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401 results for “UAVs”
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> "
UAV-based Lidar point clouds of Experimental Station Britz, Brandenburg, 2024
<p><strong>UAV-based Lidar data of a Experimental Forest Station Britz</strong></p> <ul> <li>Date of acquisition: 09.07.2024</li> <li>Location: Experimental Station Britz, Britz, Brandenburg, Germany</li> <li> <div>DEIMS.iD: <a href="https://deims.org/8ee82a9b-5086-4547-b5aa-4064e3314762">https://deims.org/8ee82a9b-5086-4547-b5aa-4064e3314762</a></div> </li> <li>UAV: DJI M300 RTK with SAPOS connection</li> <li>Flight altitude above ground level: 40 m</li> <li>Line spacing: 7 m</li> <li>Sensor: Yellowscan Mapper Plus</li> <li>EPSG: 25833; GCG2016</li> <li>Data products: <br>• High-resolution point cloud in .laz format<br>• Yellowscan Cloudstation strip adjustment report<br>• Trimble Applanix POSPAC - diagnostic report</li> </ul> <p><br><strong>Acknowledgment</strong></p> <p>Dr. Tanja Sanders</p> <p>Dr. Marco Natkhin</p> <p>Institute of Forest Ecosystems<br>Johann Heinrich von Thünen Institute<br>Federal Research Institute for Rural Areas, Forestry and Fisheries</p>
Gummern - Mining Waste Deposits 5.73cm DEM UAV-derived
<h2>Abstract</h2> <p>Mining Waste Deposits High Detailed Digital Elevation Model derived from Multispectral UAV DJI Mavic 3M.</p> <p>This depository contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Mining Waste Deposits 5.73cm DEM UAV-derived</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Mining Waste Deposits High Detailed Digital Elevation Model derived from Multispectral UAV DJI Mavic 3M</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Mining Waste Deposits, DEM, DTM, DSM, UAV, Drone</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>See "Mining Waste Deposits Multispectral Drone Imagery" <a href="https://doi.org/10.5281/zenodo.13622458">https://doi.org/10.5281/zenodo.13622458</a></p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Hydrography</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>3.10.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>3.10.2023</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>See "Mining Waste Deposits Multispectral Drone Imagery"</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.0573m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.05m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 4258</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UNILEON</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Typical Village UAV Aerial Photography Dataset in northeastern Tibetan Plateau (2022)
<p>This dataset was collected during a field survey in the Hehuang Valley, located in the northeastern Tibetan Plateau, in July and August 2022. Using a DJI Mavic 2 Pro equipped with a Hasselblad L1D-20c camera, over 4,600 aerial photographs were captured from 55 typical villages across the region. These images were processed into high-resolution orthophotos using Agisoft PhotoScan 1.25 software, resulting in ultra-high-precision orthophoto data for the 55 villages. The "Village Information" section provides detailed information on each village, including its full name, abbreviation, latitude and longitude coordinates, and elevation. This dataset accurately reflects the overall situation, spatial patterns, and surrounding environment of the typical villages, offering a high-resolution data source for spatial structure analysis, land use mapping, and correction tasks.</p>
Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs
<p>Additional code and data for the paper by Groos et al. entitled "Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs"</p> <p>Correspondence: Alexander R. Groos (alexander.groos@fau.de)</p> <p><br>The repository contains:<br>(1) The raw data (log files) for each UAV-based atmospheric sounding<br>(2) The postprocessed and reformatted data for each sounding and vertical profile<br>(3) The commented R-Scripts for data processing, analysis and visualisation<br>(4) A subset of the meteorological data from the nearby weather stations</p> <p><br>Description of sub-folders:</p> <p>-aws_data<br>-- aws_fisistock.txt # meteorological data from AWS Fisistock for the period of the campaign<br>-- aws_gandegg.txt # meteorological data from AWS Gandegg for the period of the campaign<br>-- aws_sackhorn.txt # meteorological data from AWS Sackhorn for the period of the campaign</p> <p>- processed_data<br>-- kanderfirn_2021-06-16_10:45_p1_pprz.tab # meteorological data for first profile/descent at about <br>-- kanderfirn_2021-06-16_10:45_p2_fr.tab # flight recorder data for second profile/descent at about 10:45 CEST<br>-- kanderfirn_2021-06-16_10:45_p2_pprz.tab # meteorological data for second profile/descent at about 10:45 CEST<br>-- kanderfirn_2021-06-16_10:45_pprz.tab # meteorological data for the entire sounding (first and second profile/descent) at about 10:45 CEST<br>-- .