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The work is positioned within Advances in Space Research and presents a structured, multi-author scholarly contribution with formal citation metadata, received/revised/accepted dates, and a DOI reference. It emphasizes forward-looking assessment of how machine-learning methods can evolve for operational Earth observation, grounded in the reviewed technical landscape and research directions.","Journal Pre-proofs  \nReview  \nA critical review on the state-of-the-art and future prospects of Machine Learning for Earth Observation Operations  \nPablo Miralles, Kathiravan Thangavel, Antonio Fulvio Scannapieco, Nitya Jagadam, Prerna Baranwal, Bhavin Faldu, Ruchita Abhang, Sahil Bhatia, Sebastien Bonnart, Ishita Bhatnagar, Beenish Batul, Pallavi Prasad, Héctor Ortega-González, Harrish Joseph, Harshal More, Sondes Morchedi, Aman Kumar Panda, Marco Zaccaria Di Fraia, Daniel Wischert, Daria Stepanova  \nPII: S0273-1177(23)00145-X  \nDOI: [https://doi.org/10.1016/j.asr.2023.02.025](https://doi.org/10.1016/j.asr.2023.02.025)  \nReference: JASR 16573  \nTo appear in: Advances in Space Research  \nReceived Date: 5 August 2022  \nRevised Date: 10 February 2023  \nAccepted Date: 13 February 2023  \nPlease cite this article as: Miralles, P., Thangavel, K., Fulvio Scannapieco, A., Jagadam, N., Baranwal, P., Faldu, B., Abhang, R., Bhatia, S., Bonnart, S., Bhatnagar, I., Batul, B., Prasad, P., Ortega-González, H., Joseph, H., More, H., Morchedi, S., Kumar Panda, A., Zaccaria Di Fraia, M., Wischert, D., Stepanova, D., A critical review on the state-of-the-art and future prospects of Machine Learning for Earth Observation Operations, Advances in Space Research (2023), doi: [https://doi.org/10.1016/j.asr.2023.02.025](https://doi.org/10.1016/j.asr.2023.02.025)  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \nCOSPAR  \nA critical review on the state-of-the-art and future prospects of Machine Learning for Earth Observation Operations  \nPablo Miralles*, Kathiravan Thangavel*, Antonio Fulvio Scannapieco*, Nitya Jagadam*, Prerna Baranwal*, Bhavin Faldu*, Ruchita Abhang*, Sahil Bhatia*, Sebastien Bonnart*, Ishita Bhatnagar*, Beenish Batul*, Pallavi Prasad*, Héctor Ortega-González*, Harrish Joseph*, Harshal More*, Sondes Morchedi*, Aman Kumar Panda*, Marco Zaccaria Di Fraia*, Daniel Wischert*, Daria Stepanova*.  \n(*) In respective order:  \nGTD International, GTD Group, 99 Route d’Espagne, 31100, Toulouse, France, [pmirallesr@gmail.com](pmirallesr@gmail.com), Sir Lawrence Wackett Defence & Aerospace Centre, RMIT University, Melbourne, VIC 3083, Australia. [kathiravan.thanagvel@student.rmit.edu.au](kathiravan.thanagvel@student.rmit.edu.au)  \nSpace Generation Advisory Council (SGAC), c/o European Space Policy Institute, Schwarzenbergplatz 6, 1030 Vienna, Austria, [antonio.scannapieco@spacegeneration.org](antonio.scannapieco@spacegeneration.org)  \nSpace Generation Advisory Council (SGAC), c/o European Space Policy Institute, Schwarzenbergplatz 6, 1030 Vienna, Austria, [jagadamnitya@gmail.com](jagadamnitya@gmail.com)  \nDepartment of Electrical & Electronics/Instrumentation & Department of Mathematics, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan-333031, India, [f2016568@pilani.bits-pilani.ac.in](f2016568@pilani.bits-pilani.ac.in)  \nSpace Generation Advisory Council (SGAC), c/o European Space Policy Institute, Schwarzenbergplatz 6, 1030 Vienna, Austria, [bhavinfaldu6474@gmail.com](bhavinfaldu6474@gmail.com)  \nDepartment of Computer Science Engineering, Savitribai Phule Pune University, Pune, Maharashtra-411052, India, [rabhang09@gmail.com](rabhang09@gmail.com)  \nDepartment of Aerospace Engineering, University of Petroleum and Energy Studies, Energy Acres, Bidholi via Premnagar, Dehradun, Uttarakhand-248007, India, [sahil2112.b@gmail.com](sahil2112.b@gmail.com)  \nSpace Generation Advisory Council (SGAC), c/o European Spac","cbCaik8MhIh0qO2d","https://ap.wps.com/l/cbCaik8MhIh0qO2d","pdf",2662571,1,35,"English","en",105,"# Review Overview\n## Research Context and Publication Metadata\n## Focus: Earth Observation Operations and Machine Learning\n## Outlook and Future Prospects","[{\"question\":\"What does the paper review focus on?\",\"answer\":\"The paper focuses on a critical review of machine learning for Earth observation operations, covering both the current state-of-the-art and future prospects.\"},{\"question\":\"Where is the paper intended to be published?\",\"answer\":\"The metadata indicates it is to appear in Advances in Space Research.\"},{\"question\":\"How can the paper be referenced?\",\"answer\":\"The text provides a DOI link (10.1016/j.asr.2023.02.025) and a journal reference code (JASR 16573) for citation.\"}]","A critical review on the state-of-the-art and future prospects of Machine Learning for Earth Observation Operations | 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