Underwater images collected by an Underwater Vision Census in Toliara, Madagascar - 2021-12-14
Fuente:
Zenodo
Salvato in:
| Autori principali: | , , , , , , , , , , , |
|---|---|
| Natura: | Recurso digital |
| Lingua: | inglese |
| Pubblicazione: |
Zenodo
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866902049422049280 |
|---|---|
| author | Aina Le Don Nomenisoa Yves Amoros Mitondrasoa Gildas Todinanahary Hubert Zafimampiravo Edwin Israel John Bunyan Toky Razakarisoa Tsiresimiary Mandimbilaza Michel Ratsizafy Saverio Raseta Henitsoa Jaonalison Jamal Mahafina Igor Eeckhaut |
| author_facet | Aina Le Don Nomenisoa Yves Amoros Mitondrasoa Gildas Todinanahary Hubert Zafimampiravo Edwin Israel John Bunyan Toky Razakarisoa Tsiresimiary Mandimbilaza Michel Ratsizafy Saverio Raseta Henitsoa Jaonalison Jamal Mahafina Igor Eeckhaut |
| contents | <i>This dataset was collected by an Underwater Vision Census in Toliara, Madagascar - 2021-12-14.</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> <h2>Survey information</h2> <ul> <li> <strong> Camera</strong>: Not enough information in metadata to get camera information.</li> <li> <strong> Number of images</strong>: 157 </li> <li> <strong> Total size</strong>: 0.01 Gb</li> <li> <strong> Mission start</strong>: 2021:12:14 07:44:13 </li> <li> <strong> Mission end</strong>: 2021:12:14 08:04:01</li> <li> <strong> Mission duration</strong>: 0h 19min 48sec</li> <li> <strong> Total distance</strong>: 448 m</li> </ul> <h2> GPS information: </h2> Surveys were conducted during low spring tides on reef areas less than 20 meters deep. <br> A GPS device, kept in a floating waterproof bag at the surface, recorded a position every 2 seconds while following the diver's path. <br> One diver took a benthic photo every 5 meters using a compass for direction, while a second diver guided the GPS bag from the surface. <br> Image positions were interpolated with GPS data through time synchronization, using the timestamps embedded in the image metadata. <br> Details of the acquisition method can be found in this <a href="https://doi.org/10.4314/wiojms.v23i2.4" target="_blank">paper</a>. <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> │ ├── CPCE_ANNOTATION : All cpc files annotations made with the CPCe software. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> <h2> Software </h2> <br> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/cpce-workflow" target="_blank">worflow</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_15188839 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Underwater images collected by an Underwater Vision Census in Toliara, Madagascar - 2021-12-14 Aina Le Don Nomenisoa Yves Amoros Mitondrasoa Gildas Todinanahary Hubert Zafimampiravo Edwin Israel John Bunyan Toky Razakarisoa Tsiresimiary Mandimbilaza Michel Ratsizafy Saverio Raseta Henitsoa Jaonalison Jamal Mahafina Igor Eeckhaut Artificial Intelligence Computer Vision Coral Reef Coral Reef Habitat Deep Learning Ecology GeoAI Global Coral Reef Monitoring Network Habitat Mapping Indian Ocean Machine Learning Madagascar Mapping Reef Ecosystem Remote Sensing UVC Underwater Vision Census <i>This dataset was collected by an Underwater Vision Census in Toliara, Madagascar - 2021-12-14.</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> <h2>Survey information</h2> <ul> <li> <strong> Camera</strong>: Not enough information in metadata to get camera information.</li> <li> <strong> Number of images</strong>: 157 </li> <li> <strong> Total size</strong>: 0.01 Gb</li> <li> <strong> Mission start</strong>: 2021:12:14 07:44:13 </li> <li> <strong> Mission end</strong>: 2021:12:14 08:04:01</li> <li> <strong> Mission duration</strong>: 0h 19min 48sec</li> <li> <strong> Total distance</strong>: 448 m</li> </ul> <h2> GPS information: </h2> Surveys were conducted during low spring tides on reef areas less than 20 meters deep. <br> A GPS device, kept in a floating waterproof bag at the surface, recorded a position every 2 seconds while following the diver's path. <br> One diver took a benthic photo every 5 meters using a compass for direction, while a second diver guided the GPS bag from the surface. <br> Image positions were interpolated with GPS data through time synchronization, using the timestamps embedded in the image metadata. <br> Details of the acquisition method can be found in this <a href="https://doi.org/10.4314/wiojms.v23i2.4" target="_blank">paper</a>. <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> │ ├── CPCE_ANNOTATION : All cpc files annotations made with the CPCe software. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> <h2> Software </h2> <br> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/cpce-workflow" target="_blank">worflow</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 an Underwater Vision Census in Toliara, Madagascar - 2021-12-14 |
| topic | Artificial Intelligence Computer Vision Coral Reef Coral Reef Habitat Deep Learning Ecology GeoAI Global Coral Reef Monitoring Network Habitat Mapping Indian Ocean Machine Learning Madagascar Mapping Reef Ecosystem Remote Sensing UVC Underwater Vision Census |
| url | https://doi.org/10.5281/zenodo.15188839 |