{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T10:41:58Z","timestamp":1775558518543,"version":"3.50.1"},"reference-count":87,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T00:00:00Z","timestamp":1752451200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon Europe Research and Innovation Program","award":["101073952"],"award-info":[{"award-number":["101073952"]}]},{"name":"GRNET","award":["101073952"],"award-info":[{"award-number":["101073952"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Oil spills on the water surface pose a significant environmental hazard, underscoring the critical need for developing Artificial Intelligence (AI) detection methods. Utilizing Unmanned Aerial Vehicles (UAVs) can significantly improve the efficiency of oil spill detection at early stages, reducing environmental damage; however, there is a lack of training datasets in the domain. In this paper, LADOS is introduced, an aeriaL imAgery Dataset for Oil Spill detection, classification, and localization by incorporating both liquid and solid classes of low-altitude images. LADOS comprises 3388 images annotated at the pixel level across six distinct classes, including the background. In addition to including a general oil class describing various oil spill appearances, LADOS provides a detailed categorization by including emulsions and sheens. Detailed examination of both instance and semantic segmentation approaches is illustrated to validate the dataset\u2019s performance and significance to the domain. The results on the test set demonstrate an overall performance exceeding 66% mean Intersection over Union (mIoU), with specific classes such as oil and emulsion to surpass 74% of IoU part of the experiments.<\/jats:p>","DOI":"10.3390\/data10070117","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T08:04:41Z","timestamp":1752566681000},"page":"117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["LADOS: Aerial Imagery Dataset for Oil Spill Detection, Classification, and Localization Using Semantic Segmentation"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7711-3773","authenticated-orcid":false,"given":"Konstantinos","family":"Gkountakos","sequence":"first","affiliation":[{"name":"Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece"},{"name":"Department of Agricultural Economics and Rural Development, Agricultural University of Athens (AUA), 11855 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2111-3151","authenticated-orcid":false,"given":"Maria","family":"Melitou","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6767-8762","authenticated-orcid":false,"given":"Konstantinos","family":"Ioannidis","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8858-6389","authenticated-orcid":false,"given":"Konstantinos","family":"Demestichas","sequence":"additional","affiliation":[{"name":"Department of Agricultural Economics and Rural Development, Agricultural University of Athens (AUA), 11855 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2505-9178","authenticated-orcid":false,"given":"Stefanos","family":"Vrochidis","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6447-9020","authenticated-orcid":false,"given":"Ioannis","family":"Kompatsiaris","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/416039a","article-title":"Determining the composition of the Earth","volume":"416","author":"Drake","year":"2002","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"197715","DOI":"10.1109\/ACCESS.2024.3522248","article-title":"A Comprehensive Review of Deep Learning-Based Anomaly Detection Methods for Precision Agriculture","volume":"12","author":"Gkountakos","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","first-page":"1","article-title":"The Effects of the Ports and Water Transportation on the Aquatic Ecosystem","volume":"10","author":"Selamoglu","year":"2021","journal-title":"Open Access J. Biog. Sci. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2495\/WM120011","article-title":"Illegal dumping investigation: A new challenge for forensic environmental engineering","volume":"163","author":"Lega","year":"2012","journal-title":"WIT Trans. Ecol. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111921","DOI":"10.1016\/j.marpolbul.2020.111921","article-title":"Marine oil spill detection using synthetic aperture radar over indian ocean","volume":"162","author":"Naz","year":"2021","journal-title":"Mar. Pollut. Bull."