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Their large size (2\u20134 m) and continent-wide distribution make them ideal candidates for monitoring programs via very-high-resolution satellite imagery. The sheer volume of imagery required, however, hampers our ability to rely on manual annotation alone. Here, we present SealNet 2.0, a fully automated approach to seal detection that couples a sea ice segmentation model to find potential seal habitats with an ensemble of semantic segmentation convolutional neural network models for seal detection. Our best ensemble attains 0.806 precision and 0.640 recall on an out-of-sample test dataset, surpassing two trained human observers. Built upon the original SealNet, it outperforms its predecessor by using annotation datasets focused on sea ice only, a comprehensive hyperparameter study leveraging substantial high-performance computing resources, and post-processing through regression head outputs and segmentation head logits at predicted seal locations. Even with a simplified version of our ensemble model, using AI predictions as a guide dramatically boosted the precision and recall of two human experts, showing potential as a training device for novice seal annotators. Like human observers, the performance of our automated approach deteriorates with terrain ruggedness, highlighting the need for statistical treatment to draw global population estimates from AI output.<\/jats:p>","DOI":"10.3390\/rs14225655","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T02:07:48Z","timestamp":1668046068000},"page":"5655","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["SealNet 2.0: Human-Level Fully-Automated Pack-Ice Seal Detection in Very-High-Resolution Satellite Imagery with CNN Model Ensembles"],"prefix":"10.3390","volume":"14","author":[{"given":"Bento C.","family":"Gon\u00e7alves","sequence":"first","affiliation":[{"name":"Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY 11794-5245, USA"},{"name":"Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY 11794-3365, USA"}]},{"given":"Michael","family":"Wethington","sequence":"additional","affiliation":[{"name":"Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY 11794-5245, USA"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-9026-1612","authenticated-orcid":false,"given":"Heather J.","family":"Lynch","sequence":"additional","affiliation":[{"name":"Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY 11794-5245, USA"},{"name":"Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY 11794-3365, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.biocon.2012.02.002","article-title":"Responses of Antarctic pack-ice seals to environmental change and increasing krill fishing","volume":"149","author":"Forcada","year":"2012","journal-title":"Biol. Conserv."},{"key":"ref_2","first-page":"49","article-title":"A review of data on abundance, trends in abundance, habitat use and diet of ice-breeding seals in the Southern Ocean","volume":"19","author":"Southwell","year":"2012","journal-title":"CCAMLR Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1111\/j.1365-2664.2007.01399.x","article-title":"Taking account of dependent species in management of the Southern Ocean krill fishery: Estimating crabeater seal abundance off east Antarctica","volume":"45","author":"Southwell","year":"2008","journal-title":"J. Appl. Ecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4742","DOI":"10.1038\/s41467-019-12668-7","article-title":"The importance of Antarctic krill in biogeochemical cycles","volume":"10","author":"Cavan","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Trathan, P.N., and Hill, S.L. (2016). The importance of krill predation in the Southern Ocean. Biology and Ecology of Antarctic Krill, Springer.","DOI":"10.1007\/978-3-319-29279-3_9"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1016\/j.dsr.2008.12.007","article-title":"A re-appraisal of the total biomass and annual production of Antarctic krill","volume":"56","author":"Atkinson","year":"2009","journal-title":"Deep. Sea Res. Part I Oceanogr. Res. Pap."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3354\/meps12099","article-title":"Overwinter habitat selection by Antarctic krill under varying sea-ice conditions: Implications for top predators and fishery management","volume":"568","author":"Reiss","year":"2017","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"193","DOI":"10.5194\/essd-9-193-2017","article-title":"KRILLBASE: A circumpolar database of Antarctic krill and salp numerical densities, 1926\u20132016","volume":"9","author":"Atkinson","year":"2017","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_9","unstructured":"Holdgate, M. (1970). Antarctic Ecology, Academic Press."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111617","DOI":"10.1016\/j.rse.2019.111617","article-title":"SealNet: A fully-automated pack-ice seal detection pipeline for sub-meter satellite imagery","volume":"239","author":"Spitzbart","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_11","unstructured":"\u00d8ritsland, T. (1970). Biology and Population Dynamics of Antarctic Seals, Academic Press."},{"key":"ref_12","unstructured":"Marthan, N.B., and Brent, S.S. (2006). The International Antarctic Pack Ice Seals (APIS) Program. Multi-disciplinary research into the ecology and behavior of Antarctic pack ice seals. Summary Update. The Expert Group on Seals (EGS), Scientific Committee on Antarctic Research (SCAR)."