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DC Field | Value | Language |
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dc.contributor.author | Gauci, Adam | - |
dc.contributor.author | Deidun, Alan | - |
dc.contributor.author | Abela, John | - |
dc.contributor.author | Zarb Adami, Kristian | - |
dc.date.accessioned | 2018-01-30T17:35:53Z | - |
dc.date.available | 2018-01-30T17:35:53Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Gauci, A., Deidun, A., Abela, J., & Adami, K. Z. (2016). Machine learning for benthic sand and maerl classification and coverage estimation in coastal areas around the Maltese Islands. Journal of Applied Research and Technology, 14(5), 338-344. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/26194 | - |
dc.description.abstract | Analysis of the seabed composition over a large spatial scale is an interesting yet very challenging task. Apart from the field work involved, hours of video footage captured by cameras mounted on Remote Operated Vehicles (ROVs) have to be reviewed by an expert in order to classify the seabed topology and to identify potential anthropogenic impacts on sensitive benthic assemblages. Apart from being time consuming, such work is highly subjective and through visual inspection alone, a quantitative analysis is highly unlikely to be made. This study investigates the applicability of various Machine Learning techniques for the automatic classification of the seabed into maerl and sand regions from recorded ROV footage. ROV data collected from depths ranging between 50 m and 140 m and at 9.5 km from the northeast coastline of the Maltese Islands, is processed. Through the application of the presented technique, 5.23 GB of data corresponding to 2 h and 24 min of footage which was collected during June 2013, was initially cleaned and classified. An estimate for the percentage cover of the two benthic habitats (sandy seabed and maerl) was also computed by using artifacts encountered during the ROV survey and of known dimensions as a reference. Unlike other automatic seabed mapping techniques, the presented prototype processes video footage captured by a down-facing camera and not through acoustic backscatter. Image data is easier and much cheaper to capture. Promising results that indicate a very good degree of agreement between the true and predicted habitat type distribution values, were obtained. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Universidad Nacional Autonoma de Mexico. Centro de Ciencias Aplicadas y Desarrollo Tecnologico | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Machine learning -- Development | en_GB |
dc.subject | Image processing | en_GB |
dc.subject | Ocean bottom ecology -- Malta | en_GB |
dc.subject | Decision trees | en_GB |
dc.title | Machine learning for benthic sand and maerl classification and coverage estimation in coastal areas around the Maltese Islands | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1016/j.jart.2016.08.003 | - |
dc.publication.title | Journal of Applied Research and Technology | en_GB |
Appears in Collections: | Scholarly Works - FacSciGeo Scholarly Works - FacSciPhy Scholarly Works - InsSSA |
Files in This Item:
File | Description | Size | Format | |
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Machine_Learning_for_benthic_sand_and_maerl_classi.pdf | 2.43 MB | Adobe PDF | View/Open |
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