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/0146-6380(96)00010-1","article-title":"Organic geochemistry applied to environmental assessments of Prince William Sound, Alaska, after the Exxon Valdez oil spill\u2014A review","volume":"24","author":"Bence","year":"1996","journal-title":"Org. Geochem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.rse.2012.03.024","article-title":"State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill","volume":"124","author":"Leifer","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"122978","DOI":"10.1016\/j.jclepro.2020.122978","article-title":"Marine oil spill pollution causes and governance: A case study of Sanchi tanker collision and explosion","volume":"273","author":"Chen","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rajendran, S., Sadooni, F.N., Al-Kuwari, H.A.S., Oleg, A., Govil, H., Nasir, S., and Vethamony, P. (2021). Monitoring oil spill in Norilsk, Russia using satellite data. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-83260-7"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Llenque, J.C.E., Valiente, M.M., Fababa, J.C.C., Quiroz, L.E., Del Castillo, M.C., Gonzales, J.J.P., Sanchez, M.R., Lynes, G.M., and Ortiz, J.M.Q. (2024, January 18\u201320). Identification of the Marine Coast Area Affected by Oil Spill Using Multispectral Satellite and UAV Images in Ventanilla-Callao, Per\u00fa. Proceedings of the 2024 IEEE Biennial Congress of Argentina (ARGENCON), San Nicol\u00e1s de los Arroyos, Argentina.","DOI":"10.1109\/ARGENCON62399.2024.10735911"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Al-Ruzouq, R., Gibril, M.B.A., Shanableh, A., Kais, A., Hamed, O., Al-Mansoori, S., and Khalil, M.A. (2020). Sensors, features, and machine learning for oil spill detection and monitoring: A review. Remote Sens., 12.","DOI":"10.3390\/rs12203338"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nezhad, M., Groppi, D., Laneve, G., Marzialetti, P.A., and Piras, G. (2018, January 8\u201310). Oil Spill Detection Analyzing \u201cSentinel 2 \u201cSatellite Images: A Persian Gulf Case Study. Proceedings of the 3rdWorld Congress on Civil, Structural, and Environmental Engineering (CSEE\u201918), Budapest, Hungary.","DOI":"10.11159\/awspt18.134"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Orfanidis, G., Ioannidis, K., Avgerinakis, K., Vrochidis, S., and Kompatsiaris, I. (2018, January 7\u201310). A deep neural network for oil spill semantic segmentation in Sar images. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451113"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Krestenitis, M., Orfanidis, G., Ioannidis, K., Avgerinakis, K., Vrochidis, S., and Kompatsiaris, I. (2019). Early identification of oil spills in satellite images using deep CNNs. Proceedings of the MultiMedia Modeling: 25th International Conference, MMM 2019, Springer.","DOI":"10.1007\/978-3-030-05710-7_35"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2017.09.002","article-title":"Oil spill detection by imaging radars: Challenges and pitfalls","volume":"201","author":"Alpers","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mancini, A., Frontoni, E., and Zingaretti, P. (2019, January 11\u201314). Satellite and uav data for precision agriculture applications. Proceedings of the 2019 International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA.","DOI":"10.1109\/ICUAS.2019.8797930"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1016\/j.cie.2018.11.008","article-title":"A new approach to oil spill detection that combines deep learning with unmanned aerial vehicles","volume":"135","author":"Jiao","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_18","first-page":"279","article-title":"Automatic oil spill detection on quad polarimetric UAVSAR imagery","volume":"Volume 9853","author":"Rahnemoonfar","year":"2016","journal-title":"Proceedings of the Polarization: Measurement, analysis, and remote sensing XII"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2611","DOI":"10.1016\/j.marpolbul.2011.09.036","article-title":"Oil spill detection with fully polarimetric UAVSAR data","volume":"62","author":"Liu","year":"2011","journal-title":"Mar. Pollut. Bull."