<br>-- .<br>-- .<br>-- kanderfirn_2021-06-16_16:50_p1_pprz.tab # meteorological data for first profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_p2_fr.tab # flight recorder data for second profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_p2_pprz.tab # meteorological data for second profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_pprz.tab # meteorological data for the entire sounding (first and second profile/descent) at about 16:50 CEST<br>-- kanderfirn_soundings_2021-06-16.csv # summary table of vertical profiles (1 m height intervals): one column for each profile/descent and variable<br>-- kanderfirn_turbulence_2021-06-16.csv # summary table of vertical turbulence profiles (1 m height intervals): one column for each profile/descent</p> <p>- raw_data<br>-- fr_kanderfirn_2021-06-16_10:45.LOG # flight recorder data from the sounding at about 10:45 CEST (binary file)<br>-- .<br>-- .<br>-- .<br>-- fr_kanderfirn_2021-06-16_16:50.LOG # flight recorder data from the sounding at about 16:50 CEST (binary file)<br>-- pprz_kanderfirn_2021-06-16_10:45.LOG # meteorological data from the sounding at about 10:45 CEST (human readable text file)<br>-- .<br>-- .<br>-- .<br>-- pprz_kanderfirn_2021-06-16_16:50.LOG # meteorological data data from the sounding at about 16:50 CEST (human readable text file)</p> <p>- R_scripts<br>-- figures.R # Script to create Figures 5, 6, 8, 9, 10, 11, 12<br>-- lapse_rate.R # Script to calculate lapse rates and surface-based inversions (includes code for Figures 7 and B1)<br>-- postprocessing.R # Script to reformat preprocessed and preselected pprz-files<br>-- turbulence.R # Script for the calculation of the turbulence proxy from the recorded roll rate</p>
Codes and test datasets developed for Mapping paleolacustrine deposits with a UAV-borne multispectral camera: Implications for future drone mapping on Mars.
<p>NASA’s Ingenuity Mars Helicopter has ushered in a new era in planetary exploration by utilizing Unmanned Aerial Vehicles (UAVs) to enhance our understanding of planetary surfaces. This project evaluates the potential of UAVs for mapping Martian environments, using Lake Natron, Tanzania, as an analog for Martian paleolakes.</p> <p>During two field seasons (January and July 2023), we employed a Phantom 4 Pro drone equipped with a MicaSense RedEdge-M multispectral camera and a TerraSpec Halo VNIR-SWIR spectrometer to capture high-resolution imagery and spectral data. Almost all image processing and analysis were performed using Python scripting, except for image mosaic and Digital Elevation Model (DEM) generation.</p> <p>We benchmarked the onboard image processing capabilities using a Raspberry Pi 5 single-board computer. </p> <p>In this repository, we share all the code developed during our study. Processing steps include,<br>1. DN to radiance conversion<br>2. Panel radiance extraction<br>3. Calculate reflectance factors using DLS data<br>4. Calculate reflectance at MicaSense band<br>5. Convert radiance to reflectance using 1 point empirical line method (1p ELM)<br>6. Convert radiance to reflectance using 2 point empirical line method (2p ELM)<br>7. Atmospheric correction using 6SV method<br>8. Convert radiance to reflectance using DLS data<br>9. Calculate Band indices<br>10. Weighted Kmean clustering<br>11. Finding the optimal number of clusters using the elbow method<br>12. Cmean clustering</p> <p>We also included sample image data used in the study. Feel free to contact us for more information/data.</p>
Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach
<p>Main data used for the scientific paper entitled: "Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach".</p> <ol> <li>"Danta_dem_10cm_px.tif": orthomosaic-derived DEM</li> <li>"Danta_rgb_2.2cm_px.tif": ortophoto </li> <li>"GPS points": list of GPS samples points</li> <li>"Main data": field vegetation data and indexes used for the regressions</li> <li>"Raw PointCloud". Lidar original dataset</li> <li>"Pre-processed PointCloud": Lidar dataset after pre-processing (see paper's methods) </li> <li>"DTM_DantaGround_grid50cm_minimo": Output (TIFF); the LiDAR-derived DTM showed in the paper</li> <li>"LAI": Output (Shapefile); the LiDAR-derived LAI showed in the paper.</li> </ol> <p> </p>
UAV outputs and associated field measurement of the herbaceous and tree of the Senegalese savanna of the Dahra Djoloff research center
