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38,240 results for “Imaging”
Orthophoto & DEM from drone images, UAV, Aldabra arm06, Seychelles - 20221023 - 02_20
"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm06, Seychelles, on 20221023 <br> <br>The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. <br>Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. <br> <br><br>The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>The depot consists of the following elements:</b> <br>- 00_: Image preview panel <br>- DCIM.zip: RAW images from UAV <br>- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. <br>- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), <br>- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... <br> <br><b>Original tree structure:</b> <br>│ └─ 20221023_SYC-aldabra-arm06_UAV-02_20 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│-------- └─ PROCESSED_DATA <br> <br> <br><b>Flight survey information:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -75.00 <br> <br>- Survey informations: <br> No Images: 294 <br> Median height: 70 meters <br> Survey area: 19.84 hectares <br> Survey from: 2022:10:23 10:27:13 to: 2022:10:23 10:48:11 <br> "
Orthophoto & DEM from drone images, UAV, Aldabra passe dubois 40m, Seychelles - 20221024 - 02_26
"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra passe dubois 40m, Seychelles, on 20221024 <br> <br>The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. <br>Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. <br> <br><br>The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>The depot consists of the following elements:</b> <br>- 00_: Image preview panel <br>- DCIM.zip: RAW images from UAV <br>- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. <br>- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), <br>- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... <br> <br><b>Original tree structure:</b> <br>│ └─ 20221024_SYC-aldabra-passe-dubois-40m_UAV-02_26 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│-------- └─ PROCESSED_DATA <br> <br> <br><b>Flight survey information:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -90.00 <br> <br>- Survey informations: <br> No Images: 407 <br> Median height: 70 meters <br> Survey area: 9.97 hectares <br> Survey from: 2022:10:24 10:06:15 to: 2022:10:24 10:23:20 <br> "
Orthophoto & DEM from drone images, UAV, Aldabra arm01, Seychelles - 20221021 - 02_10
"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm01, Seychelles, on 20221021 <br> <br>The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. <br>Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. <br> <br><br>The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>The depot consists of the following elements:</b> <br>- 00_: Image preview panel <br>- DCIM.zip: RAW images from UAV <br>- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. <br>- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), <br>- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... <br> <br><b>Original tree structure:</b> <br>│ └─ 20221021_SYC-aldabra-arm01_UAV-02_10 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│-------- └─ PROCESSED_DATA <br> <br> <br><b>Flight survey information:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -75.00 <br> <br>- Survey informations: <br> No Images: 371 <br> Median height: 70 meters <br> Survey area: 76033.6 hectares <br> Survey from: 2022:10:21 17:47:32 to: 2022:10:21 18:09:25 <br> "
Orthophoto & DEM from drone images, UAV, Aldabra arm01, Seychelles - 20221019 - 02_1
"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm01, Seychelles, on 20221019 <br> <br>The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. <br>Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. <br> <br><br>The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>The depot consists of the following elements:</b> <br>- 00_: Image preview panel <br>- DCIM.zip: RAW images from UAV <br>- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. <br>- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), <br>- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... <br> <br><b>Original tree structure:</b> <br>│ └─ 20221019_SYC-aldabra-arm01_UAV-02_1 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│-------- └─ PROCESSED_DATA <br> <br> <br><b>Flight survey information:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -75.00 <br> <br>- Survey informations: <br> No Images: 204 <br> Median height: 70 meters <br> Survey area: 81435.99 hectares <br> Survey from: 2022:10:19 17:26:51 to: 2022:10:19 18:27:15 <br> "
Orthophoto & DEM from drone images, UAV, Aldabra arm01, Seychelles - 20221025 - 02_33
"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm01, Seychelles, on 20221025 <br> <br>The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. <br>Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. <br> <br><br>The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>The depot consists of the following elements:</b> <br>- 00_: Image preview panel <br>- DCIM.zip: RAW images from UAV <br>- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. <br>- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), <br>- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... <br> <br><b>Original tree structure:</b> <br>│ └─ 20221025_SYC-aldabra-arm01_UAV-02_33 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│-------- └─ PROCESSED_DATA <br> <br> <br><b>Flight survey information:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -75.00 <br> <br>- Survey informations: <br> No Images: 256 <br> Median height: 100 meters <br> Survey area: 13.41 hectares <br> Survey from: 2022:10:25 12:14:07 to: 2022:10:25 12:25:14 <br> "
