Underwater images collected by a Kite surf in Le-Morne-Little-Reef-Finkel-Chameau, Mauritius - 2019-06-02
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| Autores principales: | , , , |
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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Zenodo
2025
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| _version_ | 1866902009190285312 |
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| author | Julien Barde Matteo Contini Victor Illien Sylvain Bonhommeau |
| author_facet | Julien Barde Matteo Contini Victor Illien Sylvain Bonhommeau |
| contents | <i>This dataset was collected by a Kite surf in Le-Morne-Little-Reef-Finkel-Chameau, Mauritius - 2019-06-02.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/juldebar/Deep_mapping" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228534" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17558202 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Underwater images collected by a Kite surf in Le-Morne-Little-Reef-Finkel-Chameau, Mauritius - 2019-06-02 Julien Barde Matteo Contini Victor Illien Sylvain Bonhommeau Artificial Intelligence Bathymetry Citizen Sciences Coastal Ecosystems Computer Vision Coral Reef Coral Reef Habitat Deep Learning Ecology Environmental Monitoring GeoAI Global Coral Reef Monitoring Network Indian Ocean KITE Kite surf Machine Learning Mapping Marine Biodiversity Marine Conservation Mauritius Open Science Plancha Reef Ecosystem Remote Sensing Western Indian Ocean <i>This dataset was collected by a Kite surf in Le-Morne-Little-Reef-Finkel-Chameau, Mauritius - 2019-06-02.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/juldebar/Deep_mapping" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228534" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br> |
| title | Underwater images collected by a Kite surf in Le-Morne-Little-Reef-Finkel-Chameau, Mauritius - 2019-06-02 |
| topic | Artificial Intelligence Bathymetry Citizen Sciences Coastal Ecosystems Computer Vision Coral Reef Coral Reef Habitat Deep Learning Ecology Environmental Monitoring GeoAI Global Coral Reef Monitoring Network Indian Ocean KITE Kite surf Machine Learning Mapping Marine Biodiversity Marine Conservation Mauritius Open Science Plancha Reef Ecosystem Remote Sensing Western Indian Ocean |
| url | https://doi.org/10.5281/zenodo.17558202 |