<p>The dataset contains UAV outputs (mosaic , surface model and terrain) and the associated measurements of vegetation( herbaceous and woody) that were made within the research isra station of Dahra Djoloff.</p> <p>Sites</p> <p>The sites were 38 ha-1 plots across the research station. The UAV were collected on the same site at the same date in October 2018(end of the wet season and maximum of the biomass). The sites were the sites of previous studies (Raynal 1964, Ndiaye et al. 2014, Ndiaye et al. 2015). The plots were chosen based on several studies of vegetation dynamics and these plots were judged to be representative of the diversity of vegetation type within the research station.</p> <p>UAV flight plan</p> <p>We used a low-cost UAV with an RGB (Red Green Blue) captor integrated in the UAV. The plots were mapped using a Dji Spark UAV with the litchi application for the automatic flight. The flight plan was six 100 m transects each separated by 20 m was performed at an altitude of 80 m and at a speed of 5 m.s-1. Images were acquired in autofocus mode (ISO exposure were automatically adjusted) at two-second intervals throughout the flight. The angle of view was 80°. The frontal overlap was about 90% and the side overlap about 80% with 80° angle</p> <p>Field measurement.</p> <p>Herbaceous Biomass.</p> <p>For the Landscape dataset, 10 squares of 1 m² were sampled; All the aboveground biomass was cut and weighted in fresh. A composite sample was made for each site and weighted dry to evaluated the dry matter content and so the dry matter of each sample.</p> <p>The positions of the squared was mark r with a plastic bag on the ground.</p> <p>Tree measurement.</p> <p>For the landscape, we selected 10 trees on the UAV maps. The measurements were made after image analysis in January 2019 and January 2020. The trees were not measured on all the site.</p> <p>The measured variables were the maximum height of the tree (using a clinometer), the diameter of the tree crown in the north-south direction and in the west-east direction. Their tree crown area was calculated assuming that the crown was a circle. The trunk diameters were measured at 0.30 cm in both direction and the circumference were calculated. All woody species were identified at the species and genus levels.</p> <p>Image analysis.</p> <p>The images taken during each flight were processed using a PiX4D mapper (Pix4D SA, Lausanne, Switzerland). 3D mapping is the basic parameter proposed in the software. For each plot, an orthophotograph, a digital surface model, and a digital elevation model were computed and exported in GeoTIFF format.</p> <p>Data organization</p> <p>For each plot, we had</p> <ul> <li>DSM that contains the surface model in tiff</li> <li>DTM that contains the terrain model in tiff</li> <li>Mosaic that the orthomosaic in tiff.</li> </ul> <p>All the different geotiff can directly be download.</p> <p>Data are in a zip file that contains the shapefile with the position and table with the field measurements.</p> <p>The shapefile “Herbaceous.shp” contain the positions of the squared sample but also of squared that contains only soil (squared cut before the flight).</p> <p>The CSV “Herbaceous-landscape.csv” contains the measurement of Aboveground biomass. (FM fresh mass and DM dry mass). Both are in g (g.m-²). The biomass was available for 346 squared.</p> <p>The shapefile “tree.shp” contains the positions of the tree. Here the shapefile contains the positions of all the tree preselected on the map. Only a selection of theses tree was measured on the field.</p> <p>The file “Tree-landscape.csv” contains the tree measurements with the species, the height (in m), the trunk circumference (TC) in cm and the area of the crown(area) in m². The tree measurements were available for 240 trees.</p> <p> </p><p>reference</p> <p></p> <p>Ndiaye, O., A. T. Diop, L. E. Akpo, and M. Diène. 2014. Dynamique de la teneur en carbone et en azote des sols dans les systèmes d’exploitation du Ferlo: cas du CRZ de Dahra. Journal of Applied Biosciences <strong>83</strong>:7554-7569.</p> <p>Ndiaye, O., A. T. Diop, M. Diène, and L. E. Akpo. 2015. Étude comparée de la végétation de 1964 et 2011 en milieu pâturé: Cas du CRZ de Dahra. Journal of Applied Biosciences <strong>88</strong>:8235–8248.</p> <p>Raynal, J. 1964. Etude botanique de pâturages du Centre de Recherches Zootechniques de Dahra-Djoloff (Sénégal).</p> <p> </p>
UAV outputs and associated field measurement of the herbaceous and tree of the Senegalese savanna across Senegal