Orthophoto & DEM from drone images, UAV, Aldabra gigi, Seychelles - 20221024 - 02_30
"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra gigi, Seychelles, on 20221024 <br> <br>The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. <br>Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. <br> <br><br>The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>The depot consists of the following elements:</b> <br>- 00_: Image preview panel <br>- DCIM.zip: RAW images from UAV <br>- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. <br>- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), <br>- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... <br> <br><b>Original tree structure:</b> <br>│ └─ 20221024_SYC-aldabra-gigi_UAV-02_30 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│-------- └─ PROCESSED_DATA <br> <br> <br><b>Flight survey information:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -75.00 <br> <br>- Survey informations: <br> No Images: 176 <br> Median height: 148 meters <br> Survey area: 43.47 hectares <br> Survey from: 2022:10:24 15:55:20 to: 2022:10:24 16:09:47 <br> "
Orthophoto & DEM from drone images, UAV, Aldabra passe dubois, Seychelles - 20221022 - 02_12
"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra passe dubois, Seychelles, on 20221022 <br> <br>The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. <br>Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. <br> <br><br>The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>The depot consists of the following elements:</b> <br>- 00_: Image preview panel <br>- DCIM.zip: RAW images from UAV <br>- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. <br>- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), <br>- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... <br> <br><b>Original tree structure:</b> <br>│ └─ 20221022_SYC-aldabra-passe-dubois_UAV-02_12 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│-------- └─ PROCESSED_DATA <br> <br> <br><b>Flight survey information:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -75.00 <br> <br>- Survey informations: <br> No Images: 303 <br> Median height: 70 meters <br> Survey area: 16.11 hectares <br> Survey from: 2022:10:22 09:02:54 to: 2022:10:22 09:14:43 <br> "
Orthophoto & DEM from drone images, UAV, Aldabra arm01, Seychelles - 20221020 - 02_3
"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm01, Seychelles, on 20221020 <br> <br>The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. <br>Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. <br> <br><br>The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>The depot consists of the following elements:</b> <br>- 00_: Image preview panel <br>- DCIM.zip: RAW images from UAV <br>- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. <br>- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), <br>- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... <br> <br><b>Original tree structure:</b> <br>│ └─ 20221020_SYC-aldabra-arm01_UAV-02_3 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│-------- └─ PROCESSED_DATA <br> <br> <br><b>Flight survey information:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -75.00 <br> <br>- Survey informations: <br> No Images: 279 <br> Median height: 70 meters <br> Survey area: 12.37 hectares <br> Survey from: 2022:10:20 10:13:31 to: 2022:10:20 10:27:28 <br> "
Orthophoto & DEM from drone images, UAV, Aldabra arm06, Seychelles - 20221022 - 02_15
"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm06, Seychelles, on 20221022 <br> <br>The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. <br>Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. <br> <br><br>The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>The depot consists of the following elements:</b> <br>- 00_: Image preview panel <br>- DCIM.zip: RAW images from UAV <br>- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. <br>- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), <br>- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... <br> <br><b>Original tree structure:</b> <br>│ └─ 20221022_SYC-aldabra-arm06_UAV-02_15 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│-------- └─ PROCESSED_DATA <br> <br> <br><b>Flight survey information:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: Average <br> Camera Pitch: -75.00 <br> <br>- Survey informations: <br> No Images: 299 <br> Median height: 150 meters <br> Survey area: 28.16 hectares <br> Survey from: 2022:10:22 15:36:50 to: 2022:10:22 15:53:50 <br> "
Orthophoto & DEM from drone images, UAV, Aldabra passe dubois msp, Seychelles - 20221024 - 02_27
