Underwater images collected by an Autonomous Surface Vehicle in Aldabra-Arm06, Seychelles - 2022-10-22
This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Arm06, Seychelles - 2022-10-22. 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...
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Zusammenfassung: | This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Arm06, Seychelles - 2022-10-22.
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.
Image acquisition
This session has 21.0 GB of MP4 files, which were trimmed into 7993 frames (at 2997/1000 fps).
The frames are georeferenced.
51.06% of these extracted images are useful and 48.94% are useless, according to predictions made by Jacques model.
Multilabel predictions have been made on useful frames using DinoVd'eau model.
GPS information:
The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy.
Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames.
Device GPS : Emlid Reach M2
Quality of our data - Q1: 75.76 %, Q2: 24.12 %, Q5: 0.12 %
Bathymetry
The data are collected using a single-beam echosounder ETC 400.
We only keep the values which have a GPS correction in Q1.
We keep the points that are the waypoints.
We keep the raw data where depth was estimated between 0.2 m and 30.0 m deep.
The data are first referenced against the WGS84 ellipsoid.
At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles.
The size of the grid cells is 1.259 m.
The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot.
Generic folder structure
YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number
├── DCIM : folder to store videos and photos depending on the media collected.
├── 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.
│ ├── BASE : files coming from rtk station or any static positioning instrument.
│ └── DEVICE : files coming from the device.
├── METADATA : folder with |
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DOI: | 10.5281/zenodo.11145841 |