GeoAI for Marine Ecosystem Monitoring: a Complete Workflow to Generate Maps from AI Model Predictions
Type | Article | ||||||||
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Date | 2023-06-22 | ||||||||
Language | English | ||||||||
Author(s) | Talpaert Daudon Justine1, Contini Matteo2, Urbina-Barreto Isabel3, Elliott Brianna4, Guilhaumon François3, Joly Alexis5, Bonhommeau Sylvain2, Barde Julien6 | ||||||||
Affiliation(s) | 1 : UMR Marbec, IRD, La Reunion, France 2 : Ifremer DOI, La Reunion, France 3 : UMR Entropie (Future Maore Reefs), IRD, La Reunion, France 4 : Duke University, Duke Marine Lab, Beaufort, NC, USA 5 : INRIA Zenith, Montpellier, France 6 : UMR Marbec, IRD, France |
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Meeting | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVIII-4/W7-2023 FOSS4G (Free and Open Source Software for Geospatial) 2023 – Academic Track, 26 June–2 July 2023, Prizren, Kosovo | ||||||||
Source | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2194-9034) (Copernicus GmbH), 2023-06-22 , Vol. XLVIII-4/W7-2023 , P. 223-230 | ||||||||
DOI | 10.5194/isprs-archives-XLVIII-4-W7-2023-223-2023 | ||||||||
Keyword(s) | GeoAI, computer vision, deep learning, marine ecology, object detection, segmentation, geospatial, photogrammetry | ||||||||
Abstract | Mapping and monitoring marine ecosystems imply several challenges for data collection and processing: water depth, restricted access to locations, instrumentation costs or weather constraints for sampling, among others. Nowadays, Artificial Intelligence (AI) and Geographic Information System (GIS) open source software can be combined in new kinds of workflows, to annotate and predict objects directly on georeferenced raster data (e.g. orthomosaics). Here, we describe and share the code of a generic method to train a deep learning model with spatial annotations and use it to directly generate model predictions as spatial features. This workflow has been tested and validated in three use cases related to marine ecosystem monitoring at different geographic scales: (i) segmentation of corals on orthomosaics made of underwater images to automate coral reef habitats mapping, (ii) detection and classification of fishing vessels on remote sensing satellite imagery to estimate a proxy of fishing effort (iii) segmentation of marine species and habitats on underwater images with a simple geolocation. Models have been successfully trained and the models predictions are displayed with maps in the three use cases. |
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