{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T15:38:16Z","timestamp":1776353896696,"version":"3.51.2"},"reference-count":221,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA ACCESS program","award":["#80NSSC21M0028"],"award-info":[{"award-number":["#80NSSC21M0028"]}]},{"name":"NASA ACCESS program","award":["EAR-1947875"],"award-info":[{"award-number":["EAR-1947875"]}]},{"name":"NASA ACCESS program","award":["EAR-1947893"],"award-info":[{"award-number":["EAR-1947893"]}]},{"name":"NASA ACCESS program","award":["OAC-2117834"],"award-info":[{"award-number":["OAC-2117834"]}]},{"name":"National Science Foundation award","award":["#80NSSC21M0028"],"award-info":[{"award-number":["#80NSSC21M0028"]}]},{"name":"National Science Foundation award","award":["EAR-1947875"],"award-info":[{"award-number":["EAR-1947875"]}]},{"name":"National Science Foundation award","award":["EAR-1947893"],"award-info":[{"award-number":["EAR-1947893"]}]},{"name":"National Science Foundation award","award":["OAC-2117834"],"award-info":[{"award-number":["OAC-2117834"]}]},{"name":"University of Washington eScience Institute","award":["#80NSSC21M0028"],"award-info":[{"award-number":["#80NSSC21M0028"]}]},{"name":"University of Washington eScience Institute","award":["EAR-1947875"],"award-info":[{"award-number":["EAR-1947875"]}]},{"name":"University of Washington eScience Institute","award":["EAR-1947893"],"award-info":[{"award-number":["EAR-1947893"]}]},{"name":"University of Washington eScience Institute","award":["OAC-2117834"],"award-info":[{"award-number":["OAC-2117834"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for revolutionizing data analysis and applications in many domains of Earth sciences. This review paper synthesizes the existing literature on AI applications in remote sensing, consolidating and analyzing AI methodologies, outcomes, and limitations. The primary objectives are to identify research gaps, assess the effectiveness of AI approaches in practice, and highlight emerging trends and challenges. We explore diverse applications of AI in remote sensing, including image classification, land cover mapping, object detection, change detection, hyperspectral and radar data analysis, and data fusion. We present an overview of the remote sensing technologies, methods employed, and relevant use cases. We further explore challenges associated with practical AI in remote sensing, such as data quality and availability, model uncertainty and interpretability, and integration with domain expertise as well as potential solutions, advancements, and future directions. We provide a comprehensive overview for researchers, practitioners, and decision makers, informing future research and applications at the exciting intersection of AI and remote sensing.<\/jats:p>","DOI":"10.3390\/rs15164112","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T00:46:22Z","timestamp":1692665182000},"page":"4112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":156,"title":["A Review of Practical AI for Remote Sensing in Earth Sciences"],"prefix":"10.3390","volume":"15","author":[{"given":"Bhargavi","family":"Janga","sequence":"first","affiliation":[{"name":"Center for Spatial Information Science and Systems, College of Science, George Mason University, 4400 University Drive, MSN 6E1, Fairfax, VA 22030, USA"}]},{"given":"Gokul","family":"Asamani","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science and Systems, College of Science, George Mason University, 4400 University Drive, MSN 6E1, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9810-0023","authenticated-orcid":false,"given":"Ziheng","family":"Sun","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science and Systems, College of Science, George Mason University, 4400 University Drive, MSN 6E1, Fairfax, VA 22030, USA"}]},{"given":"Nicoleta","family":"Cristea","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"key":"ref_1","unstructured":"Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, Guilford Press."},{"key":"ref_2","unstructured":"(2023, July 04). Earthdata Cloud Evolution. Earthdata. 30 March 2022, Available online: https:\/\/www.earthdata.nasa.gov\/eosdis\/cloud-evolution."},{"key":"ref_3","unstructured":"Jensen, J.R. (2009). Remote Sensing of the Environment: An Earth Resource Perspective 2\/e, Pearson Education."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e6239","DOI":"10.1002\/cpe.6239","article-title":"A deep neural network learning-based speckle noise removal technique for enhancing the quality of synthetic-aperture radar images","volume":"33","author":"Mohan","year":"2021","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/MGRS.2022.3145854","article-title":"Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11427-019-9817-6","article-title":"Spatio-temporal fusion for remote sensing data: An overview and new benchmark","volume":"63","author":"Li","year":"2020","journal-title":"Sci. China Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xu, S., Cheng, J., and Zhang, Q. (2021). A Random Forest-Based Data Fusion Method for Obtaining All-Weather Land Surface Temperature with High Spatial Resolution. Remote Sens., 13.","DOI":"10.3390\/rs13112211"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kinaneva, D., Hristov, G., Raychev, J., and Zahariev, P. (2019, January 20\u201324). Early Forest Fire Detection Using Drones and Artificial Intelligence. Proceedings of the 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO.2019.8756696"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2018.2890023","article-title":"Multisource and multitemporal data fusion in remote sensing a comprehensive review of the state of the art","volume":"7","author":"Ghamisi","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"113732","DOI":"10.1016\/j.rse.2023.113732","article-title":"Comparison of gap-filling methods for producing all-weather daily remotely sensed near-surface air temperature","volume":"296","author":"Mo","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/2016RG000543","article-title":"A review of spatial downscaling of satellite remotely sensed soil moisture","volume":"55","author":"Peng","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MGRS.2021.3064051","article-title":"Interpretable Hyperspectral Artificial Intelligence: When nonconvex modeling meets hyperspectral remote sensing","volume":"9","author":"Hong","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","article-title":"Remote sensing image change detection with transformers","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105034","DOI":"10.1016\/j.cageo.2022.105034","article-title":"A review of earth artificial intelligence","volume":"159","author":"Sun","year":"2022","journal-title":"Comput. Geosci."