Find research datasets worth reusing

Underwater images collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-03

This dataset was collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-03. 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 <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. <h2>Image acquisition</h2> This session has 15.07 GB of MP4 files, which were trimmed into 5136 frames (at 2997/1000 fps). The frames are georeferenced. 59.54% of these extracted images are useful and 40.46% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> 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: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. 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>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by an Autonomous Surface Vehicle in Ifaty, Madagascar - 2023-05-06

This dataset was collected by an Autonomous Surface Vehicle in Ifaty, Madagascar - 2023-05-06. 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 <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. <h2>Image acquisition</h2> This session has 17.33 GB of MP4 files, which were trimmed into 4251 frames (at 2997/1000 fps). The frames are georeferenced. 100.0% of these extracted images are useful and 0.0% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> Base : No Base Device GPS : Emlid Reach M2 Quality of our data - Q1: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 50.0 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 0.422 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. 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>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by an Autonomous Surface Vehicle in Anakao, Madagascar - 2023-05-01

This dataset was collected by an Autonomous Surface Vehicle in Anakao, Madagascar - 2023-05-01. 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 <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. <h2>Image acquisition</h2> This session has 13.58 GB of MP4 files, which were trimmed into 2767 frames (at 2997/1000 fps). The frames are georeferenced. 80.88% of these extracted images are useful and 19.12% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> 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: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 50.0 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 0.335 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. 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>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by an Autonomous Surface Vehicle in Ifaty, Madagascar - 2023-05-07

This dataset was collected by an Autonomous Surface Vehicle in Ifaty, Madagascar - 2023-05-07. 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 <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. <h2>Image acquisition</h2> This session has 25.86 GB of MP4 files, which were trimmed into 5425 frames (at 2997/1000 fps). The frames are georeferenced. 99.17% of these extracted images are useful and 0.83% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> Base : No Base Device GPS : Emlid Reach M2 Quality of our data - Q1: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 20.0 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 0.313 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. 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>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-04

This dataset was collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-04. 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 <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. <h2> GPS information: </h2> 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: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 35.0 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 0.492 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. 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>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-05

This dataset was collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-05. 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 <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. <h2>Image acquisition</h2> This session has 34.7 GB of MP4 files, which were trimmed into 9193 frames (at 2997/1000 fps). The frames are georeferenced. 98.34% of these extracted images are useful and 1.66% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> 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: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 20.0 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 0.463 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. 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>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by Scuba diving in Nosy-Ve, Madagascar - 2023-04-30

This dataset was collected by Scuba diving in Nosy-Ve, Madagascar - 2023-04-30. 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 <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. <h2>Image acquisition</h2> This session has 2.49 GB of JPG files, which were trimmed into 460 frames (at 4 fps). The frames are not georeferenced. 100.0% of these extracted images are useful and 0.0% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> No GPS. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. 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>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-03

This dataset was collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-03. 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 <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. <h2>Image acquisition</h2> This session has 16.67 GB of MP4 files, which were trimmed into 8820 frames (at 2997/1000 fps). The frames are georeferenced. 81.93% of these extracted images are useful and 18.07% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> 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: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. 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>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

Underwater images collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-05

This dataset was collected by an Autonomous Surface Vehicle in Sarodrano, Madagascar - 2023-05-05. 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 <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. <h2>Image acquisition</h2> This session has 20.22 GB of MP4 files, which were trimmed into 5846 frames (at 2997/1000 fps). The frames are georeferenced. 87.7% of these extracted images are useful and 12.3% are useless, according to predictions made by <a href="jacques-v0.1.0_model-20240513_v20.0" target="_blank">Jacques model</a>. Multilabel predictions have been made on useful frames using <a href="https://huggingface.co/lombardata/DinoVdeau-large-2024_04_03-with_data_aug_batch-size32_epochs150_freeze" target="_blank">DinoVd'eau</a> model. <h2> GPS information: </h2> 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: 0.0 %, Q2: 0.0 %, Q5: 100.0 % <h2> Bathymetry </h2> The data are collected using a single-beam echosounder <a href="https://www.echologger.com/products/single-frequency-echosounder-deep" target="_blank">ETC 400</a>. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 35.0 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 0.352 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. <h2> Generic folder structure </h2> 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.). <h2> Software </h2> All the raw data was processed using our <a href="https://doi.org/10.5281/zenodo.15853010" target="_blank">worflow</a>. All predictions were generated by our <a href="https://doi.org/10.5281/zenodo.15228535" target="_blank">inference pipeline</a>. 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>. Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>!

