Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
401
datasets available to search
ShareScore release 0.7.1
Dataset results
401 results for “UAVs”
Wrack classification data based on UAV imagery from Dean Creek on Sapelo Island, GA
We used a DJI Matrice 210 UAV with a MicaSense Altum to collect a total of 20 images from January 2020 - December 2021 in a the Dean Creek marsh on Sapelo Island, GA. Wrack was classified using a principal component analysis. Wrack patches under 1 m2 were excluded from analyses. Wrack classifications were converted to polygon and point data where each point represents a 5 cm x 5 cm pixel. Those files were then used to analyze wrack characteristics, their relation to environmental drivers, and landscape based patterns. For both polygon and point data, we used the National Elevation Dataset (https://gdg.sc.egov.usda.gov/Catalog/ProductDescription/NED.html) to determine the elevation of each wrack patch. Creeks and shorelines were digitized and used to determine each wrack patches' distance to water. We calculated the frequency of wrack deposition at each point by adding together the number of images where that pixel was classified as wrack over the course of the study. Polygon data were related to tide height from a NOAA tidal station data product (Ft. Pulaski, Station 8670870; https://tidesandcurrents.noaa.gov) and wind speed and wind direction from the Marsh Landing weather station (downloaded data for the SAPMLMET met station from: https://cdmo.baruch.sc.edu/) to evaluate the relationship of wrack to environmental drivers.
UAV time series and tree crowns
<p>This dataset contains:</p><p>-A UAV time series of mosaicked images of a woodland in Northeast UK. Complete detaisl are given in: "Elias Fernando Berra, Rachel Gaulton, Stuart Barr, Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations, Remote Sensing of Environment, Volume 223, 2019, Pages 229-242, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2019.01.010." </p><p>-Manual (reference) and automatic delinetaed tree crowns for the area covered by the UAV time series data. Complete details in: Elias F. Berra. Individual tree crown detection and delineation across a woodland using leaf-on and leaf-off imagery from a UAV consumer-grade camera. Journal of Applied Remote Sensing, Vol. 14, Issue 3, 034501 (July 2020). https://doi.org/10.1117/1.JRS.14.034501</p>
Energy Cycle Characteristics for 5G/6G Networks Supported by RES, UAVs, and RISs
<h2><strong>Overview</strong></h2> <p>The following dataset presents the energy cycle characteristics for 5G/6G mobile systems supported by Renewable Energy Sources (RES) and/or Unmanned Aerial Vehicles (UAVs) and Reconfigurable Intelligent Surfaces (RISs). In addition, within the dataset, the energy gain related to the engagement of RES within the Radio Access Network (RAN) has also been distinguished.</p> <h2><strong>Scenario</strong></h2> <p>The considered network scenario includes 8 three- (<em>_results_gcas.csv</em>) or one-cell (<em>_results_scas.csv</em> & <em>_results_kras.csv</em>) base stations (BSs) placed within the Poznan city (surroundings of the old market) and supported by Renewable Energy Sources — photovoltaic panels (PVs) and/or wind turbines (WTs). The aforementioned base stations can be treated as stationary towers or mobile access points (e.g., drones/UAVs). Those latter have been additionally equipped with RIS devices, which are able to reflect and manipulate a radio signal to influence occurrences such as interferences, coverage, or human exposure. However, the use of RISs has been taken into account only to evaluate the impact of the engagement of such devices on the energy side of the mobile system, omitting the changes in radio characteristics. The network traffic has been assumed to be fixed (64 mobile users (UEs) with 100 Mbps downlink — DL, and 25 Mbps uplink — UL, per each), however, its density in specific parts of the city is modeled randomly for each simulation run. The simulation runs have been performed for 4 dates (vernal equinox, summer solstice, autumn equinox, winter solstice), each one from a different season of the year. The aim of such an approach was to highlight the impact of the time of the day and the year on the energy gain obtained thanks to enabling RES generators. The weather conditions assumed within the simulation are typical for the climate in Poland. </p> <h2><strong>Methodology</strong></h2> <p>The energy-cycle calculations (system's power consumption, renewable energy production, and excessive energy storage) have been based on the mathematical formulas from the scientific literature and performed within the digital simulation runs by using the Green Radio Access Network Design (GRAND) tool (developed by teams from the Ghent University & Poznan University of Technology). The UE-BS association process within the mobile system has been done by doing multi-objective optimization using the Gurobi software, which has taken into account parameters like path loss, predicted power consumption of BSs, and guaranteed DL & UL bit rates for UEs.