<p>This dataset contains UAV outputs (mosaic, surface and terrain model) and field measurement of vegetation that were made in northern and Eastern Senegal.</p> <p>Sites</p> <p>National gradient measurements</p> <p>For the national gradients, the measurements were made on 45 different plots in two different field campaign. One in the Northern part at the end of September 2020 and the other in South eastern part of Senegal in middle of October. The selection of the site was a combination of accessibility (not far from the road) and diversity of vegetation. The average rainfall for the period 1981-2018 was ranging from 221 mm.y-1 to 468 mm. y-1 for the Northern Part and ranging 759 mm.y-1 to 1246 mm y-1 for the south eastern part.</p> <p>UAV flight plan</p> <p>We used a low-cost UAV with an RGB (Red Green Blue) captor integrated in the UAV. The UAV was an Anafi of Parrot with PIX4D capture application using the double gird flight plan in a square generally of 100m*100m; The height of the flight was 80m with an overlap of 80% at low speed with 80° angle °. The flights were made at any time during the day.</p> <p>Field measurement.</p> <p>Herbaceous Biomass.</p> <p>3 squares of 1 m² were sampled. All the aboveground biomass was cut and weighted in fresh. A composite sample was made for each site and weighted dry to evaluated the dry matter content and so the dry matter of each sample.</p> <p>The height of 5 herbaceous individuals selected randomly were measured. We recorded the species composition with percentage of cover of each species. We collected an herbarium sample each time we had a new species. The sample were used to identified the species by the IFAN herbarium team. The positions of the squared was mark with a wood triangle painted on the ground.</p> <p>Tree measurement.</p> <p>Four trees were measured on the field. It was the four woody individuals the closest to the first square of herbaceous measurements were made in each direction (Northwest, North east, South West, South East).</p> <p>The distance to the first square of each tree were measured using a telemeter. The height was also measured with a laser telemeter. The circumference at 0.30cm and 1.3 cm were measured. The diameter of the tree crown in the north-south direction and in the west-east direction were measured to the crow area calculated assuming that the crown was a circle.</p> <p>The species were recorded. We collected an herbarium sample each time we had a new species. The sample were used to identified the species by the IFAN herbarium team.</p> <p>Image analysis.</p> <p>The images taken during each flight were processed using a PiX4D mapper (Pix4D SA, Lausanne, Switzerland). 3D mapping is the basic parameter proposed in the software. For each plot, an orthophotograph, a digital surface model, and a digital elevation model were computed and exported in GeoTIFF format.</p> <p>Data organization</p> <p>The data are organized in two separated folders for each dataset.</p> <p>Each dataset folders contains four folders:</p> <ul> <li>DSM that contains the surface model in tiff</li> <li>DTM that contains the terrain model in tiff</li> <li>Mosaic that the orthomosaic in tiff.</li> <li>Data that contains the shapefile with the position and table with the field measurements.</li> </ul> <p>The shapefile” national-shape.shp" contains the positions of both tree and herbaceous samples. In some case it was hard to position the squared or the tree. The position and the shape of the object are not well defined.</p> <p>The file “tree-national.xlsx” contains the information on the tree measurement. The ID that contains the site and the positions of the trees, the distance from the squared in m that indicate the distance of the tree to the biomass square. The height H (in m), the trunk circumference at 1.30m (TC1.3) and at 0.3m(TC0.3) in cmand the area of crown (Area). The species is also described.</p> <p>The file “ herbacous_national.xlsx” contains the information on the herbaceous layer.</p> <p>For each square, the height of the herbaceous layer (H), Fresh mass (FM), Dry matter content (DMC) and dry Mass (DM) are presented; The last columns of the file are the different species with the percentage of cover in each case.</p> <p> </p>
UAV-based data for Lake Mulargia (Sardinia, Italy) (2020/09/23)
<p>This dataset contains MicaSense-derived data of Lake Mulargia (Sardinia, Italy) for the 23 September 2020. The acquisition was done by CGR Spa (Italy). Available products are: True-color image (RGB), at-sensor-radiance (TOA), and Bottom-of-atmosphere reflectance data.</p>
Post-remediation evaluation of contaminated site using geophysical methods: Multispectral UAV data Olkusz (Poland) 20220629
<p>In order to analyze the vegetation condition, photos were taken in the infrared (NIR, 750 - 2500 nm) and infrared (Red Edge, 690-720 nm) range. The DJI Matrice 600 platform was used for the raid. The photos were taken from the ceiling of 150 m with the MicaSense Red Edge M camera with a focal length of 6 mm.</p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
UAV multispectral imagery dataset over a vineyard affected by Botrytis in 'Tomiño', Pontevedra, Spain. It includes GPS location of vine trunks, diseases and GCP points.