},{"key":"ref_15","unstructured":"Le Moigne, J. (2021, January 30). Artificial Intelligence and Machine Learning for Earth Science. Proceedings of the 2021 International Space University (ISU) Alumni Conference, Online."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"373","DOI":"10.5194\/amt-13-373-2020","article-title":"A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing","volume":"13","author":"Sayer","year":"2020","journal-title":"Atmospheric Meas. Tech."},{"key":"ref_17","unstructured":"Lillesand, T., Kiefer, R.W., and Chipman, J. (2004). Remote Sensing and Image Interpretation, John Wiley & Sons. [5th ed.]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gupta, R.P. (2017). Remote Sensing Geology, Springer.","DOI":"10.1007\/978-3-662-55876-8"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Prasad, S., Bruce, L.M., and Chanussot, J. (2011). Optical Remote Sensing\u2014Advances in Signal Processing and Exploitation Techniques, Springer.","DOI":"10.1007\/978-3-642-14212-3"},{"key":"ref_20","first-page":"23","article-title":"Principles of remote sensing","volume":"23","author":"Aggarwal","year":"2004","journal-title":"Satell. Remote Sens. GIS Appl. Agric. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A survey on object detection in optical remote sensing images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, H., Nguyen, T.-N., and Chuang, T.-W. (2023). An Integrative Explainable Artificial Intelligence Approach to Analyze Fine-Scale Land-Cover and Land-Use Factors Associated with Spatial Distributions of Place of Residence of Reported Dengue Cases. Trop. Med. Infect. Dis., 8.","DOI":"10.3390\/tropicalmed8040238"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kamarulzaman, A.M.M., Jaafar, W.S.W.M., Said, M.N.M., Saad, S.N.M., and Mohan, M. (2023). UAV Implementations in Urban Planning and Related Sectors of Rapidly Developing Nations: A Review and Future Perspectives for Malaysia. Remote Sens., 15.","DOI":"10.3390\/rs15112845"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pettorelli, N. (2013). The Normalized Difference Vegetation Index, Oxford University Press.","DOI":"10.1093\/acprof:osobl\/9780199693160.001.0001"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4512","DOI":"10.1109\/JSTARS.2014.2377248","article-title":"Automation of Customized and Near-Real-Time Vegetation Condition Index Generation Through Cyberinfrastructure-Based Geoprocessing Workflows","volume":"7","author":"Sun","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2511","DOI":"10.1007\/s13762-019-02310-w","article-title":"Detecting vegetation stress as a soil contamination proxy: A review of optical proximal and remote sensing techniques","volume":"16","author":"Gholizadeh","year":"2019","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.13031\/2013.27678","article-title":"Use of Spectral Radiance for Correcting In-season Fertilizer Nitrogen Deficiencies in Winter Wheat","volume":"39","author":"Stone","year":"1996","journal-title":"Trans. ASAE"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.2134\/agronj2002.1215","article-title":"Detection of Phosphorus and Nitrogen Deficiencies in Corn Using Spectral Radiance Measurements","volume":"94","author":"Osborne","year":"2002","journal-title":"Agron. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112399","DOI":"10.1016\/j.rse.2021.112399","article-title":"High-resolution CubeSat imagery and machine learning for detailed snow-covered area","volume":"258","author":"Cannistra","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"John, A., Cannistra, A.F., Yang, K., Tan, A., Shean, D., Lambers, J.H.R., and Cristea, N. (2022). High-Resolution Snow-Covered Area Mapping in Forested Mountain Ecosystems Using PlanetScope Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14143409"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (2009). Remote Sensing with Imaging Radar, Springer.","DOI":"10.1007\/978-3-642-02020-9"},{"key":"ref_32","first-page":"1364","article-title":"Radar interferometry: 20 years of development in time series techniques and future perspectives","volume":"121","author":"Dinh","year":"2020","journal-title":"Remote Sens."},{"key":"ref_33","unstructured":"Oguchi, T., Hayakawa, Y.S., and Wasklewicz, T. (2022). Treatise on Geomorphology, Elsevier."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2248301","article-title":"A tutorial on synthetic aperture radar","volume":"1","author":"Moreira","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Devaney, J., Barrett, B., Barrett, F., Redmond, J., and O\u2019halloran, J. (2015). Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0133583"},{"key":"ref_36","first-page":"44","article-title":"Lidar remote sensing for forestry","volume":"98","author":"Dubayah","year":"2000","journal-title":"J. For."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s13595-011-0102-2","article-title":"The use of terrestrial LiDAR technology in forest science: Application fields, benefits and challenges","volume":"68","author":"Dassot","year":"2011","journal-title":"Ann. For. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"467","DOI":"10.3189\/2013JoG12J154","article-title":"Lidar measurement of snow depth: A review","volume":"59","author":"Deems","year":"2013","journal-title":"J. Glaciol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.1016\/j.rse.2010.02.009","article-title":"Simulating the impact of discrete-return lidar system and survey characteristics over young conifer and broadleaf forests","volume":"114","author":"Disney","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_40","first-page":"239","article-title":"Thermal remote sensing: Concepts, issues and applications","volume":"33","author":"Prakash","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_41","unstructured":"Bakker, W.H., Feringa, W., Gieske, A.S.M., Gorte, B.G.H., Grabmaier, K.A., Hecker, C.A., Horn, J.A., Huurneman, G.C., Janssen, L.L.F., and Kerle, N. (2009). Thermal Remote Sensing, Humboldt.Edu."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Allison, R.S., Johnston, J.M., Craig, G., and Jennings, S. (2016). Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring. Sensors, 16.","DOI":"10.3390\/s16081310"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/S0034-4257(03)00079-8","article-title":"Thermal remote sensing of urban climates","volume":"86","author":"Voogt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"3","article-title":"Spectral imaging for remote sensing","volume":"14","author":"Shaw","year":"2003","journal-title":"Linc. Lab. J."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Manolakis, D.G., Lockwood, R.B., and Cooley, T.W. (2016). Hyperspectral Imaging Remote Sensing: Physics, Sensors, and Algorithms, Cambridge University Press.","DOI":"10.1017\/CBO9781316017876"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/MGRS.2019.2911100","article-title":"Hyperspectral band selection: A review","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Dong, P., and Chen, Q. (2017). LiDAR Remote Sensing and Applications, CRC Press.","DOI":"10.4324\/9781351233354"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_49","first-page":"49","article-title":"A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.gsf.2015.07.003","article-title":"Machine learning in geosciences and remote sensing","volume":"7","author":"Lary","year":"2016","journal-title":"Geosci. Front."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","article-title":"Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1130\/2022.2558(11)","article-title":"A review of cyberinfrastructure for machine learning and big data in the geosciences","volume":"558","author":"Sun","year":"2023","journal-title":"Recent Adv. Geoinformatics Data Sci."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sun, Z., Cristea, N., and Rivas, P. (2023). Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges, Elsevier-Health Sciences Division.","DOI":"10.1016\/B978-0-323-91737-7.00003-7"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Saini, R., and Ghosh, S. (2017, January 5\u20136). Ensemble classifiers in remote sensing: A review. Proceedings of the 2017 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India.","DOI":"10.1109\/CCAA.2017.8229969"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1080\/01431161.2011.602651","article-title":"Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data","volume":"33","author":"Miao","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, J., and Shen, W. (2022). A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications. Appl. Sci., 12.","DOI":"10.3390\/app12178654"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_58","unstructured":"Schapire, R.E. (2023, August 04). A Brief Introduction to Boosting. Psu.Edu. Available online: https:\/\/citeseerx.ist.psu.edu\/document?repid=rep1&type=pdf&doi=fa329f834e834108ccdc536db85ce368fee227ce."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Mascaro, J., Asner, G.P., Knapp, D.E., Kennedy-Bowdoin, T., Martin, R.E., Anderson, C., Higgins, M., and Chadwick, K.D. (2014). A Tale of Two \u201cForests\u201d: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0085993"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier\u2014The Role of Image Composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1128758","DOI":"10.3389\/frwa.2023.1128758","article-title":"High-resolution mapping of snow cover in montane meadows and forests using Planet imagery and machine learning","volume":"5","author":"Yang","year":"2023","journal-title":"Front. Water"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"112608","DOI":"10.1016\/j.rse.2021.112608","article-title":"Multi-sensor fusion using random forests for daily fractional snow cover at 30 m","volume":"264","author":"Rittger","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","article-title":"Investigation of the random forest framework for classification of hyperspectral data","volume":"43","author":"Ham","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Sabat-Tomala, A., Raczko, E., and Zagajewski, B. (2020). Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data. Remote Sens., 12.","DOI":"10.3390\/rs12030516"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Behnamian, A., Banks, S., White, L., Millard, K., Pouliot, D., Pasher, J., and Duffe, J. (August, January 28). Dimensionality Reduction in The Presence of Highly Correlated Variables for Random Forests: Wetland Case Study. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898308"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/15481603.2017.1408892","article-title":"Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application","volume":"55","author":"Georganos","year":"2017","journal-title":"GIScience Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"8489","DOI":"10.3390\/rs70708489","article-title":"On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping","volume":"7","author":"Millard","year":"2015","journal-title":"Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Chen, T., and Carlos, G. (2016). XGBoost: A Scalable Tree Boosting System. arXiv.