</p> <h2><strong>Simulation setup</strong></h2> <p>The setup of the input parameters for used mathematical models (power consumption, energy generation, energy storage) has been done in accordance with the values attached within the delivered literature positions (cited within the publications included in the <em>Related works</em> section of the following dataset) and adjusted to the considered study. Furthermore, the data used to model the network environment (building distribution, coverage area, base stations' locations) as well as to predict weather conditions are the real data (for the year 2022) collected by the city hall of Poznan, one of the Polish mobile operators, and weather stations placed in Poznan, respectively. The number of simulation runs performed has been equal to 10 (each run has included energy-cycle calculations for 4 seasons of the year), with the time step of a single run set to 1 hour of the day.</p> <h2><strong>Results</strong></h2> <p>The results of the aforementioned investigations have been included in the attached files, which can be described as follows:</p> <h3><strong>File <em>_results_gcas.csv</em></strong></h3> <p>The first column denotes the date (season of the year), for which the values have been obtained. The columns from second to fifth present observed values of the State of Charge (SoC) of a battery system (in %) for a single network cell on average in a time step. Those columns are the obtained values for the RAN, in which no RES, only PVs, only WTs, and both types of RES generators have been enabled, respectively. </p> <h3><strong>Files <em>_results_scas.csv</em> & <em>_results_kras.csv</em></strong></h3> <p>The first column denotes the date (season of the year), for which the values have been obtained. The second and third columns denote the number of drone base station (DBS) exchanges within the wireless system on average in a particular time step, where no RES and only PVs are enabled, respectively. The fourth and fifth columns present the conventional (fossil-fuels-based) energy consumption (in kWh) for the whole system in a specific time step, in which no RES and only PVs are engaged for all the access nodes. The sixth column is the energy savings (in kWh) related to the use of RES generators within the mobile network. Furthermore, the seventh and eighth columns represent the amount of renewable energy harvested from the solar radiation in total and the peak value of this amount observed during the entire day, respectively.</p> <h2><strong>Acknowledgment</strong></h2> <p>More details about the conducted studies have been described within the attached papers (<em>Related works</em> section). The data has been collected within the COST CA10210 INTERACT. M. Deruyck is a Post-Doctoral Fellow of the FWO-V (Research Foundation – Flanders, ref: 12Z5621N). The work (including the following dataset preparation) by A. Samorzewski and A. Kliks was realized within project no. 2021/43/B/ST7/01365 funded by the National Science Center in Poland.</p>
Power Balance Characteristics for Multirotor- and Fixed-Wing-Type UAV-BSs Equipped with RES and RISs
<h2><strong>Overview</strong></h2> <p>The following dataset presents the power balance characteristics for Unmanned Aerial Vehicle Base Stations (UAV-BSs) equipped with Renewable Energy Sources (RES) and Reconfigurable Intelligent Surfaces (RISs). The dataset has been prepared for two different types of UAVs, i.e., multirotor and fixed-wing ones.</p> <h2><strong>Scenario</strong></h2> <p>The considered scenario includes 2 UAV-BSs (each of a different type) equipped with a single RF transceiver and an RIS device and RES — a single photovoltaic panel (PV) and a single wind turbine (WT). The UAV-BSs are placed within the city of Poznan and hover (multirotor) or follow a circular route (fixed-wing) above a single mobile user with fixed traffic demand (100 Mbps downlink — DL, and 50 Mbps uplink — UL). The simulation runs have been performed for 4 dates (vernal equinox, summer solstice, autumn equinox, winter solstice), each one from a different season of the year. The aim of such an approach was to highlight the impact of the time of the day and the year on the energy gain obtained thanks to enabling RES generators as well as on the power consumption of the hardware of each UAV-BS type. The weather conditions assumed within the simulation are typical for the climate in Poland.</p> <h2><strong>Methodology</strong></h2> <p>The power-balance calculations (UAV-BSs' power consumption, renewable energy production) have been based on the mathematical formulas from the scientific literature and performed within the digital simulation runs by using dedicated software developed in Python programming language.</p> <h2><strong>Simulation setup</strong></h2> <p>The setup of the input parameters for used mathematical models (power consumption, energy generation) has been done in accordance with the values attached within the literature positions (cited within the publication included in the <em>Related works</em> section of the following dataset) and adjusted to the considered study. Furthermore, the data used to predict weather conditions are the real data (for the year 2022) collected by the weather stations placed in Poznan. A single simulation run has been performed (which takes into account 2 types of UAV-BS simultaneously and estimates their power balance for 4 seasons of the year), where the time step has been set to 1 hour of the day.</p> <h2><strong>Results</strong></h2> <p>The results of the aforementioned investigations have been included in the attached files (<em>_power_balance_multirotor.csv</em> & <em>_power_balance_fixed_wing.csv</em>). The first column denotes the hour of a particular day. Next, 4 multicolumns have been presented for the following variants — No RES enabled, only PV enabled, only WT enabled, and both types of RES generators enabled. In addition, each multicolumn consists of 4 columns, each of which represents a UAV-BS's hardware power balance (in W) for a different date (season of the year).</p> <h2><strong>Acknowledgment</strong></h2> <p>More details about the conducted study have been described within the attached paper (<em>Related works</em> section). The work (including the following dataset preparation) was realized within project no. 2021/43/B/ST7/01365 funded by the National Science Center in Poland.</p>
