Flying Robots Teach Floating Robots—A Machine Learning Approach for Marine Habitat Mapping Based on Combined Datasets
Unmanned aerial and autonomous surface vehicles (UAVs and ASVs, respectively) are two emerging technologies for the mapping of coastal and marine environments. Using UAV photogrammetry, the sea-bottom composition can be resolved with very high fidelity in shallow waters. At greater depths, acoustic...
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MDPI AG
2025-03-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/3/611 |
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| author | Zacharias Kapelonis Georgios Chatzigeorgiou Manolis Ntoumas Panos Grigoriou Manos Pettas Spyros Michelinakis Ricardo Correia Catarina Rasquilha Lemos Luis Menezes Pinheiro Caio Lomba João Fortuna Rui Loureiro André Santos Eva Chatzinikolaou |
| author_facet | Zacharias Kapelonis Georgios Chatzigeorgiou Manolis Ntoumas Panos Grigoriou Manos Pettas Spyros Michelinakis Ricardo Correia Catarina Rasquilha Lemos Luis Menezes Pinheiro Caio Lomba João Fortuna Rui Loureiro André Santos Eva Chatzinikolaou |
| author_sort | Zacharias Kapelonis |
| collection | DOAJ |
| description | Unmanned aerial and autonomous surface vehicles (UAVs and ASVs, respectively) are two emerging technologies for the mapping of coastal and marine environments. Using UAV photogrammetry, the sea-bottom composition can be resolved with very high fidelity in shallow waters. At greater depths, acoustic methodologies have far better propagation properties compared to optics; therefore, ASVs equipped with multibeam echosounders (MBES) are better-suited for mapping applications in deeper waters. In this work, a sea-bottom classification methodology is presented for mapping the protected habitat of Mediterranean seagrass <i>Posidonia oceanica</i> (habitat code 1120) in a coastal subregion of Heraklion (Crete, Greece). The methodology implements a machine learning scheme, where knowledge obtained from UAV imagery is embedded (through training) into a classifier that utilizes acoustic backscatter intensity and features derived from the MBES data provided by an ASV. Accuracy and precision scores of greater than 85% compared with visual census ground-truth data for both optical and acoustic classifiers indicate that this hybrid mapping approach is promising to mitigate the depth-induced bias in UAV-only models. The latter is especially interesting in cases where the studied habitat boundaries extend beyond depths that can be studied via aerial devices’ optics, as is the case with <i>P. oceanica</i> meadows. |
| format | Article |
| id | doaj-art-c7346bfa08884abc8ae68fd6852bc670 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-c7346bfa08884abc8ae68fd6852bc6702025-08-20T01:49:04ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-03-0113361110.3390/jmse13030611Flying Robots Teach Floating Robots—A Machine Learning Approach for Marine Habitat Mapping Based on Combined DatasetsZacharias Kapelonis0Georgios Chatzigeorgiou1Manolis Ntoumas2Panos Grigoriou3Manos Pettas4Spyros Michelinakis5Ricardo Correia6Catarina Rasquilha Lemos7Luis Menezes Pinheiro8Caio Lomba9João Fortuna10Rui Loureiro11André Santos12Eva Chatzinikolaou13Institute of Marine Biological Resources and Inland Waters (IMBRIW), Hellenic Centre for Marine Research (HCMR), 16452 Athens, GreeceInstitute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), 71500 Heraklion, GreeceInstitute of Oceanography (IO), Hellenic Centre for Marine Research (HCMR), 71500 Heraklion, GreeceCretaquarium, Hellenic Centre for Marine Research (HCMR), 71500 Heraklion, GreeceInstitute of Oceanography (IO), Hellenic Centre for Marine Research (HCMR), 71500 Heraklion, GreeceInstitute of Oceanography (IO), Hellenic Centre for Marine Research (HCMR), 71500 Heraklion, GreeceCESAM—Centre for Environmental and Marine Studies, Geosciences Department, University of Aveiro (UA), Campus Universitário de Santiago, 3810-193 Aveiro, PortugalCentre of Engineering and Product Development (CEiiA), 4450-017 Matosinhos, PortugalCESAM—Centre for Environmental and Marine Studies, Geosciences Department, University of Aveiro (UA), Campus Universitário de Santiago, 3810-193 Aveiro, PortugalCentre of Engineering and Product Development (CEiiA), 4450-017 Matosinhos, PortugalCentre of Engineering and Product Development (CEiiA), 4450-017 Matosinhos, PortugalCentre of Engineering and Product Development (CEiiA), 4450-017 Matosinhos, PortugalCentre of Engineering and Product Development (CEiiA), 4450-017 Matosinhos, PortugalInstitute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), 71500 Heraklion, GreeceUnmanned aerial and autonomous surface vehicles (UAVs and ASVs, respectively) are two emerging technologies for the mapping of coastal and marine environments. Using UAV photogrammetry, the sea-bottom composition can be resolved with very high fidelity in shallow waters. At greater depths, acoustic methodologies have far better propagation properties compared to optics; therefore, ASVs equipped with multibeam echosounders (MBES) are better-suited for mapping applications in deeper waters. In this work, a sea-bottom classification methodology is presented for mapping the protected habitat of Mediterranean seagrass <i>Posidonia oceanica</i> (habitat code 1120) in a coastal subregion of Heraklion (Crete, Greece). The methodology implements a machine learning scheme, where knowledge obtained from UAV imagery is embedded (through training) into a classifier that utilizes acoustic backscatter intensity and features derived from the MBES data provided by an ASV. Accuracy and precision scores of greater than 85% compared with visual census ground-truth data for both optical and acoustic classifiers indicate that this hybrid mapping approach is promising to mitigate the depth-induced bias in UAV-only models. The latter is especially interesting in cases where the studied habitat boundaries extend beyond depths that can be studied via aerial devices’ optics, as is the case with <i>P. oceanica</i> meadows.https://www.mdpi.com/2077-1312/13/3/611mobile platformsunmanned and autonomous vehiclesmultibeam echosoundermarine habitat mappingaerial mappingtransfer learning |
| spellingShingle | Zacharias Kapelonis Georgios Chatzigeorgiou Manolis Ntoumas Panos Grigoriou Manos Pettas Spyros Michelinakis Ricardo Correia Catarina Rasquilha Lemos Luis Menezes Pinheiro Caio Lomba João Fortuna Rui Loureiro André Santos Eva Chatzinikolaou Flying Robots Teach Floating Robots—A Machine Learning Approach for Marine Habitat Mapping Based on Combined Datasets Journal of Marine Science and Engineering mobile platforms unmanned and autonomous vehicles multibeam echosounder marine habitat mapping aerial mapping transfer learning |
| title | Flying Robots Teach Floating Robots—A Machine Learning Approach for Marine Habitat Mapping Based on Combined Datasets |
| title_full | Flying Robots Teach Floating Robots—A Machine Learning Approach for Marine Habitat Mapping Based on Combined Datasets |
| title_fullStr | Flying Robots Teach Floating Robots—A Machine Learning Approach for Marine Habitat Mapping Based on Combined Datasets |
| title_full_unstemmed | Flying Robots Teach Floating Robots—A Machine Learning Approach for Marine Habitat Mapping Based on Combined Datasets |
| title_short | Flying Robots Teach Floating Robots—A Machine Learning Approach for Marine Habitat Mapping Based on Combined Datasets |
| title_sort | flying robots teach floating robots a machine learning approach for marine habitat mapping based on combined datasets |
| topic | mobile platforms unmanned and autonomous vehicles multibeam echosounder marine habitat mapping aerial mapping transfer learning |
| url | https://www.mdpi.com/2077-1312/13/3/611 |
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