opencc-by-4.0Nov 2024View details →

K1702 - Kura Clover (Trifolium ambiguum) USDA Accession Image Dataset

Images This dataset consists of 1135 images of Kura clover (Trifolium ambiguum) USDA accessions grown at The Land Institute in Salina, Kansas over the 2017 growing season. Each image contains a single Kura clover plant framed by a 1/2" PVC sampling quadrat with internal dimensions of 16"x16" (internal area of 0.165 m2). Kura clover plots were hand weeded to remove all other vegetation except Kura clover. Some images may contain dead clover accessions that are either brown and dried up, or missing entirely. The images were acquired with a Canon EOS Rebel T6 DSLR camera under the following settings: <ul> <li>ISO: 200</li> <li>Exposure: Auto</li> <li>Focal Length: Variable (33-40mm)</li> <li>Format: JPEG</li> <li>Size: 5184x3456</li> <li>Metering Mode: Multi-segment</li> </ul> Images were acquired on two different dates: 2017-06-08 and 2017-07-03 and were named using the following convention "<IMG_ID>_<yyyymmdd>.jpg". All image can be found in processed/images folder.  No image preprocessing was performed. Annotations The annotations consist of segmentation masks and bounding boxes. Each segmentation mask is saved as a png image and named using the convention "IMG_ID>_<yyyymmdd>.png".  The segmentation class labels ('segmentation_class_map.json') are as follows: <ul> <li>0: 'soil' background class containing all soil and non-target materials</li> <li>1: 'quadrat'</li> <li>2: 'clover'</li> </ul> We drew bounding boxes for the quadrat, each quadrat corner, and the entire clover plant. The class labels ('obj_det_class_map.json') are as follows: <ul> <li>1: 'clover'</li> <li>2: 'quadrat'</li> <li>3: 'quadrat_corner'</li> </ul> Bounding boxes are in (xmin, ymin, xmax, ymax) format and can be found in 'bboxes.csv'. All images are annotated using Labelbox software. Masks were generated by point prompts using Meta's Segment Anything model (SAM). The point prompts used to generate the masks can be found in 'SAM_points.csv' Additionally, some kura clover plants died or are not present in the plots where they were planted. We included the file 'plant_status.csv' to indicate which images include a living plant or a dead one. Train/Val/Test Split All 1035 images were randomly split with an 80/20 split on 1000 of the images (n=880, n=220) with the final 35 images reserved for the test holdout set. The file 'data_split.csv' holds the split class for each image. This dataset is released under a Creative Commons Attribution 4.0 International license which allows redistribution and re-use of the data herein as long as all authors are appropriately credited.

opencc-by-4.0Jan 2024View details →

LifeWatch observatory data: phytoplankton annotated trainingset by FlowCam imaging in the Belgian Part of the North Sea