"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra passe dubois msp, Seychelles, on 20221024 <br> <br>The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. <br>Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. <br> <br><br>The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>The depot consists of the following elements:</b> <br>- 00_: Image preview panel <br>- DCIM.zip: RAW images from UAV <br>- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. <br>- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), <br>- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... <br> <br><b>Original tree structure:</b> <br>│ └─ 20221024_SYC-aldabra-passe-dubois-MSP_UAV-02_27 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│-------- └─ PROCESSED_DATA <br> <br> <br><b>Flight survey information:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -75.00 <br> <br>- Survey informations: <br> No Images: 245 <br> Median height: 70 meters <br> Survey area: 14.72 hectares <br> Survey from: 2022:10:24 10:40:53 to: 2022:10:24 10:52:48 <br> "
Orthophoto & DEM from drone images, UAV, Aldabra arm01, Seychelles - 20221020 - 02_6
"This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm01, Seychelles, on 20221020 <br> <br>The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI. <br>Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. <br> <br><br>The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>The depot consists of the following elements:</b> <br>- 00_: Image preview panel <br>- DCIM.zip: RAW images from UAV <br>- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. <br>- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files), <br>- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ... <br> <br><b>Original tree structure:</b> <br>│ └─ 20221020_SYC-aldabra-arm01_UAV-02_6 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│-------- └─ METADATA <br>│-------- └─ PROCESSED_DATA <br> <br> <br><b>Flight survey information:</b> <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -17.80 <br> <br>- Survey informations: <br> No Images: 17 <br> Median height: 196 meters <br> Survey area: 39.4 hectares <br> Survey from: 2022:10:20 17:49:42 to: 2022:10:20 17:56:03 <br> "
Dataset - DeepWealth: A Generalizable Open-Source Deep Learning Framework using Satellite Images for Well-Being Estimation
<p>This dataset encapsulates the Checkpoints obtained during the training process of the Deep Learning model, which can be used for new estimations.</p> <p>The aim of the DeepWealth package is to provide a generalizable Deep Learning framework for the use of remote sensing in poverty estimation. The combination of Deep Learning and Earth Observation data is increasingly being used to estimate socioeconomic conditions at regional and global scales. The proposed framework aligns with the Sustainable Development Goal SDG1 of ending poverty. The framework provides open-source data, code, and training models (checkpoints) for reproducibility and replicability.</p> <ul> <li>The source code can be found in <a href="https://github.com/PARSECworld/DeepWealth" target="_blank" rel="noopener">https://github.com/PARSECworld/DeepWealth</a></li> <li>The metadata from source code can be found in <a href="https://github.com/PARSECworld/DeepWealth/blob/main/metadata.pdf" target="_blank" rel="noopener">https://github.com/PARSECworld/DeepWealth/blob/main/metadata.pdf</a></li> <li>The paper describing the development of this framework can be found at: Ben Abbes, A., Machicao, J., Corrêa, P. L. P., Specht, A., Devillers, R., Ometto, J. P., Kondo, Y., & Mouillot, D. (2024). DeepWealth: A generalizable open-source deep learning framework using satellite images for well-being estimation. <em>SoftwareX</em>, 27, 101785. <a href="https://doi.org/10.1016/j.softx.2024.101785">https://doi.org/10.1016/j.softx.2024.101785</a> </li> </ul>
IODP Expedition 397 Core composite images
A digital composite image (PNG) is made for each core comprising core sections scanned using a line-scan camera. The composite layout is equivalent to traditional core table photos. Top left is top of core; color and meter rule references are included.
Jacobaea vulgaris and meadow Augmented image classification dataset (binary)
<h3>General Information</h3> <p>Total instances: 117008<br>Instances in the Jacobaea vulgaris class: 58504 <br>Instances in the Meadow class: 58504<br>Image sizes from 224x224 pixels on three color channels (RGB)</p> <p><br>Performance increase training a ResNet50 on the base dataset versus the same architecture on the augmented data set shared here: +3,79 percent points in ROC AUC on an independent test set with 240 instances.<br><br></p> <h3>Data Generation and Source</h3> <p>The initial images in this dataset were taken as part of the project “UAV-basiertes Grünlandmonitoring auf Bestands- und Einzelpflanzenebene” (engl. “UAV-based Grassland Monitoring at Population and Individual Plant Level”), financed by the Authority for Economy, Transport, and Innovation of Hamburg. <br>In September 2018, flights with an octocopter were conducted over two extensively used grassland areas in the urban area of Hamburg.