UAV-based colour-infrared orthomosaics and digital elevation models of basalts and rock glaciers on Disko Island, West Greenland
<p><span>This data set contains multispectral surveys conducted with an unoccupied aerial vehicle over rock glaciers and steep mafic outcrops (intrusive and flood volcanics) near the coastline of Disko Island.</span></p> <ul> <li><span>Acquisition date: 07.08.2019 – 10.08.2019</span></li> <li><span>Location: Illukunnguaq, Disko Island, Greenland</span></li> <li><span>UAV: SenseFly eBee Plus</span></li> <li><span>Flight altitude above ground level: >100m</span></li> <li><span>Image Overlap forward/side: various</span></li> <li><span>Camera: Parrot Sequoia multispectral</span></li> <li><span>EPSG: 32622</span></li> <li><span>Center coordinates: 69.885277°N, -52.577724°E</span></li> <li><span>Flight mode: automatic flight plan</span></li> </ul> <p><span>Data products: </span></p> <ul> <li><span>Orthomosaic colour-infrared, 10-16 cm pixel resolution</span></li> <li><span>Colour-infrared spectral bands: 790nm, 660nm, 550nm</span></li> <li><span>DEM, 20-30cm pixel resolution</span></li> <li><span>Data coverage: approx. 5500 x 2500 m</span></li> <li><span>Elevation profile: 20-680m </span></li> <li><span>Processing in Agisoft Metashape</span></li> </ul> <p><span>Additional data supplement for article:<br>Barnes, E. (2020). Assessment of Drone-Borne Multispectral Mapping in the Exploration of Magmatic Ni-Cu Sulphides–an Example from Disko Island, West Greenland. <br><em>URN: urn:nbn:se:uu:diva-418858</em></span></p> <p>MULSEDRO field campaign was conducted under scientific survey licence (VU-00158-2019) within mineral exploration licence MEL 2018-16 by Blue Jay Mining PLC. This research has been supported by the project MULSEDRO, funded by HZDR-HIF & EITRawMaterials (project ID 16193) and the European Union.</p>
UAV-based orthomosaic and digital elevation model of a basalt outcrop on Disko Island, West Greenland
<p><span>This data set contains an RGB survey conducted with an unoccupied aerial vehicle (UAV) over a flat basaltic outcrop (intrusive and flood volcanics), surrounded by boreal vegetation (Salix species).</span></p> <ul> <li><span>Acquisition date: 13.08.2019</span></li> <li><span>Location: Qullissat (Qutdlikssat), Disko Island, Greenland</span></li> <li><span>UAV: DJI Mavic 1 Pro</span></li> <li><span>Flight altitude above ground level: 75 m</span></li> <li><span>Image Overlap forward/side: 70 % / 70 %</span></li> <li><span>Camera: RGB</span></li> <li><span>EPSG: 32622</span></li> <li><span>Center coordinates: 70.05330°N, -52.97780°E</span></li> <li><span>Flight mode: manual image acquisition</span></li> </ul> <p><span>Data products: </span></p> <ul> <li><span>Orthomosaic RGB 2.3 cm pixel resolution</span></li> <li><span>DEM 5cm pixel resolution</span></li> <li><span>Processing in Agisoft Metashape</span></li> <li><span>Data coverage: approx. 250 x 360 m</span></li> </ul> <p>Acknowledgements</p> <p><span>MULSEDRO field campaign was conducted under scientific survey licence (VU-00158-2019) within mineral exploration licence MEL 2018-16 by Blue Jay Mining PLC. This research has been supported by the project MULSEDRO, funded by HZDR-HIF & EITRawMaterials (project ID 16193) and the European Union.</span></p>
Orthophoto mosaic from UAV of a Pinna nobilis population within the Venice lagoon
<p>Orthophoto mosaic from a UAV survey on a tidal flat within the Venice lagoon (Italy) colonized by Pinna nobilis and Cymodocea nodosa. On June 23<sup>rd</sup>, 2020 at 06:05 a.m. (GMT) (07:05 solar local time) UAV images were collected at low tide with a DJI Zenmuse X4S camera (20 Mpixels, focal length 8.8 mm, 1-inch CMOS Sensor) mounted on a professional quadcopter DJI Matrice210v2. A total of 228 images were collected and the Agisoft Metashape Pro v 1.6.2 software was then used to produce an ortho-photo mosaic through Structure from Motion photogrammetric technique.</p>
The potential of low-cost UAVs and open-source photogrammetry software for high-resolution monitoring of alpine glaciers: A case study from the Kanderfirn (Swiss Alps)
<p>This dataset contains high-resolution orthophotos (5 x 5 cm) and digital surface models (25 x 25 cm) of the Kandernfirn Glacier located in the Swiss Alps. Aerial images were aquired with a self-developed fixed-wing Unmanned Aerial Vehicle during ten surveys on five different days in 2017 and 2018. The open-source photogrammetry software OpenDroneMap (version 0.4.1) was used for image processing.</p> <p>The orthophotos and digital surface models were validated through dGNSS point measurements of ground control points. Please refer to the corresponding paper for information on the horizontal and vertical accuracy of the files.</p>