<h1>Training dataset </h1> The images were collected in the framework of the Belgian Lifewatch Research Infrastructure. During multidisciplinary campaigns, a number of fixed stations in the Belgian Part of the North Sea (BPNS) are visited on a monthly (onshore stations) or seasonal (offshore stations) basis. Samples are taken using a 55µm mesh size Apstein net and fixed in Lugol's iodine solution. In the lab, the samples are processed using a VS-4 FlowCAM model at 4X magnification targeting a particle size range of 55-300µm. The identification of the image data is done with the use of a CNN and followed by a manual validation step. Since May 2017, this dataset has provided micro- and phytoplankton observations, mainly covering diatoms, dinoflagellates and cilliates, for the Belgian Part of the North Sea (BPNS).   This dataset comprises a trainings datasplit of 337,514 images distributed across 95 classes, with each class containing a minimum of 100 and a maximum of 10,000 images. The goal of this dataset is to be able to facilitate model training, here we have organized the data into a standard split, with 80% allocated for training, 10% for validation, and another 10% for testing purposes. This dataset structure ensures a balanced representation and supports scientific rigor in subsequent analyses. <h1>Technical details </h1> <h2>Data preprocessing </h2> Raw FlowCam output data is fully processed using in-house datapipelines, the VisualSpreadsheet software is only used for data acquisition during the lab run of the sample. Raw images and binary images are never saved during the FlowCam run, we only work on the image collages saved at the end of the run. Single images are cut from these collages using each image coordinates width and height pulled from the .lst file using in-house python code. The background of the images is not removed. These images are then predicted and annotated in-house at VLIZ. <h2>Data splitting </h2> The training dataset is 80% used for training, 10% for validation and 10% for prediction.  <h2>Classes, labels and annotations</h2> The dataset comprises 337,514 images distributed across 95 classes, with each class containing a minimum of 100 and a maximum of 10,000 images. Taxonomic coverage of the dataset comprises mainly of diatoms, dinoflagellates and cilliates, but to a lesser extent also zooplankton and other protists. <h2>Parameters </h2> The images are read using cv2.imread and the values are used as parameters. Metadata Parameter Descriptions: <ul> <li> image_path: The relative path to the image file showing the plankton or particle, usually structured by taxon or project folder. </li> <li> sample_datetime: The exact date and time (in YYYY-MM-DD HH:MM:SS.sss format) when the sample was acquired using the imaging instrument. </li> <li> flowcam_version: The software or hardware version of the FlowCAM instrument used to capture the image and process the sample. </li> <li> station: The sampling location code where the image was taken. These station codes typically refer to predefined geographic or monitoring points in the field. </li> <li> accepted_label: The final validated taxonomic label (e.g., genus or species) assigned to the organism or particle in the image, often based on expert review. </li> <li> accepted_aphia_id: The unique identifier (AphiaID) corresponding to the accepted taxonomic label in the World Register of Marine Species (WoRMS) database, which ensures standardized taxonomic reference. </li> <li> original_reference_id: A unique identifier (often a UUID) assigned to the image or sample in the original classification system (e.g., EcoTaxa), useful for traceability and linking back to the source record. </li> </ul> <h2>Data sources </h2> Images are collected during the monthly monitoring of phytoplankton communities in the Belgian Part of the North Sea during the LifeWatch multidisciplinary campaigns by FlowCam VS-4 benchmodel (Fluid Imaging Technologies, Yarmouth, Maine, U.S.A.). <h2>Data quality </h2> All images are predicted and subsequently manually validated to ensure the quality of the trainingset. <h2>Image resolution </h2> The size range imaged is 55-300µm. Images are acquired using a Sony XCD SC90 digital gray-scale camera. Images are during training of CNN resized to 100px by 100px. <h2>Spatial coverage </h2> The data comes from a number of fixed stations in the Belgian Part of the North Sea (BPNS).  Nine stations onshore are visited monthly: <table> <tbody> <tr> <td>Station</td> <td>Longitude</td> <td>Latitude</td> </tr> <tr> <td>130</td> <td>2.90535</td> <td>51.27055</td> </tr> <tr> <td>780</td> <td>3.057283</td> <td>51.471367</td> </tr> <tr> <td>330</td> <td>2.809083</td> <td>51.434117</td> </tr> <tr> <td>230</td> <td>2.85035</td> <td>51.308683</td> </tr> <tr> <td>710</td> <td>3.138283</td> <td>51.441217</td> </tr> <tr> <td>215</td> <td>2.61075</td> <td>51.274867</td> </tr> <tr> <td>ZG02</td> <td>2.500717</td> <td>51.33515</td> </tr> <tr> <td>120</td> <td>2.702483</td> <td>51.186083</td> </tr> <tr> <td>700</td> <td>3.221017</td> <td>51.377</td> </tr> </tbody> </table> Eight additional offshore stations are visited seasonally: <table> <tbody> <tr> <td>Station</td> <td>Longitude</td> <td>Latitude</td> </tr> <tr> <td>LW01</td> <td>2.256</td> <td>51.568667</td> </tr> <tr> <td>LW02</td> <td>2.556</td> <td>51.8</td> </tr> <tr> <td>435</td> <td>2.790333</td> <td>51.580667</td> </tr> <tr> <td>W07bis</td> <td>3.012517</td> <td>51.588033</td> </tr> <tr> <td>W08</td> <td>2.35</td> <td>51.458333</td> </tr> <tr> <td>W09</td> <td>2.7</td> <td>51.75</td> </tr> <tr> <td>W10</td> <td>2.416667</td> <td>51.683333</td> </tr> <tr> <td>421</td> <td>2.45</td> <td>51.4805</td> </tr> </tbody> </table> <h2> </h2> <h2>Temporal coverage </h2> The monitoring was initiated in May 2017 and has been running continuously every month. <h2>Contact information </h2> For technical questions about training dataset, you can contact wout.decrop@vliz.be.

opencc-by-4.0Sep 2024View details →

NMNH Images: NMNH images from GBIF export

Public records of accessioned specimens and observations curated by the National Museum of Natural History, Smithsonian Institution. These data are from the Departments of Botany, Entomology, Invertebrate Zoology and Vertebrate Zoology (Amphibians & Reptiles, Birds, Fishes, and Mammals) and include more than 270,000 primary type specimen records. https://collections.nmnh.si.edu/ipt/resource?r=nmnh_extant_dwc-a

opencc-by-4.0Sep 2024View details →

Type III interferons may suppress viral infections by triggering cell death -- Imaging Dataset