</p> <p>In my master's thesis at <a href="https://www.tu.berlin/dams">DAMS Lab</a> at TU Berlin, I evaluated the effect of different augmentation strategies for Jacobaea vulgaris image classification on the several performance metrics (most importantly the ROC AUC score). The identified augmentation strategies are -besides to performance based selection- also selected based on domain knowledge, which I acquired during the research for my master thesis. </p> <p>Additional information about the initial image generation process is to be found <a href="https://archiv.geomv.de/geoforum/2019/doc/Tagungsband_GeoForum-MV-2019_eBook.pdf">here </a> [p. 45–53] and <a href="https://www.auf.uni-rostock.de/storages/uni-rostock/Alle_AUF/AUF/GG/PDF/gruenlandmonitoring/2019-12-12-FHH-Workshop_Vortrag_Zacharias.pdf">here</a>. </p> <h3> </h3> <h3>Augmentations applied</h3> <ul> <li>Gaussian Noise: For the Gaussian noise augmentation, the mean of the added noise is set to zero. The lower and upper bounds for the random variance of the noise are 20.4663 and 54.0395 respectively. The bounds were identified by hyperparameter tuning. The search space for the lower bound was set from 5 to 30 and for the upper bound from 31 to 100. Those two search spaces were defined by visual inspection of the effects of applying Gaussian noise with different variance values to images of both classes. The Gaussian noise is sampled for each color channel individually. </li> <li>Random Brightness and Contrast: The brightness will randomly be increased or decreased by a factor ranging from 0.7010 to 1.2990. The The contrast will also be randomly increased by a factor ranging from 0.5775 to 1.4225. Those two ranges were identified using hyperparameter tuning. The search space for the maximal percentual increase or decrease of brightness and contrast was individually set from 1% to maximally 50% increase or decrease.</li> <li>Cutout Dropout: In this augmentation method a certain percentage of the input image is getting covered by black patches. The patches have a certain size in pixels, the implementation of this technique in this thesis uses square patches. The black patches are then randomly introduced into the image, by randomly alloacting the<br>patches across the image and then setting the corresponding pixel values to zero. The iamge is getting covered with patches until the cover percentage is reached. We<br>set percentage of the image to be randomly covered by black patches to 56.76%. The size of the patches, which randomly cover the image, is set to 4 pixels. A<br>good illustration of this is found in figure 4.2. The augmentation technique is inspired by the research proposed by Devries et al.[8]. Both values were identified by hyperparameter tuning. The search space for the patch size in pixels is categorical and includes the values [1, 2, 4, 7, 8, 14, 16, 28]. Those values all are multiples of 224, which is the image width and height in pixels. The patch size needs to be a multiple of the width and height in order to be suitable for the algorithm implementation. The search space for the cover percentage of the image had been set from 1% to 60%. This search space limits narrows the search down to a space where still a big part of the image is uncovered. The algorithm rearranges the image into a two dimensional grid and randomly masks rows of this grid by setting the pixel values in this row to zero. Then, the image gets rearranged, now with the randomly generated patches included.</li> <li>Random Saturation: The saturation of each pixel is randomly getting shifted. The upper bound for randomly shifting<span> </span>the saturation value of each pixel is set to 231.689%. This value was identified using hyperparameter tuning. An upper limit of the maximal saturation shift had been set to 40% shift in either direction for hyperparameter tuning.</li> <li>Horizontal Flip: The image gets flipped along the horizontal axis. </li> <li>Vertical Flip: The image gets flipped along the vertical axis.</li> <li>Random Rotation 90 degrees: Randomly rotates the image by a k-fold of 90 degrees, whereby k = {0, 1, 2, 3}.</li> </ul> <p> </p> <p>All augmentation methods and with their tuned augmentation hyperparameters (if existent) are applied to an image from the test set in figure 4.2. With the seven identified<br>augmentation techniques a dataset of 800% the size of the original dataset is created. The Augment model is trained on exactly this dataset. Of course next to the augmented images, the dataset still includes the original, unaugmented images. TensorFlow, along with additional libraries including Optuna for hyperparameter optimization and Albumentations for image augmentation, were used in for the implementation of this project.</p> <p> </p> <h3>Rational behind the augmentations applied</h3> <ul> <li>Random Rotation, Vertical and Horizontal Flip: These three augmentation strategies were chosen to make the classifier less sensitive to the orientation of the plant. The goal is to train a model that can classify plants regardless of their orientation. In order to achieve this effectively across different orientations, vertical flips, horizontal flips, and random 90-degree rotations are chosen for evaluation.