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"9412","DOI":"10.1080\/01431161.2019.1633696","article-title":"Classification of algal bloom species from remote sensing data using an extreme gradient boosted decision tree model","volume":"40","author":"Ghatkar","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_77","first-page":"352","article-title":"A kernel functions analysis for support vector machines for land cover classification","volume":"11","author":"Kavzoglu","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., and Efros, A.A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_82","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.ecoinf.2018.10.002","article-title":"Deep convolution neural network for image recognition","volume":"48","author":"Traore","year":"2018","journal-title":"Ecol. Inform."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., and Miao, Y. (2021). Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sens., 13.","DOI":"10.3390\/rs13224712"},{"key":"ref_86","unstructured":"Agarap, A.F. (2018). Deep learning using rectified linear units (relu). arXiv."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Aloysius, N., and Geetha, M. (2017, January 6\u20138). A review on deep convolutional neural networks. Proceedings of the 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, India.","DOI":"10.1109\/ICCSP.2017.8286426"},{"key":"ref_88","unstructured":"Dubey, A.K., and Jain, V. (2019). Applications of Computing, Automation and Wireless Systems in Electrical Engineering, Springer."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocs.2018.07.003","article-title":"Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU","volume":"28","author":"Zhang","year":"2018","journal-title":"J. Comput. Sci."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_92","unstructured":"Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Van Esesn, B.C., Awwal, A.A.S., and Asari, V.K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv."},{"key":"ref_93","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2016). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. arXiv."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhang, X., Feng, W., and Xu, J. (2022). Deep Learning Classification by ResNet-18 Based on the Real Spectral Dataset from Multispectral Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14194883"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"103039","DOI":"10.1016\/j.infrared.2019.103039","article-title":"Infrared and visible image fusion with ResNet and zero-phase component analysis","volume":"102","author":"Li","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2015). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_98","unstructured":"Redmon, J. (2023, August 04). Darknet: Open Source Neural Networks in C. Pjreddie.Com. Available online: https:\/\/pjreddie.com\/darknet\/."},{"key":"ref_99","first-page":"1915","article-title":"Rapid Target Detection in High Resolution Remote Sensing Images Using Yolo Model","volume":"42","author":"Wu","year":"2018","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2016). YOLO9000: Better, Faster, Stronger. arXiv.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Xu, D., and Wu, Y. (2020). Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection. Sensors, 20.","DOI":"10.3390\/s20154276"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"012028","DOI":"10.1088\/1742-6596\/2132\/1\/012028","article-title":"An improved YOLO v3 algorithm for remote Sensing image target detection","volume":"2132","author":"Yang","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_103","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","article-title":"A Review of Yolo Algorithm Developments","volume":"199","author":"Jiang","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_105","unstructured":"Terven, J., and Cordova-Esparza, D. (2023). A Comprehensive Review of YOLO: From YOLOv1 and Beyond. arXiv."},{"key":"ref_106","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. arXiv."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 13\u201316). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_108","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Aleissaee, A.A., Kumar, A., Anwer, R.M., Khan, S., Cholakkal, H., Xia, G.-S., and Khan, F.S. (2023). Transformers in Remote Sensing: A Survey. Remote Sens., 15.","DOI":"10.3390\/rs15071860"},{"key":"ref_110","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TGRS.2019.2934760","article-title":"HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation from Transformers","volume":"58","author":"He","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens. A Publ. IEEE Geosci. Remote Sens. Soc."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1080\/01431161.2018.1516313","article-title":"Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series","volume":"40","author":"Sun","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Graves, A., and Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_115","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2023, August 07). Generative Adversarial Nets. Neurips.Cc. Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2014\/file\/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf."},{"key":"ref_116","unstructured":"Ankan, D., Ye, J., and Wang, G. (2021). A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines\u2014From Medical to Remote Sensing. arXiv."