This dataset accompanies the article "Type III interferons may suppress viral infections by triggering cell death". Earlier version is available as a preprint, <a href="https://doi.org/10.1101/2024.09.09.612051" target="_blank" rel="noopener">https://doi.org/10.1101/2024.09.09.612051</a>. The updated dataset includes quantifications for Figure 7C and Figure 7D.

opencc-by-4.0Dec 2024View details →

Product Images of Life Cycle Assessment Dataset For Peritoneal Dialysis in Madrid, Spain

The database contains a collection of images showcasing the individual components of peritoneal dialysis (PD) products, along with their corresponding weights. These images serve as a visual record for life cycle assessment (LCA) purposes, focusing on the material composition and environmental impact of each product. <ol> <li> Patient Education Materials: Photographs of educational materials provided to patients, with accompanying data on the weight of the paper and packaging. </li> <li> Catheters and Surgical Kits: Images display the disassembled components of PD catheters and surgical kits, including tubing, connectors, and packaging. Each image is annotated with the precise weight of the individual components. </li> <li> Dialysis Solution Bags: The database includes images of both CAPD and APD solution bags, separated into their constituent parts (e.g., plastic bag, solution, and protective wrapping), with weights noted for each component. </li> <li> Connection Devices and Consumables: Detailed images of connection devices, clamps, and other consumable items, with individual component weights clearly labeled. </li> <li> Packaging and Transport Materials: Photographs of transport packaging, such as cardboard boxes and plastic wraps, alongside recorded weights for each element. </li> <li> Maintenance Items: Visuals of terminal catheter sets, cleaning agents, and related products, each accompanied by their respective weight data. </li> <li> Disposal Components: Images of used solution bags, syringes, and other single-use items, separated into recyclable and non-recyclable components, with weights specified for each. </li> </ol> This image-based database provides a clear and comprehensive reference for the material breakdown and weight distribution of PD product components, essential for conducting a thorough LCA and identifying areas for environmental improvement.

Video, image, and supplemental files linked in Burge et al. (2023) "Depredation by Bottlenose Dolphins Tursiops truncatus from Antillean Z-traps at Discovery Bay, Jamaica"

Video, image, and supplementary text files linked in Burge et al. (2023), Caribbean Naturalist, 95: 1–25.Depredation by Bottlenose Dolphins Tursiops truncatus from Antillean Z-traps at Discovery Bay, JamaicaAll video and image files referred to in the main text, figures, and tables are available from this repository. See Table 1 and Table S1 for additional details.

Still images from the KuramBio expedition 2012 (Stations 3-6, 8-11) obtained with the Ocean Floor Observation System

Repository with still images obtained from the videos of the KuramBio 2012 expedition in 5 second intervals. Still images belong to deep-sea stations where the Ocean Floor Observation System (OFOS) was deployed and enough video survey was obtained (i.e., Stations 3-6, 8-11). The Station 7 survey length was enough but has no HD video. Thus, images were not retrieved since the quality does not allow accurate lebensspuren or benthic fauna identification. The OFOS was lowered into the water at the CTD position. The first 300 meters lowering was conducted with 0.5 m/sec, and then the speed was increased to 0.8 m/sec while the ship was kept in position. At 500 meters above ground the speed was reduced to 0.5 m/sec, and further reduced to 0.3 m/sec at 200 meters above ground. As soon as visual contact with the bottom was established, the winch was stopped. The ship started moving with 0.5 knots in the appropriate direction, which was chosen depending on current and wind situation at the according station. The OFOS was kept in an appropriate distance to the seafloor, enabling the scientists to watch the macrofaunal organisms. The approximate size of the observed animals could be calculated with the help of two laser pointers having a distance of 10 cm between each other (see Fig. 1-3). Generally, the survey lasted slightly over one hour, then the ship was stopped and heaving of the OFOS started. This was first conducted at 0.5 m/sec and accelerated to 1.0 m/sec. For more information see the cruise report (RV Sonne cruise SO223; doi:10.1016/j.dsr2.2014.11.001).

opencc-by-4.0Nov 2023View details →

Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.

This dataset contains the video clips used to produce the results presented in:Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.

