DOI: 10.5281/zenodo.15809569
title: Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2025-06-16

description: This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2025-06-16. 

            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.

            
                
                
Image acquisition
                This session has 21.16 GB of MP4 files, which were trimmed into 6759 frames (at 2997/1000 fps).  
                The frames are  georeferenced. 
                97.54% of these extracted images are useful and 2.46% 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: 56.16 %, Q2: 12.83 %, Q5: 31.01 % 

        
                    
                

                
            
Photogrammetry

            OpenDroneMap software was used to create an orthophoto from the raw images. 
            Here is the list of parameters different from the default values for the orthophoto generation. 
            For more details, you can read the log.json file or the 000_photogrammatry_report.pdf report. 

            
                {'auto_boundary': True, 'cog': True, 'fast_orthophoto': True, 'feature_quality': 'ultra', 'gps_accuracy': 0.1, 'max_concurrency': 50, 'optimize_disk_space': True, 'orthophoto_resolution': 0.1, 'rolling_shutter': True, 'skip_3dmodel': True}
            
        
            

            
 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