},{"key":"ref_117","first-page":"102734","article-title":"A review and meta-analysis of Generative Adversarial Networks and their applications in remote sensing","volume":"108","author":"Jozdani","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative Adversarial Networks: An Overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_119","unstructured":"Xu, C., and Zhao, B. (2018). Satellite Image Spoofing: Creating Remote Sensing Dataset with Generative Adversarial Networks (Short Paper), Schloss Dagstuhl\u2014Leibniz-Zentrum fuer Informatik GmbH."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3140033","article-title":"Thin Cloud Removal for Remote Sensing Images Using a Physical-Model-Based CycleGAN With Unpaired Data","volume":"19","author":"Zi","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A.P., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A.A. (2017). Image-to-Image Translation with Conditional Adversarial Networks. arXiv.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"012060","DOI":"10.1088\/1755-1315\/428\/1\/012060","article-title":"HRPGAN: A GAN-based Model to Generate High-resolution Remote Sensing Images","volume":"428","author":"Sun","year":"2020","journal-title":"IOP Conf. Series Earth Environ. Sci."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1109\/LGRS.2017.2752750","article-title":"MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification","volume":"14","author":"Lin","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Liu, X., Wang, Y., and Liu, Q. (2018, January 7\u201310). Psgan: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451049"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Hu, A., Xie, Z., Xu, Y., Xie, M., Wu, L., and Qiu, Q. (2020). Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks. Remote Sens., 12.","DOI":"10.3390\/rs12244162"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Singh, P., and Komodakis, N. (2018, January 22\u201327). Cloud-Gan: Cloud Removal for Sentinel-2 Imagery Using a Cyclic Consistent Generative Adversarial Networks. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519033"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MSP.2017.2743240","article-title":"Deep Reinforcement Learning: A Brief Survey","volume":"34","author":"Arulkumaran","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_129","unstructured":"Li, Y. (2017). Deep Reinforcement Learning: An Overview. arXiv."},{"key":"ref_130","first-page":"1","article-title":"Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification","volume":"60","author":"Mou","year":"2021","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Fu, K., Li, Y., Sun, H., Yang, X., Xu, G., Li, Y., and Sun, X. (2018). A Ship Rotation Detection Model in Remote Sensing Images Based on Feature Fusion Pyramid Network and Deep Reinforcement Learning. Remote Sens., 10.","DOI":"10.3390\/rs10121922"},{"key":"ref_132","unstructured":"Filar, J., and Vrieze, K. (2012). Competitive Markov Decision Processes, Springer Science & Business Media."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"e1264","DOI":"10.1002\/widm.1264","article-title":"Deep learning for remote sensing image classification: A survey","volume":"8","author":"Li","year":"2018","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1080\/20964471.2019.1657720","article-title":"A survey of remote sensing image classification based on CNNs","volume":"3","author":"Song","year":"2019","journal-title":"Big Earth Data"},{"key":"ref_136","unstructured":"(2023, June 29). Methodology & Accuracy Summary 10m Global Land Use Land Cover Maps. Impactobservatory.Com. Available online: https:\/\/www.impactobservatory.com\/static\/lulc_methodology_accuracy-ee742a0a389a85a0d4e7295941504ac2.pdf."},{"key":"ref_137","unstructured":"(2023, July 05). AI Enables Rapid Creation of Global Land Cover Map. Esri. 7 September 2021. Available online: https:\/\/www.esri.com\/about\/newsroom\/arcuser\/ai-enables-rapid-creation-of-global-land-cover-map\/."},{"key":"ref_138","unstructured":"SpaceKnow (2023, June 29). GEMSTONE CASE STUDY: Global Economic Monitoring Using Satellite Data and AI\/ML Technology. Medium. 25 April 2022. Available online: https:\/\/spaceknow.medium.com\/gemstone-case-study-global-economic-monitoring-using-satellite-data-and-ai-ml-technology-6526c336bf18."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.nhres.2022.10.002","article-title":"Object detection in high resolution optical image based on deep learning technique","volume":"2","author":"Qi","year":"2022","journal-title":"Nat. Hazards Res."},{"key":"ref_140","first-page":"3404858","article-title":"A Change Detection Method for Remote Sensing Images Based on Coupled Dictionary and Deep Learning","volume":"2022","author":"Yang","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2016.2561021","article-title":"Data Fusion and Remote Sensing: An ever-growing relationship","volume":"4","author":"Schmitt","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_142","unstructured":"(2023, June 29). Floodly AI. Esa.Int. 15 January 2021. Available online: https:\/\/business.esa.int\/projects\/floodly-ai."},{"key":"ref_143","unstructured":"Paganini, M., Wyniawskyj, N., Talon, P., White, S., Watson, G., and Petit, D. (2023, July 05). Total Ecosystem Management of the InterTidal Habitat (TEMITH). Esa.Int. 12 September 2020. Available online: https:\/\/eo4society.esa.int\/wp-content\/uploads\/2021\/06\/TEMITH-DMU-TEC-ESR01-11-E_Summary_Report.pdf."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"964769","DOI":"10.3389\/fpls.2022.964769","article-title":"Identification of tree species based on the fusion of UAV hyperspectral image and LiDAR data in a coniferous and broad-leaved mixed forest in Northeast China","volume":"13","author":"Zhong","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_145","unstructured":"Woodie, A. (2023, July 05). AI Opens Door to Expanded Use of LIDAR Data. Datanami. 17 September 2020. Available online: https:\/\/www.datanami.com\/2020\/09\/17\/ai-opens-door-to-expanded-use-of-lidar-data\/."