</li> <li>Random Saturation: The varying saturation of the images simulates different levels of chlorophyll in the leaves, which is responsible for the green color of the<br>leaves and the intensity of this color. The color of the plant parts (leaves, stems, and flowers) is also influenced by factors such as soil, sun, weed density and pressure, location, and water availability. Varying the saturation of the images simulates changes in these factors.</li> <li>Gaussian Noise: By adding noise, in this case Gaussian noise, different lighting conditions are simulated when capturing the images. We specifically chose Gaussian<br>noise because it is common in many real-world scenarios and is based on the Central Limit Theorem, which states that the sum of many independent random variables.<br>tends to be normally distributed. This makes Gaussian noise a logical choice for simulating real-world random noise.</li> <li>Random Brightness Contrast: The Random Brightness and Random Contrast Augmentation uses brightness to mimic varying lighting conditions and contrast to highlight differences between plants by contrasting them more strongly, thereby highlighting their edges. This approach for highlighting edges is of course much more subtle than the canny edge detection augmentation. This augmentation method combines a weak focus on edges with variations in lighting conditions in one approach. The random contrast is a much softer approach for highlighting edges of plants, compared to the Canny edge detection augmentation. The other features in the images do not get changed that much, compared to the changes from edge detection augmentation.</li> <li>Cutout Dropout: The cutout augmentation simulates random occlusion by other plants. These occlusions are common and expected. Jacobaea vulgaris plants may be partially or completely obscured by other plants during image capturing. This augmentation technique makes the models more robust to random occlusion.</li> </ul> <h3> </h3> <h3>Data License</h3> <p>The dataset is licensed under the license CC BY 4.0. The attributor of the data is the Chair of Geodesy and Geoinformatics at the University of Rostock. The data was created within the scope of the project 'UAV-based Grassland Monitoring at Population and Individual Plant Level', financed by the Authority for Economy, Transport, and Innovation of Hamburg.</p>
IODP Expedition 397 Scanning electron microscope images
Microscopic images of discrete samples were acquired using a scanning electron microscope (SEM) and captured as image files. These files were uploaded along with a brief description and a record of the microscopic conditions when the image was taken.
IODP Expedition 397 Thin section images
Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.
IODP Expedition 397 Closeup images
Close-up images taken by digital cameras as requested by the science party, typically when the section-half image is not sufficient. Close-up photographs of the areas of interest may be taken from whole-round sections, pieces, or section halves in sediments and rock.
IODP Expedition 397 Section-half images
Digital section images were taken of the flat face of split cores on the Section Half Imaging Logger (SHIL) using a linescan camera at a resolution of 20 lines/mm (50 micron pixels). Cores were imaged as soon as possible after splitting to minimize color changes that occur through oxidation and drying. The SHIL produces TIF files as well as reduced-size JPG files. The TIF files are not kept online but users may request them from the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a>.
AGS_apple_detection - Apple fruit images dataset for full image object detection
<p>This dataset correspond to full apple tree images (623) annotated for the task of object detection with its corresponding annotations in yolo format saved as txt files. The dataset was divided into test, train and validation<br><br>The data was collected in 2017 on 4 different apple varieties using a Samsung sm-a510F cell phone at two different resolutions: 2448 x 3264 px and 3096 x 4128 px in the orchards of Agroscope located in Wallis, Switzerland. </p>
Spectral- and image-based metics for evaluating cleaning tests on unvarnished painted surfaces
<p>Data spreadsheets, templates, and supporting image data associated to the article "Spectral- and image-based metics for evaluating cleaning tests on unvarnished painted surfaces", forming part of the doctoral thesis of Jan Dariusz Cutajar (University of Oslo). The following file types and files are included:</p> <p><strong>Templates</strong></p> <ul> <li>Template_dE00 calculation.xlsx</li> <li>Template_Skewness calculation.xlsx</li> </ul> <p><strong>Metric calculation tables</strong></p> <ul> <li>Metrics_CIELAB plots.xlsx</li> <li>Metrics_dE00 plots.xlsx</li> <li>Metrics_FTIR plots.xlsx</li> <li>Metrics_Gloss plots.xlsx</li> <li>Metrics_HSI (NDI) plots.xlsx</li> <li>Metrics_HSI (Supervised) plots.xlsx</li> <li>Metrics_HSI (Unsupervised) plots.xlsx</li> <li>Metrics_SEM-EDX particle count plots.xlsx</li> <li>Metrics_Skewness plots.xlsx</li> </ul> <p><strong>Extended result tables</strong></p> <ul> <li>Free liquid trial assessments.xlsx</li> <li>Cleaning score tables.xlsx</li> </ul> <p><strong>Processed images from photography, microscopy, FTIR and SEM</strong></p> <ul> <li>Processed images (VIS, FTIR, SEM).pdf</li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.