Orthophoto & DEM (MNE) issues d'images drone, UAV, Iot, Madagascar - 20230502 - 02_1

"Ce jeu de données présente les résultats des traitements photogrammétriques d'images de drone DJI Mavic 2 Pro UAV acquises sur le site de Iot, Madagascar à la date suivante : 20230502. Les vols ont été réalisés en partenariat avec l'IH.SM dans le but de créer des modèles numériques d'élévations pour cartographier l'écosystème marin. Le paramétrage du logiciel OpenDroneMap est partagé pour permettre la reproductibilité ou l'amélioration des traitements proposés: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] Le dépôt est composé des éléments suivants: - 00_: Planche d'aperçu des images - DCIM.zip: Images brutes issues du drone - GPS.zip: Geopackage contenant l'emprise du survol ainsi que la géolocalisation des images accompagnées de leurs miniatures dans la table d'attribut en base64 - METADATA.zip: Métadonnées au format ISO19115, Rapports avec miniatures des images de drone (dossier tb) et statistiques de vols. - PROCESSED_DATA.zip: Orthophoto, DEM, nuages de points, ... Arborescence d'origine: │ └─ 20230502_MDG-iot_UAV-02_1 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │---------------- └─ tb │-------- └─ PROCESSED_DATA Informations de survol: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -89.90 - Survey informations: No Images: 303 Median height: 256 meters Survey area: 141.79 hectares Survey from: 2023:05:02 16:10:31 to: 2023:05:02 16:50:41 "

Orthophoto & DEM (MNE) issues d'images drone, UAV, Nosyve, Madagascar - 20230430 - 02_1

"Ce jeu de données présente les résultats des traitements photogrammétriques d'images de drone DJI Mavic 2 Pro UAV acquises sur le site de Nosyve, Madagascar à la date suivante : 20230430. Les vols ont été réalisés en partenariat avec l'IH.SM dans le but de créer des modèles numériques d'élévations pour cartographier l'écosystème marin. Le paramétrage du logiciel OpenDroneMap est partagé pour permettre la reproductibilité ou l'amélioration des traitements proposés: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] Le dépôt est composé des éléments suivants: - 00_: Planche d'aperçu des images - DCIM.zip: Images brutes issues du drone - GPS.zip: Geopackage contenant l'emprise du survol ainsi que la géolocalisation des images accompagnées de leurs miniatures dans la table d'attribut en base64 - METADATA.zip: Métadonnées au format ISO19115, Rapports avec miniatures des images de drone (dossier tb) et statistiques de vols. - PROCESSED_DATA.zip: Orthophoto, DEM, nuages de points, ... Arborescence d'origine: │ └─ 20230430_MDG-nosyve_UAV-02_1 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │---------------- └─ tb │-------- └─ PROCESSED_DATA Informations de survol: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -85.00 - Survey informations: No Images: 467 Median height: 75 meters Survey area: 76.32 hectares Survey from: 2023:04:30 09:34:58 to: 2023:04:30 09:56:17 "

Orthophoto & DEM (MNE) issues d'images drone, UAV, Sarodrano, Madagascar - 20230505 - 02_1

"Ce jeu de données présente les résultats des traitements photogrammétriques d'images de drone DJI Mavic 2 Pro UAV acquises sur le site de Sarodrano, Madagascar à la date suivante : 20230505. Les vols ont été réalisés en partenariat avec l'IH.SM dans le but de créer des modèles numériques d'élévations pour cartographier l'écosystème marin. Le paramétrage du logiciel OpenDroneMap est partagé pour permettre la reproductibilité ou l'amélioration des traitements proposés: [ { 'name': 'orthophoto-resolution', 'value': 1 }, { 'name': 'auto-boundary', 'value': true }, { 'name': 'dem-resolution', 'value': '2.0' }, { 'name': 'dsm', 'value': true } ] Le dépôt est composé des éléments suivants: - 00_: Planche d'aperçu des images - DCIM.zip: Images brutes issues du drone - GPS.zip: Geopackage contenant l'emprise du survol ainsi que la géolocalisation des images accompagnées de leurs miniatures dans la table d'attribut en base64 - METADATA.zip: Métadonnées au format ISO19115, Rapports avec miniatures des images de drone (dossier tb) et statistiques de vols. - PROCESSED_DATA.zip: Orthophoto, DEM, nuages de points, ... Arborescence d'origine: │ └─ 20230505_MDG-sarodrano_UAV-02_1 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA │---------------- └─ tb │-------- └─ PROCESSED_DATA Informations de survol: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: Average Camera Pitch: -89.90 - Survey informations: No Images: 177 Median height: 155 meters Survey area: 59.52 hectares Survey from: 2023:05:05 07:03:19 to: 2023:05:05 07:17:41 "