Gummern UAV DEM 10cm (20230906)
<h2>Abstract</h2> <p>Digital elevation model, created from UAV survey, spatial resolution 10 cm, for evaluation of DEM, created from Pleiades Neo tri-stereo imagery.</p> <p>This depositry 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>Gummern_UAV_DEM_10cm_20230906</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Digital elevation model, created from UAV survey, spatial resolution 10 cm, for evaluation of DEM, created from Pleiades Neo tri-stereo imagery</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>digital surface model, UAV imagery</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>/</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>Elevation</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>2.12.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>2.12.2023</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>Fromat</p> </td> <td> <p>GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.10m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.05cm</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 25833</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>Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UL</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Gummern UAV DEM 10cm (20231018)
<h2>Abstract</h2> <p>Digital elevation model, created from UAV survey, spatial resolution 10 cm, for evaluation of DEM, created from Pleiades Neo tri-stereo imagery.</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>Gummern_UAV_DEM_10cm_20231018</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Digital elevation model, created from UAV survey, spatial resolution 10 cm, for evaluation of DEM, created from Pleiades Neo tri-stereo imagery</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>digital surface model, UAV imagery</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>/</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>Elevation</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>22.12.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>22.12.2023</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>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.10m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.05cm</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 25833</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>Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UL</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table> <p> </p>
FireMan-UAV-RGBT
<p>The FireMan-UAV-RGBT dataset is a collection of UAV-captured RGB and thermal videos aimed at improving wildfire detection methods in boreal forests. Captured during four controlled burns in Finland from 2022 to 2023, the dataset includes 34 RGB videos and 20 RGB-Thermal paired videos. These high-resolution images have been annotated using both manual and semi-automatic methods to ensure accuracy.</p> <p>Using advanced drone technology, including the DJI Matrice 30T and Matrice 300 with Zenmuse H20T and H20N cameras, the dataset provides essential visual and thermal data for early fire detection and management. The annotations, formatted in YOLO, support machine learning applications in wildfire detection.</p> <p>To validate the dataset, state-of-the-art deep learning models were trained, showing the reliability and applicability of the FireMan-UAV-RGBT dataset in real-world scenarios. This dataset is publicly available to support researchers in developing new methods for wildfire management.</p> <p><br><br><br></p>
Dataset on UAV RGB videos acquired over a vineyard property of Bodegas Terras Gauda at an early stage of Botrytis cinerea infection in 2021
<p>The videos were collected in a vineyard owned by Bodegas Terras Gauda, in June 2021. The videos were collected with a DJI Matrice 210 RTK UAV, which had a DJI Zenmuse X5S sensor onboard. A total of 4 rows were recorded with side videos. The flights were carried out on a sunny day with wind velocity lower than 0.5 m/s. Annotations of the grape clusters in the MOTS style are provided. </p>
Orthorectfied photo mosaics derived from UAV overflights of the fifteen NPP study sites at Jornada Basin LTER in 2019
This data package contains orthorectified mosaic photographs of each of the 15 NPP study sites at the Jornada Basin LTER in southern New Mexico, USA. The mosaics were derived from raw images collected during uncrewed aerial vehicle (UAV) overflight missions conducted in late summer and early autumn of 2019 (1 overflight day per site). For each site one or two missions were flown during an afternoon using a DJI Phantom 4 UAV, and between 450 and 1300 12.4 megapixel RGB images were captured. A subset of images captured at each site were loaded into Agisoft Metashape software to derive orthorectified photo mosaics. The raw images are not provided but can be made available via project PIs. This data package includes one centimeter resolution orthomosaic images of all 15 sites as geotiff raster files. Other derived products from these UAV missions, including digital elevation models, digital terrain models, and sparse point clouds, are available in other EDI data packages (knb-lter-jrn.210543002, knb-lter-jrn.210543003, and knb-lter-jrn.210543004, respectively). This study is complete.