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s12601-020-0023-9","article-title":"Oil spill four-class classification using UAVSAR polarimetric data","volume":"55","author":"Hassani","year":"2020","journal-title":"Ocean. Sci. J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gallego, A.J., Gil, P., Pertusa, A., and Fisher, R.B. (2018). Segmentation of oil spills on side-looking airborne radar imagery with autoencoders. Sensors, 18.","DOI":"10.3390\/s18030797"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gallego, A.J., Gil, P., Pertusa, A., and Fisher, R.B. (2019). Semantic segmentation of SLAR imagery with convolutional LSTM selectional autoencoders. Remote Sens., 11.","DOI":"10.3390\/rs11121402"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5217","DOI":"10.1109\/TGRS.2018.2812619","article-title":"Two-stage convolutional neural network for ship and spill detection using SLAR images","volume":"56","author":"Gallego","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9071","DOI":"10.1109\/JSTARS.2021.3109951","article-title":"Identifying oil spill types based on remotely sensed reflectance spectra and multiple machine learning algorithms","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, J., Hu, Y., Zhang, J., Ma, Y., Li, Z., and Jiang, Z. (2023). Identification of marine oil spill pollution using hyperspectral combined with thermal infrared remote sensing. Front. Mar. Sci., 10.","DOI":"10.3389\/fmars.2023.1135356"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Zhang, J., Ma, Y., and Mao, X. (2021). Hyperspectral remote sensing detection of marine oil spills using an adaptive long-term moment estimation optimizer. Remote Sens., 14.","DOI":"10.3390\/rs14010157"},{"key":"ref_27","unstructured":"Kerf, T.D., Sels, S., Samsonova, S., and Vanlanduit, S. (2024). Oil Spill Drone: A Dataset of Drone-Captured, Segmented RGB Images for Oil Spill Detection in Port Environments. arXiv."},{"key":"ref_28","unstructured":"Gkountakos, K. (2025, May 05). LADOS Dataset. Available online: https:\/\/universe.roboflow.com\/konstantinos-gkountakos\/lados."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fingas, M., and Brown, C.E. (2017). A review of oil spill remote sensing. Sensors, 18.","DOI":"10.3390\/s18010091"},{"key":"ref_30","unstructured":"Drons (2025, May 05). OIL Dataset. Available online: https:\/\/universe.roboflow.com\/drons-kogn4\/oil-8sfgp."},{"key":"ref_31","unstructured":"chris (2025, May 05). yhyth Dataset. Available online: https:\/\/universe.roboflow.com\/chris-mxxpq\/yhyth."},{"key":"ref_32","unstructured":"kriti (2025, May 05). Oil Spillage Dataset. Available online: https:\/\/universe.roboflow.com\/kriti-bcb8w\/oil-spillage-4aupb."},{"key":"ref_33","unstructured":"Toast, B. (2025, May 05). Oil Spill Two Dataset. Available online: https:\/\/universe.roboflow.com\/baka-toast\/oil-spill-two."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Stavrothanasopoulos, K., Gkountakos, K., Ioannidis, K., Tsikrika, T., Vrochidis, S., and Kompatsiaris, I. (2025). CylinDeRS: A Benchmark Visual Dataset for Robust Gas Cylinder Detection and Attribute Classification in Real-World Scenes. Sensors, 25.","DOI":"10.3390\/s25041016"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Krestenitis, M., Orfanidis, G., Ioannidis, K., Avgerinakis, K., Vrochidis, S., and Kompatsiaris, I. (2019). Oil Spill Identification from Satellite Images Using Deep Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11151762"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105716","DOI":"10.1016\/j.asoc.2019.105716","article-title":"Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms","volume":"84","author":"Cantorna","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3230829","article-title":"Oil Spill Contextual and Boundary-Supervised Detection Network Based on Marine SAR Images","volume":"60","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"116549","DOI":"10.1016\/j.marpolbul.2024.116549","article-title":"Marine oil spill detection and segmentation in SAR data with two steps deep learning framework","volume":"204","year":"2024","journal-title":"Mar. Pollut. Bull."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Shaban, M., Salim, R., Abu Khalifeh, H., Khelifi, A., Shalaby, A., El-Mashad, S., Mahmoud, A., Ghazal, M., and El-Baz, A. (2021). A deep-learning framework for the detection of oil spills from SAR data. Sensors, 21.","DOI":"10.3390\/s21072351"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bianchi, F.M., Espeseth, M.M., and Borch, N. (2020). Large-scale detection and categorization of oil spills from SAR images with deep learning. Remote Sens., 12.","DOI":"10.3390\/rs12142260"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1080\/19475705.2022.2155998","article-title":"Deep neural network for oil spill detection using Sentinel-1 data: Application to Egyptian coastal regions","volume":"14","author":"Ahmed","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"116637","DOI":"10.1016\/j.jenvman.2022.116637","article-title":"Detection of marine oil spills from radar satellite images for the coastal ecological risk assessment","volume":"325","author":"Ma","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_43","unstructured":"Blondeau-Patissier, D., Schroeder, T., Diakogiannis, F., and Li, Z. CSIRO Sentinel-1 SAR image dataset of oil- and non-oil features for machine learning (Deep Learning). Data Collect., 2022."