},{"key":"ref_146","unstructured":"(2023, June 29). Technology. Metaspectral. 20 September 2022. Available online: https:\/\/metaspectral.com\/technology\/."},{"key":"ref_147","unstructured":"Redins, L. (2023, July 05). Metaspectral\u2019s AI Platform Uses Hyperspectral Imaging, Edge Computing to Transform Space, Recycling and Other Industries. 26 January 2023. Available online: https:\/\/www.edgeir.com\/metaspectrals-ai-platform-uses-hyperspectral-imaging-edge-computing-to-transform-space-recycling-and-other-industries-20230125."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/s41597-023-02053-x","article-title":"A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset","volume":"10","author":"Skulovich","year":"2023","journal-title":"Sci. Data"},{"key":"ref_149","unstructured":"(2023, July 05). Esen, Berivan, and Jonathan Wentworth. 2020. \u201cRemote Sensing and Machine Learning.\u201d Parliament.Uk. 19 June 2020. Available online: https:\/\/post.parliament.uk\/research-briefings\/post-pn-0628\/."},{"key":"ref_150","first-page":"1","article-title":"The dataset nutrition label","volume":"12","author":"Holland","year":"2020","journal-title":"Data Prot. Priv."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2012.02.022","article-title":"Near real-time disturbance detection using satellite image time series","volume":"123","author":"Verbesselt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ecolind.2015.12.009","article-title":"Remote sensing for lake research and monitoring\u2014Recent advances","volume":"64","author":"Oppelt","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1080\/10473289.2004.10471005","article-title":"Recommendations on the Use of Satellite Remote-Sensing Data for Urban Air Quality","volume":"54","author":"Hoff","year":"2004","journal-title":"J. Air Waste Manag. Assoc."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s40745-020-00253-5","article-title":"A Comprehensive Survey of Loss Functions in Machine Learning","volume":"9","author":"Wang","year":"2020","journal-title":"Ann. Data Sci."},{"key":"ref_155","first-page":"46","article-title":"Mixture of expert agents for handling imbalanced data sets","volume":"1","author":"Kotsiantis","year":"2003","journal-title":"Ann. Math. Comput. Teleinform."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"2052013","DOI":"10.1142\/S0218001420520138","article-title":"A comparison of optimization algorithms for deep learning","volume":"34","author":"Soydaner","year":"2020","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Sheng, V.S., Provost, F., and Ipeirotis, P.G. (2008, January 24\u201327). Get another label? improving data quality and data mining using multiple, noisy labelers. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA.","DOI":"10.1145\/1401890.1401965"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"217","DOI":"10.14358\/PERS.71.2.217","article-title":"Urban DEM generation from raw LiDAR data","volume":"71","author":"Shan","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/MGRS.2015.2434351","article-title":"Fusing Landsat and MODIS Data for Vegetation Monitoring","volume":"3","author":"Gao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2179050","article-title":"Satellite Image Time Series Analysis Under Time Warping","volume":"50","author":"Petitjean","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Griffith, D.A., and Chun, Y. (2016). Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data. Remote Sens., 8.","DOI":"10.3390\/rs8070535"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1109\/36.843034","article-title":"Evaluation of sensor calibration uncertainties on vegetation indices for MODIS","volume":"38","author":"Miura","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_164","first-page":"W05416","article-title":"A global analysis of temporal and spatial variations in continental water storage","volume":"43","author":"Stuck","year":"2007","journal-title":"Water Resour. Res."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"100019","DOI":"10.1016\/j.srs.2021.100019","article-title":"UAV & satellite synergies for optical remote sensing applications: A literature review","volume":"3","author":"Corpetti","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.inffus.2022.06.003","article-title":"Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives","volume":"86\u201387","author":"Himeur","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1007\/s13347-021-00477-0","article-title":"Transparency and the black box problem: Why we do not trust AI","volume":"34","year":"2021","journal-title":"Philos. Technol."},{"key":"ref_168","first-page":"102520","article-title":"Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing","volume":"103","author":"Kakogeorgiou","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"688969","DOI":"10.3389\/fdata.2021.688969","article-title":"Principles and Practice of Explainable Machine Learning","volume":"4","author":"Belle","year":"2021","journal-title":"Front. Big Data"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Torrey, L., and Shavlik, J. (2010). Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global.","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"105151","DOI":"10.1016\/j.engappai.2022.105151","article-title":"Ensemble deep learning: A review","volume":"115","author":"Ganaie","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/JPROC.2016.2598228","article-title":"Big Data for Remote Sensing: Challenges and Opportunities","volume":"104","author":"Chi","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Xie, M., Jean, N., Burke, M., Lobell, D., and Ermon, S. (2016, January 12\u201317). Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.9906"},{"key":"ref_175","unstructured":"Benchaita, S., and Mccarthy, B.H. (2023, August 10). IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face. IBM Newsroom. 