DOI: 10.5281/zenodo.12759751
title: Underwater images collected by an Autonomous Surface Vehicle in Pointe-Des-Aigrettes, Réunion - 2024-07-08

description: This dataset was collected by an Autonomous Surface Vehicle in Pointe-Des-Aigrettes, Réunion - 2024-07-08. 

            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.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            
                
                                       
                
            
 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: 91.26 %, Q2: 2.39 %, Q5: 6.35 % 

        
                    
                
                
 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 3.0 m and 50.0 m deep. 
                The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.
                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 0.733 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 general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
description: The Plancha project is co-financed by the Prefecture of Reunion as part of a 2019-2022 convergence and transformation contract, measure 3.3.1.1