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.isprsjprs.2020.07.011","article-title":"A novel deep learning instance segmentation model for automated marine oil spill detection","volume":"167","author":"Yekeen","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","unstructured":"Li, Y., Yang, X., Ye, Y., Cui, L., Jia, B., Jiang, Z., and Wang, S. (2017, January 8\u201310). Detection of oil spill through fully convolutional network. Proceedings of the Geo-Spatial Knowledge and Intelligence: 5th International Conference, GSKI 2017, Chiang Mai, Thailand."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kolokoussis, P., and Karathanassi, V. (2018). Oil spill detection and mapping using sentinel 2 imagery. J. Mar. Sci. Eng., 6.","DOI":"10.3390\/jmse6010004"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.isprsjprs.2024.02.017","article-title":"Detecting Marine pollutants and Sea Surface features with Deep learning in Sentinel-2 imagery","volume":"210","author":"Kikaki","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","first-page":"1348","article-title":"ROSID: Remote Sensing Satellite Data for Oil Spill Detection on Land","volume":"32","author":"Nurseitov","year":"2024","journal-title":"Eng. Sci."},{"key":"ref_49","first-page":"1","article-title":"Hyperspectral remote sensing benchmark database for oil spill detection with an isolation forest-guided unsupervised detector","volume":"61","author":"Duan","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"111421","DOI":"10.1016\/j.rse.2019.111421","article-title":"Classification of oil spill by thicknesses using multiple remote sensors","volume":"236","author":"Staples","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"117884","DOI":"10.1016\/j.envpol.2021.117884","article-title":"Monitoring offshore oil pollution using multi-class convolutional neural networks","volume":"289","author":"Ghorbani","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.swevo.2019.01.005","article-title":"Distributed operation of collaborating unmanned aerial vehicles for time-sensitive oil spill mapping","volume":"46","author":"Odonkor","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"De Kerf, T., Gladines, J., Sels, S., and Vanlanduit, S. (2020). Oil spill detection using machine learning and infrared images. Remote Sens., 12.","DOI":"10.3390\/rs12244090"},{"key":"ref_54","unstructured":"CIBER Lab (2025, April 18). NAFTA. Available online: https:\/\/github.com\/ciber-lab\/nafta."},{"key":"ref_55","unstructured":"PPE (2025, May 05). Oil Spillage Dataset. Available online: https:\/\/universe.roboflow.com\/ppe-syzfj\/oil-spillage-4aupb-yytkd-1wdix-sksmd."},{"key":"ref_56","unstructured":"kafabillariskolarngbayanpupeduph (2025, May 05). Thesis Dataset. Available online: https:\/\/universe.roboflow.com\/kafabillariskolarngbayanpupeduph\/thesis-7ot3a."},{"key":"ref_57","unstructured":"ACES (2025, May 05). Oil Spills Dataset. Available online: https:\/\/universe.roboflow.com\/aces\/oil-spills-ubmlx."},{"key":"ref_58","unstructured":"Oil Spill (2025, May 05). Oil Stain Detection DATASET. Available online: https:\/\/universe.roboflow.com\/oil-spill-a0l20\/oil-stain-detection."},{"key":"ref_59","unstructured":"ObjectdetectionFruits (2025, May 05). Oil_spill Dataset. Available online: https:\/\/universe.roboflow.com\/objectdetectionfruits-moegm\/oilspill-qbagr."},{"key":"ref_60","unstructured":"Pollution, O. (2025, May 05). Test Dataset. Available online: https:\/\/universe.roboflow.com\/oil-pollution\/test-ka7p4."},{"key":"ref_61","unstructured":"Karanov, D. (2025, May 05). Oil Spill Classification Dataset. Available online: https:\/\/universe.roboflow.com\/dmitry-karanov-xnath\/oil-spill-classification."},{"key":"ref_62","unstructured":"Detection (2025, May 05). Offshore Oil Spills3 Dataset. Available online: https:\/\/universe.roboflow.com\/detection-tvlmd\/offshore-oil-spills3."},{"key":"ref_63","unstructured":"Dataset, O. (2025, May 05). Oilspill_v2 Dataset. Available online: https:\/\/universe.roboflow.com\/oilspill-dataset\/oilspillv2."},{"key":"ref_64","unstructured":"Group, O. (2025, May 05). Sea Report Oil Dataset. Available online: https:\/\/universe.roboflow.com\/open-group\/sea-report-oil."