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1017\/S0952836904005928","article-title":"Satellite-linked dive recorders provide insights into the reproductive strategies of crabeater seals (Lobodon carcinophagus)","volume":"264","author":"Southwell","year":"2004","journal-title":"J. Zool."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1121\/1.2736976","article-title":"Age-related differences in the acoustic characteristics of male leopard seals, Hydrurga leptonyx","volume":"122","author":"Rogers","year":"2007","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1007\/s00300-016-2029-4","article-title":"Distribution, density and abundance of Antarctic ice seals off Queen Maud Land and the eastern Weddell Sea","volume":"40","author":"Gurarie","year":"2017","journal-title":"Polar Biol."},{"key":"ref_16","first-page":"1015","article-title":"Job-related mortality of wildlife workers in the United States, 1937\u20132000","volume":"31","author":"Sasse","year":"2003","journal-title":"Wildl. Soc. Bull."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1111\/cobi.12367","article-title":"Applications of Very High-Resolution Imagery in the Study and Conservation of Large Predators in the Southern Ocean","volume":"28","author":"LaRue","year":"2014","journal-title":"Conserv. Biol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"McMahon, C.R., Howe, H., Van Den Hoff, J., Alderman, R., Brolsma, H., and Hindell, M.A. (2014). Satellites, the all-seeing eyes in the sky: Counting elephant seals from space. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0092613"},{"key":"ref_19","unstructured":"Matthews, C.J., Dispas, A., and Mosnier, A. (2022). Evaluating Satellite Imagery for Atlantic Walrus Odobenus Rosmarus Rosmarus Stock Assessment\u2014A Pilot Study."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Borowicz, A., Le, H., Humphries, G., Nehls, G., H\u00f6schle, C., Kosarev, V., and Lynch, H.J. (2019). Aerial-trained deep learning networks for surveying cetaceans from satellite imagery. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0212532"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40064-016-3583-5","article-title":"Citizen surveillance for environmental monitoring: Combining the efforts of citizen science and crowdsourcing in a quantitative data framework","volume":"5","author":"Welvaert","year":"2016","journal-title":"SpringerPlus"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/cobi.13223","article-title":"The potential for citizen science to produce reliable and useful information in ecology","volume":"33","author":"Brown","year":"2019","journal-title":"Conserv. Biol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Salas, L.A., LaRue, M., Nur, N., Ainley, D.G., Stammerjohn, S.E., Pennycook, J., Rotella, J., Paterson, J.T., Siniff, D., and Stamatiou, K. (2020). Reducing error and increasing reliability of wildlife counts from citizen science surveys: Counting Weddell Seals in the Ross Sea from satellite images. bioRxiv.","DOI":"10.1101\/2020.11.18.388157"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1002\/rse2.124","article-title":"Engaging \u2018the crowd\u2019in remote sensing to learn about habitat affinity of the Weddell seal in Antarctica","volume":"6","author":"LaRue","year":"2020","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"eabh3674","DOI":"10.1126\/sciadv.abh3674","article-title":"Insights from the first global population estimate of Weddell seals in Antarctica","volume":"7","author":"LaRue","year":"2021","journal-title":"Sci. Adv."},{"key":"ref_26","unstructured":"Gon\u00e7alves, B.C., Wethington, M., and Lynch, H.J. (2022). Roadmap to a fully-automated, pan-Antarctic pack-ice seal monitoring program, in preparation."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"ref_28","first-page":"1","article-title":"Computer vision technology in agricultural automation\u2014A review","volume":"7","author":"Tian","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1007\/s11831-020-09504-3","article-title":"Computer vision techniques in construction: A critical review","volume":"28","author":"Xu","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-020-00376-2","article-title":"Deep learning-enabled medical computer vision","volume":"4","author":"Esteva","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2014.03.009","article-title":"Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites","volume":"103","author":"Belward","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, B.C., and Lynch, H.J. (2021). Fine-Scale Sea Ice Segmentation for High-Resolution Satellite Imagery with Weakly-Supervised CNNs. Remote Sens., 13.","DOI":"10.3390\/rs13183562"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Boulze, H., Korosov, A., and Brajard, J. (2020). Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12132165"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Han, Y., Liu, Y., Hong, Z., Zhang, Y., Yang, S., and Wang, J. (2021). Sea ice image classification based on heterogeneous data fusion and deep learning. Remote Sens., 13.","DOI":"10.3390\/rs13040592"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1111\/1365-2656.12780","article-title":"A computer vision for animal ecology","volume":"87","author":"Weinstein","year":"2018","journal-title":"J. Anim. Ecol."