<p>This dataset contains a set of ground data and four flights captured on grape harvest over a vineyard affected by Botrytis cinerea. UAV flights took place on 16 September 2021, at 30 m height and using different angles (0, 30, 45 degrees). Pictures were taking using a Micasense RedEdge 3 sensor and were calibrated using the provided Micasense reflectance panel. The flight path was programmed to fly in autonomously, following manufacturer’s instructions (DJI). The dataset includes a shapefile with the GPS location of vine trunks, bunches affected by Botrytis and GCP points.</p>
UAV data of post fire dynamics, Quesenbank, Harz, 2022 (orthomosaics, topography, point clouds)
<p>Unoccupied aerial vehicles (UAVs) were used to investigate a burnt forest site situated in the Harz National Park area near the location Schierke, east of the Brocken, called Quesenbank (<a href="https://www.google.com/maps/place/51%C2%B046'07.6%22N+10%C2%B041'30.2%22E/@51.7682386,10.6905312,489m/data=!3m1!1e3!4m5!3m4!1s0x0:0xd8d7f64a3ff9acf1!8m2!3d51.76877!4d10.69173">51.76877 °N, 10.69173 °E</a>) which is a spruce stand stand severely affected by bark-beetle and windfall. Most of the trees are dead such that they provide high fuel loads for potentially occurring wildfires. The small river Wormke crosses the Quesenbank and separates the survey area between a hiking path and the forest stand.</p> <p>During the 12.08.2022, inhabitants reported a <a href="https://www.ndr.de/nachrichten/niedersachsen/braunschweig_harz_goettingen/Waldbrand-im-Harz-Polizei-geht-von-Brandstiftung-aus,waldbrand882.html">fire</a> near Schierke which was quickly contained by the authorities. Two months after the fire, on 13.10.2022, a team of scientists from GAU Göttingen and TU Berlin was accompanied by a National Park representative for investigation. The site was surveyed by using modern UAVs and by sampling soil strata, ash and vegetation. This report will briefly describe UAV-based surveys and the derived data products.</p> <p>For an overview, see the <strong>report </strong>or download the Maps.zip folder.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p><strong>Projekt ”Postfeuerdynamik auf Brandflächen im Nationalpark Harz”</strong></p> <p>Dr. Simon Drollinger, Georg-August Universität Göttingen</p> <p>Marlene Düngelhoef, Nationalparkverwaltung Harz</p> <p>Thomas Glinka, Nationalparkverwaltung Harz</p>
Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for FloodNet/10-class segmentation of RGB 768x512 UAV images
<p><em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for FloodNet/10-class segmentation of RGB 768x512 UAV images</strong></em></p> <p>These Residual-UNet model data are based on [FloodNet](https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021) images and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021</p> <p>Image size used by model: 768 x 512 x 3 pixels</p> <p><em>classes:</em><br> 1. Background<br> 2. Building-flooded<br> 3. Building-non-flooded<br> 4. Road-flooded<br> 5. Road-non-flooded<br> 6. Water<br> 7. Tree<br> 8. Vehicle<br> 9. Pool<br> 10. Grass</p> <p><em>File descriptions</em></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p>images.zip and labels.zip contain the images and labels, respectively, used to train the model</p> <p><em>References</em><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>** Rahnemoonfar, M., Chowdhury, T., Sarkar, A., Varshney, D., Yari, M. and Murphy, R.R., 2021. Floodnet: A high resolution aerial imagery dataset for post flood scene understanding. IEEE Access, 9, pp.89644-89654.</p>
Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for FloodNet/10-class segmentation of RGB 1024x768 UAV images
