Aerial images collected by an Unmanned Aerial Vehicle in Tessier, Réunion - 2024-04-04
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2025
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| _version_ | 1866901869066977280 |
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| author | Matteo Contini Sylvain Bonhommeau Serge Bernard Julien Barde |
| author_facet | Matteo Contini Sylvain Bonhommeau Serge Bernard Julien Barde |
| contents | <i>This dataset was collected by an Unmanned Aerial Vehicle in Tessier, Réunion - 2024-04-04.</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>Survey information</h2> <ul> <li> <strong> Camera</strong>: Hasselblad L1D-20c</li> <li> <strong> Number of images</strong>: 90 </li> <li> <strong> Total size</strong>: 0.9 Gb</li> <li> <strong> Flight start</strong>: 2024:04:04 05:37:52 </li> <li> <strong> Flight end</strong>: 2024:04:04 05:41:14</li> <li> <strong> Flight duration</strong>: 0h 3min 22sec</li> <li> <strong> Median height</strong>: 59.9 m</li> <li> <strong> Area covered</strong>: 0.82 a</li> </ul> <h2>Photogrammetry</h2> OpenDroneMap software was used to create an orthophoto from the raw images. <br> Here is the list of parameters different from the default values for the orthophoto generation. <br> For more details, you can read the log.json file or the 000_photogrammatry_report.pdf report. <br><br> <code> {'auto_boundary': True, 'cog': True, 'fast_orthophoto': True, 'feature_quality': 'ultra', 'max_concurrency': 128, 'optimize_disk_space': True, 'orthophoto_resolution': 1.0, 'rolling_shutter': True, 'skip_3dmodel': True, 'use_exif': True} </code> <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/SeatizenDOI/drone-workflow" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/drone-inference" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://doi.org/10.5281/zenodo.15455743" 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_15831003 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Aerial images collected by an Unmanned Aerial Vehicle in Tessier, Réunion - 2024-04-04 Matteo Contini Sylvain Bonhommeau Serge Bernard Julien Barde Artificial Intelligence Citizen Sciences Computer Vision Coral Reef Coral Reef Habitat Deep Learning Drone Ecology GeoAI Global Coral Reef Monitoring Network Indian Ocean Machine Learning Mapping Plancha Reef Ecosystem Remote Sensing Réunion UAV Unmanned Aerial Vehicle <i>This dataset was collected by an Unmanned Aerial Vehicle in Tessier, Réunion - 2024-04-04.</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>Survey information</h2> <ul> <li> <strong> Camera</strong>: Hasselblad L1D-20c</li> <li> <strong> Number of images</strong>: 90 </li> <li> <strong> Total size</strong>: 0.9 Gb</li> <li> <strong> Flight start</strong>: 2024:04:04 05:37:52 </li> <li> <strong> Flight end</strong>: 2024:04:04 05:41:14</li> <li> <strong> Flight duration</strong>: 0h 3min 22sec</li> <li> <strong> Median height</strong>: 59.9 m</li> <li> <strong> Area covered</strong>: 0.82 a</li> </ul> <h2>Photogrammetry</h2> OpenDroneMap software was used to create an orthophoto from the raw images. <br> Here is the list of parameters different from the default values for the orthophoto generation. <br> For more details, you can read the log.json file or the 000_photogrammatry_report.pdf report. <br><br> <code> {'auto_boundary': True, 'cog': True, 'fast_orthophoto': True, 'feature_quality': 'ultra', 'max_concurrency': 128, 'optimize_disk_space': True, 'orthophoto_resolution': 1.0, 'rolling_shutter': True, 'skip_3dmodel': True, 'use_exif': True} </code> <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/SeatizenDOI/drone-workflow" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/drone-inference" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://doi.org/10.5281/zenodo.15455743" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br> |
| title | Aerial images collected by an Unmanned Aerial Vehicle in Tessier, Réunion - 2024-04-04 |
| topic | Artificial Intelligence Citizen Sciences Computer Vision Coral Reef Coral Reef Habitat Deep Learning Drone Ecology GeoAI Global Coral Reef Monitoring Network Indian Ocean Machine Learning Mapping Plancha Reef Ecosystem Remote Sensing Réunion UAV Unmanned Aerial Vehicle |
| url | https://doi.org/10.5281/zenodo.15831003 |