},{"key":"ref_65","unstructured":"Karanov, D. (2025, May 05). Oil Spill One Class Classification Dataset. Available online: https:\/\/universe.roboflow.com\/dmitry-karanov-xnath\/oil-spill-one-class-classification."},{"key":"ref_66","unstructured":"Punthon (2025, May 05). Oil-Spill-Segmentation Dataset. Available online: https:\/\/universe.roboflow.com\/punthon-i5rmx\/oil-spill-segmentation-tysge."},{"key":"ref_67","unstructured":"Karanov, D. (2025, May 05). Oil Spots Segmentation Dataset. Available online: https:\/\/universe.roboflow.com\/dmitry-karanov-xnath\/oil-spots-segmentation."},{"key":"ref_68","unstructured":"Detection (2025, May 05). Offshore Oil Spills2 Dataset. Available online: https:\/\/universe.roboflow.com\/detection-tvlmd\/offshore-oil-spills2."},{"key":"ref_69","unstructured":"Computervision (2025, May 05). Oil_Spill_Seg Dataset. Available online: https:\/\/universe.roboflow.com\/computervision-naujm\/oilspillseg."},{"key":"ref_70","unstructured":"hamdi ali (2025, May 05). Oil Pollution Dataset. Available online: https:\/\/universe.roboflow.com\/hamdi-ali\/oil-pollution."},{"key":"ref_71","unstructured":"Drons (2025, May 05). Oil Spills v1 Dataset. Available online: https:\/\/universe.roboflow.com\/drons-kogn4\/oil-spills-v1."},{"key":"ref_72","unstructured":"idkman (2025, May 05). Oil Spill Dataset. Available online: https:\/\/universe.roboflow.com\/idkman\/oil-spill-rcnm4."},{"key":"ref_73","unstructured":"amjad (2025, May 05). Oil Spli New v Dataset. Available online: https:\/\/universe.roboflow.com\/amjad-myr3z\/oil-spli-new-v."},{"key":"ref_74","unstructured":"kafabillariskolarngbayanpupeduph (2025, May 05). Custom_Data Dataset. Available online: https:\/\/universe.roboflow.com\/kafabillariskolarngbayanpupeduph\/customdata-5nmls."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TBDATA.2019.2921572","article-title":"Billion-scale similarity search with GPUs","volume":"7","author":"Johnson","year":"2019","journal-title":"IEEE Trans. Big Data"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/S0025-326X(03)00212-1","article-title":"Studies of the formation process of water-in-oil emulsions","volume":"47","author":"Fingas","year":"2003","journal-title":"Mar. Pollut. Bull."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Fingas, M. (2021). Visual appearance of oil on the sea. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9010097"},{"key":"ref_78","unstructured":"Lewis, A. (2009, January 12\u201314). The development and use of the Bonn Agreement oil appearance code (BAOAC). Proceedings of the Interspill Conference, Marseilles, France."},{"key":"ref_79","unstructured":"Khanam, R., and Hussain, M. (2024). Yolov11: An overview of the key architectural enhancements. arXiv."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_81","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., and Torr, P.H. (2021, January 20\u201325). Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., and Girdhar, R. (2022, January 18\u201324). Masked-attention mask transformer for universal image segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft coco: Common objects in context. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_85","unstructured":"Cordts, M., Omran, M., Ramos, S., Scharw\u00e4chter, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2015, January 7\u201312). The cityscapes dataset. Proceedings of the CVPR Workshop on the Future of Datasets in Vision, Boston, MA, USA."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"01003","DOI":"10.1051\/itmconf\/20246401003","article-title":"An empirical study on the correlation between early stopping patience and epochs in deep learning","volume":"Volume 64","author":"Hussein","year":"2024","journal-title":"Proceedings of the ITM Web of Conferences"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.gltp.2022.04.020","article-title":"A review: Data pre-processing and data augmentation techniques","volume":"3","author":"Maharana","year":"2022","journal-title":"Glob. Transitions Proc."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/7\/117\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:09:50Z","timestamp":1760033390000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/7\/117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,14]]},"references-count":87,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["data10070117"],"URL":"https:\/\/doi.org\/10.3390\/data10070117","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,14]]}}}