<p><em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for FloodNet/10-class segmentation of RGB 1024x768<strong> </strong>UAV images</strong></em></p> <p>These Residual-UNet model data are based on [FloodNet](https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021) images and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021</p> <p>Image size used by model: 1024 x 768 x 3 pixels</p> <p><em>classes:</em><br> 1. Background<br> 2. Building-flooded<br> 3. Building-non-flooded<br> 4. Road-flooded<br> 5. Road-non-flooded<br> 6. Water<br> 7. Tree<br> 8. Vehicle<br> 9. Pool<br> 10. Grass</p> <p><em>File descriptions</em></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p> </p> <p><em>References</em><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>** Rahnemoonfar, M., Chowdhury, T., Sarkar, A., Varshney, D., Yari, M. and Murphy, R.R., 2021. Floodnet: A high resolution aerial imagery dataset for post flood scene understanding. IEEE Access, 9, pp.89644-89654.</p>
Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology
<p>This dataset contains direct measurements of biomass burning emission factors for CO<sub>2</sub>, CO, CH<sub>4</sub> and N<sub>2</sub>O. It includes over 4500 EF bag measurements sampled using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems in Brazil, Australia, Botswana, Zambia, South-Africa and Mozambique under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. The table in the included word file explains the individual columns in the excell file. </p> <p> </p>
FlexiGroBots - Blueberry orchard UAV dataset August 2022 - raw data
<p>This data represents the UAV image acquisition from August 2022 in blueberry orchards located in Babe, Serbia.</p> <p>This is the first part of larger dataset, that contains raw images and orthomosaics generated using these images. Raw images are in 100FPLAN, 101FPLAN, and 102PLAN, while generated orthomosaics are in folder Raw orthomosaics August.</p> <p>Another dataset will be uploaded with preprocessed data titled in the similar manner.</p>
Orthophoto & DEM (MNE) issues d'images drone, UAV, Ermitage, Saint-Gilles, Réunion - 20230524 - 02_4
"Ce jeu de données présente les résultats des traitements photogrammétriques d'images de drone DJI Mavic 2 Pro UAV acquises sur le site de Ermitage, Saint-Gilles, Réunion à la date suivante : 20230524. <br>Les traitements ont été réalisés avec le logiciel OpenDroneMap à partir des images brutes fournies dans la première version de ce DOI. <br>Les vols ont été réalisés dans le but de créer des modèles numériques d'élévations pour cartographier la rugosité récifale du lagon jusqu'à la pente externe. Les données seront utilisées dans le cadre du projet TELEMAC. <br> <br><br>Le paramétrage du logiciel OpenDroneMap est partagé pour permettre la reproductibilité ou l'amélioration des traitements proposés:<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>Le dépôt est composé des éléments suivants:</b> <br> - 00_: Planche d'aperçu des images <br> - DCIM.zip: Images brutes issues du drone <br> - 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. RINEX et LLH issus d'un Emlid Reach M2 synchronisé avec la LED de navigation (événement envoyé dans le log du récepteur GNSS lors du déclenchement d'une image). Le but est de pouvoir réaliser un PPK (similaire au RTK en post-traitement) et ainsi disposer d'une position centrimétrique sur chaque image. <br> - METADATA.zip: Métadonnées au format ISO19115, Rapports avec miniatures des images de drone (dossier tb) et statistiques de vols. <br> - PROCESSED_DATA.zip: Orthophoto, DEM, nuages de points, ... <br> <br><b>Arborescence d'origine:</b> <br>│ └─ 20230524_REU-ermitage_UAV-02_4 <br>│-------- └─ DCIM <br>│-------- └─ GPS <br>│---------------- └─ base_2023_05_24_pascal <br>│---------------- └─ reach_2023_05_24_drone <br>│------------------------ └─ reachsylvai_raw_202305240249_RINEX_3_03 <br>│------------------------ └─ reachsylvai_raw_202305240330_RINEX_3_03 <br>│---------------- └─ reach_2023_05_24_rover <br>│-------- └─ METADATA <br>│---------------- └─ tb <br>│-------- └─ PROCESSED_DATA <br> <br><b>Informations de survol:</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: -70.00 <br> <br>- Survey informations: <br> No Images: 193 <br> Median height: 70 meters <br> Survey area: 4.16 hectares <br> Survey from: 2023:05:24 09:53:20 to: 2023:05:24 10:03:57 <br>"
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.