},{"key":"ref_36","unstructured":"M Le, H., Goncalves, B., Samaras, D., and Lynch, H. (2019, January 16\u201317). Weakly labeling the antarctic: The penguin colony case. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Marcel, S., and Rodriguez, Y. (2010, January 25\u201329). Torchvision the Machine-Vision Package of Torch. Proceedings of the 18th ACM International Conference on Multimedia, Firenze, Italy.","DOI":"10.1145\/1873951.1874254"},{"key":"ref_40","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_41","unstructured":"Yakubovskiy, P. (2022, November 07). Segmentation Models Pytorch. Available online: https:\/\/2.zoppoz.workers.dev:443\/https\/github.com\/qubvel\/segmentation_models.pytorch."},{"key":"ref_42","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv."},{"key":"ref_43","unstructured":"Loshchilov, I., and Hutter, F. (2019, January 6\u20139). Decoupled Weight Decay Regularization. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_44","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32, Curran Associates, Inc."},{"key":"ref_45","unstructured":"Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., and Venkatesh, G. (May, January 30). Mixed Precision Training. Proceedings of the International Conference on Learning Representations, Vancouver, BC, CA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Brown, S.T., Buitrago, P., Hanna, E., Sanielevici, S., Scibek, R., and Nystrom, N.A. (2021). Bridges-2: A Platform for Rapidly-Evolving and Data Intensive Research. Practice and Experience in Advanced Research Computing, Association for Computing Machinery.","DOI":"10.1145\/3437359.3465593"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A.A. (2020). Albumentations: Fast and flexible image augmentations. Information, 11.","DOI":"10.3390\/info11020125"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1214\/aoms\/1177703732","article-title":"Robust Estimation of a Location Parameter","volume":"35","author":"Huber","year":"1964","journal-title":"Ann. Math. Stat."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R.B., He, K., and Doll\u00e1r, P. (2017). Focal Loss for Dense Object Detection. arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecologic association between species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_52","first-page":"6105","article-title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","volume":"Volume 97","author":"Chaudhuri","year":"2019","journal-title":"Proceedings of the 36th International Conference on Machine Learning"},{"key":"ref_53","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_54","unstructured":"Shapley, L.S. (1952). A Value for N-Person Games, RAND Corporation."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and Variable Selection via the Elastic Net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"ref_56","unstructured":"Dorogush, A.V., Ershov, V., and Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_58","unstructured":"Pereira, F., Burges, C., Bottou, L., and Weinberger, K. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 4\u20138). Optuna: A Next-Generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"105015","DOI":"10.1016\/j.envsoft.2021.105015","article-title":"Quantarctica, an integrated mapping environment for Antarctica, the Southern Ocean, and sub-Antarctic islands","volume":"140","author":"Matsuoka","year":"2021","journal-title":"Environ. Model. Softw."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Xue, Y., Wang, T., and Skidmore, A.K. (2017). Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9090878"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2018.06.028","article-title":"Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning","volume":"216","author":"Kellenberger","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1613\/jair.1.11345","article-title":"Human-in-the-loop artificial intelligence","volume":"64","author":"Zanzotto","year":"2019","journal-title":"J. Artif. Intell. Res."},{"key":"ref_64","unstructured":"Branson, S., Wah, C., Schroff, F., Babenko, B., Welinder, P., Perona, P., and Belongie, S. Visual recognition with humans in the loop. Proceedings of the European Conference on Computer Vision."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Paladini, E., Vantaggiato, E., Bougourzi, F., Distante, C., Hadid, A., and Taleb-Ahmed, A. (2021). Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification. J. Imaging, 7.","DOI":"10.3390\/jimaging7030051"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"106776","DOI":"10.1016\/j.cmpb.2022.106776","article-title":"A fuzzy distance-based ensemble of deep models for cervical cancer detection","volume":"219","author":"Pramanik","year":"2022","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/2072-4292\/14\/22\/5655\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:13:09Z","timestamp":1760145189000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/2072-4292\/14\/22\/5655"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,9]]},"references-count":66,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225655"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/rs14225655","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,9]]}}}