Digital elevation models derived from UAV overflights of the fifteen NPP study sites at Jornada Basin LTER in 2019
This data package contains digital elevation models (DEMs) of each of the 15 NPP study sites at the Jornada Basin LTER in southern New Mexico, USA. The models were derived from raw images collected during uncrewed aerial vehicle (UAV) overflight missions conducted in late summer and early autumn of 2019 (1 overflight day per site). For each site one or two missions were flown during an afternoon using a DJI Phantom 4 UAV, and between 450 and 1300 12.4 megapixel RGB images were captured. A subset of images captured at each site were loaded into Agisoft Metashape software to derive digital elevation models using a structure from motion method. These models include elevations of vegetation and other aboveground features. The raw images are not provided but can be made available via project PIs. This data package includes one centimeter resolution DEMs of all 15 sites as geotiff raster files. Other derived products from these UAV missions, including orthomosaic photos, digital terrain models, and sparse point clouds, are available in other EDI data packages (knb-lter-jrn.210543001, knb-lter-jrn.210543003, and knb-lter-jrn.210543004, respectively). This study is complete.
Digital terrain models derived from UAV overflights of the fifteen NPP study sites at Jornada Basin LTER in 2019
This data package contains digital terrain models (DTMs) of each of the 15 NPP study sites at the Jornada Basin LTER in southern New Mexico, USA. The models were derived from raw images collected during uncrewed aerial vehicle (UAV) overflight missions conducted in late summer and early autumn of 2019 (1 overflight day per site). For each site one or two missions were flown during an afternoon using a DJI Phantom 4 UAV, and between 450 and 1300 12.4 megapixel RGB images were captured. A subset of images captured at each site were loaded into Agisoft Metashape software to derive DTMs using a structure from motion method. These models do not include the elevations of aboveground features such as vegetation and large rocks. The raw images are not provided but can be made available via project PIs. This data package includes one centimeter resolution DTMs of all 15 sites as geotiff raster files. Other derived products from these UAV missions, including orthomosaic photos, digital elevation models, and sparse point clouds, are available in other EDI data packages (knb-lter-jrn.210543001, knb-lter-jrn.210543002, and knb-lter-jrn.210543004, respectively). This study is complete.
Sparse point clouds derived from UAV overflights of the fifteen NPP study sites at Jornada Basin LTER in 2019
This data package contains sparse point clouds for each of the 15 NPP study sites at the Jornada Basin LTER in southern New Mexico, USA. The point clouds were derived from raw images collected during uncrewed aerial vehicle (UAV) overflight missions conducted in late summer and early autumn of 2019 (1 overflight day per site). For each site one or two missions were flown during an afternoon using a DJI Phantom 4 UAV, and between 450 and 1300 12.4 megapixel RGB images were captured. A subset of images captured at each site were loaded into Agisoft Metashape software to derive points using a structure from motion method. The raw images are not provided but can be made available via project PIs. This data package includes point clouds for all 15 sites as geotiff raster files. Other derived products from these UAV missions, including orthomosaic photos, digital elevation models, and digital terrain models, are available in other EDI data packages (knb-lter-jrn.210543001, knb-lter-jrn.210543002, and knb-lter-jrn.210543003, respectively). This study is complete.
Surface temperature mapped from thermal infrared survey from UAV campaign at Niwot Ridge, 2017.
Data collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment. Surface temperature of Niwot Ridge saddle was mapped from thermal infrared survey on June 21, July 11, 18, 25, and August 14, 2017.
Uncalibrated RGB orthomosaic imagery from UAV campaign at Niwot Ridge, 2017.
Uncalibrated RGB data were collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. The purpose of the project was to investigate snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Photogrammetric Point Cloud and DSM from UAV campaign at Niwot Ridge, 2017.
Elevation data from 14 August 2017 collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth varaibility and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Calibrated Red/Near Infrared orthomosaic imagery from UAV campaign at Niwot Ridge, 2017.
Red/Near Infrared data were collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. The purpose of the project was to investigate snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
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.