3 August 2023. Available online: https:\/\/newsroom.ibm.com\/2023-08-03-IBM-and-NASA-Open-Source-Largest-Geospatial-AI-Foundation-Model-on-Hugging-Face."},{"key":"ref_176","first-page":"8052","article-title":"Generalizing to Unseen Domains: A Survey on Domain Generalization","volume":"35","author":"Wang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3457607","article-title":"A Survey on Bias and Fairness in Machine Learning","volume":"54","author":"Mehrabi","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Roselli, D., Matthews, J., and Talagala, N. (2019, January 13\u201317). Managing bias in AI. Proceedings of the 2019 World Wide Web Conference, New York, NY, USA.","DOI":"10.1145\/3308560.3317590"},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Raji, I.D., Smart, A., White, R.N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., and Barnes, P. (2020, January 27\u201330). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, Spain.","DOI":"10.1145\/3351095.3372873"},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"Alkhelaiwi, M., Boulila, W., Ahmad, J., Koubaa, A., and Driss, M. (2021). An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13112221"},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1016\/j.optcom.2011.12.023","article-title":"Remote-sensing image encryption in hybrid domains","volume":"285","author":"Zhang","year":"2012","journal-title":"Opt. Commun."},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Potkonjak, M., Meguerdichian, S., and Wong, J.L. (2010, January 1\u20134). Trusted sensors and remote sensing. Proceedings of the SENSORS, 2010 IEEE, Waikoloa, HI, USA.","DOI":"10.1109\/ICSENS.2010.5690721"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Ismael","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1139\/er-2020-0019","article-title":"A review of machine learning applications in wildfire science and management","volume":"28","author":"Jain","year":"2020","journal-title":"Environ. Rev."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"108309","DOI":"10.1016\/j.sigpro.2021.108309","article-title":"A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms","volume":"190","author":"Bouguettaya","year":"2022","journal-title":"Signal Process."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10462-018-09679-z","article-title":"Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: A survey","volume":"52","author":"Nguyen","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1109\/JSTARS.2020.3021052","article-title":"Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review","volume":"13","author":"Amani","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_188","unstructured":"Garbini, S. (2023, July 06). How Geospatial AI Can Help You Comply with EU\u2019s Deforestation Law\u2014Customers. Picterra. 25 April 2023. Available online: https:\/\/picterra.ch\/blog\/how-geospatial-ai-can-help-you-comply-with-eus-deforestation-law\/."},{"key":"ref_189","doi-asserted-by":"crossref","unstructured":"Mujetahid, A., Nursaputra, M., and Soma, A.S. (2023). Monitoring Illegal Logging Using Google Earth Engine in Sulawesi Selatan Tropical Forest, Indonesia. Forests, 14.","DOI":"10.3390\/f14030652"},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Rivero, M., Beijbom, O., Rodriguez-Ramirez, A., Bryant, D.E., Ganase, A., Gonzalez-Marrero, Y., Herrera-Reveles, A., Kennedy, E.V., Kim, C.J., and Lopez-Marcano, S. (2020). Monitoring of Coral Reefs Using Artificial Intelligence: A Feasible and Cost-Effective Approach. Remote Sens., 12.","DOI":"10.3390\/rs12030489"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1007\/s00530-020-00733-x","article-title":"Application of machine learning in ocean data","volume":"29","author":"Lou","year":"2021","journal-title":"Multimedia Syst."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"918104","DOI":"10.3389\/fmars.2022.918104","article-title":"Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective","volume":"9","author":"Ditria","year":"2022","journal-title":"Front. Mar. Sci."},{"key":"ref_193","first-page":"418","article-title":"Artificial intelligence and big data science for oceanographic research in Bangladesh: Preparing for the future","volume":"38","author":"Shafiq","year":"2023","journal-title":"J. Data Acquis. Process."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1023\/A:1018348204573","article-title":"Image analysis, neural networks, and the taxonomic impediment to biodiversity studies","volume":"6","author":"Weeks","year":"1997","journal-title":"Biodivers. Conserv."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1038\/s41893-022-00851-6","article-title":"Improving biodiversity protection through artificial intelligence","volume":"5","author":"Silvestro","year":"2022","journal-title":"Nat. Sustain."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.biocon.2019.01.023","article-title":"Social media data for conservation science: A methodological overview","volume":"233","author":"Toivonen","year":"2019","journal-title":"Biol. Conserv."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"e2021RG000763","DOI":"10.1029\/2021RG000763","article-title":"Health and Safety Effects of Airborne Soil Dust in the Americas and Beyond","volume":"61","author":"Tong","year":"2023","journal-title":"Rev. Geophys."},{"key":"ref_198","unstructured":"Alnuaim, A., Ziheng, S., and Didarul, I. (2023). Artificial Intelligence in Earth Science, Elsevier."},{"key":"ref_199","doi-asserted-by":"crossref","unstructured":"Bragazzi, N.L., Dai, H., Damiani, G., Behzadifar, M., Martini, M., and Wu, J. (2020). How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic. Int. J. Environ. Res. Public Heal., 17.","DOI":"10.3390\/ijerph17093176"},{"key":"ref_200","doi-asserted-by":"crossref","unstructured":"Alnaim, A., Sun, Z., and Tong, D. (2022). Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants. Remote Sens., 14.","DOI":"10.3390\/rs14030729"},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.dsx.2020.04.012","article-title":"Artificial Intelligence (AI) applications for COVID-19 pandemic","volume":"14","author":"Vaishya","year":"2020","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1191\/0309133303pp360ra","article-title":"LiDAR remote sensing of forest structure","volume":"27","author":"Lim","year":"2003","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_203","doi-asserted-by":"crossref","unstructured":"Liu, L., Zhang, Q., Guo, Y., Chen, E., Li, Z., Li, Y., Wang, B., and Ri, A. (2023). Mapping the Distribution and Dynamics of Coniferous Forests in Large Areas from 1985 to 2020 Combining Deep Learning and Google Earth Engine. Remote Sens., 15.","DOI":"10.3390\/rs15051235"},{"key":"ref_204","first-page":"4747","article-title":"Precision Forestry: Integration of Robotics and Sensing Technologies for Tree Measurement and Monitoring","volume":"12","author":"Sharma","year":"2023","journal-title":"Eur. Chem. Bull."},{"key":"ref_205","unstructured":"Stere\u0144czak, K. (2023, July 05). Precision Forestry. IDEAS NCBR\u2014Intelligent Algorithms for Digital Economy. 13 April 2023. Available online: https:\/\/ideas-ncbr.pl\/en\/research\/precision-forestry\/."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"8085","DOI":"10.1109\/JSTARS.2022.3206399","article-title":"YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning","volume":"15","author":"Liu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_207","doi-asserted-by":"crossref","unstructured":"Amila, J., Ranaweera, N., Abenayake, C., Bandara, N., and De Silva, C. (2023). Modelling vegetation land fragmentation in urban areas of Western Province, Sri Lanka using an Artificial Intelligence-based simulation technique. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0275457"},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.solener.2006.06.005","article-title":"The London Heat Island and building cooling design","volume":"81","author":"Kolokotroni","year":"2007","journal-title":"Sol. Energy"},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s44212-022-00002-4","article-title":"An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data","volume":"1","author":"Lyu","year":"2022","journal-title":"Urban Inform."},{"key":"ref_210","doi-asserted-by":"crossref","unstructured":"Rahman, A., Roy, S.S., Talukdar, S. (2023). Advancements in Urban Environmental Studies: Application of Geospatial Technology and Artificial Intelligence in Urban Studies, Springer International Publishing.","DOI":"10.1007\/978-3-031-21587-2"},{"key":"ref_211","doi-asserted-by":"crossref","unstructured":"Alnaim, A., and Ziheng, S. (2022, January 11\u201314). Using Geoweaver to Make Snow Mapping Workflow FAIR. Proceedings of the 2022 IEEE 18th International Conference on e-Science (e-Science), Salt Lake City, UT, USA.","DOI":"10.1109\/eScience55777.2022.00062"},{"key":"ref_212","unstructured":"Yang, K., John, A., Sun, Z., and Cristea, N. (2023). Artificial Intelligence in Earth Science, Elsevier."},{"key":"ref_213","doi-asserted-by":"crossref","unstructured":"An, S., and Rui, X. (2022). A High-Precision Water Body Extraction Method Based on Improved Lightweight U-Net. Remote. Sens., 14.","DOI":"10.3390\/rs14174127"},{"key":"ref_214","doi-asserted-by":"crossref","unstructured":"Al-Bakri, J.T., D\u2019Urso, G., Calera, A., Abdalhaq, E., Altarawneh, M., and Margane, A. (2022). Remote Sensing for Agricultural Water Management in Jordan. Remote Sens., 15.","DOI":"10.3390\/rs15010235"},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"106515","DOI":"10.1016\/j.eiar.2020.106515","article-title":"Urban water resource management for sustainable environment planning using artificial intelligence techniques","volume":"86","author":"Xiang","year":"2020","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"073001","DOI":"10.1088\/1748-9326\/ab1b7d","article-title":"How can Big Data and machine learning benefit environment and water management: A survey of methods, applications, and future directions","volume":"14","author":"Sun","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1007\/s11069-020-04124-3","article-title":"Applications of artificial intelligence for disaster management","volume":"103","author":"Sun","year":"2020","journal-title":"Nat. Hazards"},{"key":"ref_218","unstructured":"Chapman, A. (2023). Leveraging Big Data and AI for Disaster Resilience and Recovery, Texas A&M University College of Engineering. Available online: https:\/\/engineering.tamu.edu\/news\/2023\/06\/leveraging-big-data-and-ai-for-disaster-resilience-and-recovery.html."},{"key":"ref_219","unstructured":"Imran, M., Castillo, C., Lucas, J., Meier, P., and Vieweg, S. (2014, January 7\u201311). AIDR: Artificial Intelligence for Disaster Response. Proceedings of the 23rd International Conference on World Wide Web, Seoul, Republic of Korea."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"100363","DOI":"10.1016\/j.patter.2021.100363","article-title":"Fairness and accountability of AI in disaster risk management: Opportunities and challenges","volume":"2","author":"Gevaert","year":"2021","journal-title":"Patterns"},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s41060-023-00393-w","article-title":"AI and Data Science for Smart Emergency, Crisis and Disaster Resilience","volume":"15","author":"Cao","year":"2023","journal-title":"Int. J. Data Sci. Anal."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/4112\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:38:55Z","timestamp":1760128735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/4112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,21]]},"references-count":221,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15164112"],"URL":"https:\/\/doi.org\/10.3390\/rs15164112","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,21]]}}}