"10.3390\/fi14100278","article-title":"Refuse: Generating imperviousness maps from multi-spectral sentinel-2 satellite imagery","volume":"14","author":"Giacco","year":"2022","journal-title":"Future Internet"},{"key":"10.1016\/j.future.2024.107499_b52","series-title":"Artificial Intelligence in Earth Science","first-page":"1","article-title":"Introduction of artificial intelligence in earth sciences","author":"Sun","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b53","article-title":"Advances in region-based multisource machine learning for remote sensing","author":"Luotamo","year":"2023","journal-title":"Series Publ."},{"key":"10.1016\/j.future.2024.107499_b54","series-title":"MLflow: An open source machine learning platform","author":"MLflow","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b55","series-title":"Neptune.ai: The machine learning platform","author":"Neptune.ai","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b56","series-title":"2020 International Conference on Artificial Intelligence and Computer Engineering","first-page":"494","article-title":"Towards MLOps: A case study of ML pipeline platform","author":"Zhou","year":"2020"},{"key":"10.1016\/j.future.2024.107499_b57","series-title":"Prefect: The dataflow automation platform","author":"Prefect","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b58","series-title":"Python module that helps you build complex pipelines of batch jobs","author":"Luigi Documentation","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b59","series-title":"Apache airflow: A platform to programmatically author, schedule, and monitor workflows","author":"Apache Airflow","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b60","series-title":"Seldon: Deploy, monitor, and scale machine learning models","author":"Seldon Technologies","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b61","series-title":"BentoML: A framework for machine learning model serving","author":"BentoML","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b62","series-title":"TensorFlow: An open source machine learning framework for everyone","author":"TensorFlow","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b63","series-title":"Prometheus: An open-source monitoring and alerting toolkit","author":"Prometheus","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b64","series-title":"Zabbix: The enterprise-class monitoring solution for everyone","author":"Zabbix","year":"2023"},{"key":"10.1016\/j.future.2024.107499_b65","series-title":"PostgreSQL: introduction and concepts","author":"Momjian","year":"2001"},{"issue":"3","key":"10.1016\/j.future.2024.107499_b66","doi-asserted-by":"crossref","first-page":"166","DOI":"10.3390\/rs8030166","article-title":"First experience with sentinel-2 data for crop and tree species classifications in central europe","volume":"8","author":"Immitzer","year":"2016","journal-title":"Remote Sens"},{"issue":"7","key":"10.1016\/j.future.2024.107499_b67","doi-asserted-by":"crossref","DOI":"10.3390\/rs12071156","article-title":"ASTER global digital elevation model (GDEM) and ASTER global water body dataset (ASTWBD)","volume":"12","author":"Abrams","year":"2020","journal-title":"Remote Sens.","ISSN":"https:\/\/id.crossref.org\/issn\/2072-4292","issn-type":"print"},{"key":"10.1016\/j.future.2024.107499_b68","series-title":"Grafana: The open observability platform","author":"Grafana","year":"2024"},{"key":"10.1016\/j.future.2024.107499_b69","series-title":"Streamlit: A faster way to build and share data apps","author":"Streamlit","year":"2024"},{"key":"10.1016\/j.future.2024.107499_b70","series-title":"Copernicus sentinel-2 data documentation","author":"Copernicus DataSpace","year":"2023"}],"container-title":["Future Generation Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X24004631?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X24004631?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T08:12:12Z","timestamp":1739520732000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167739X24004631"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":70,"alternative-id":["S0167739X24004631"],"URL":"https:\/\/doi.org\/10.1016\/j.future.2024.107499","relation":{},"ISSN":["0167-739X"],"issn-type":[{"value":"0167-739X","type":"print"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Big Data-driven MLOps workflow for annual high-resolution land cover classification models","name":"articletitle","label":"Article Title"},{"value":"Future Generation Computer Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.future.2024.107499","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"107499"}}