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.
533
datasets available to search
ShareScore release 0.9.0
Dataset results
533 results for “Aerial”
Historical digital elevation models (DEMs) and orthoimage mosaics for North American Glacier Aerial Photography (NAGAP) program, version 1.0
<p>This data archive contains digital elevation models (DEMs) and orthoimages generated from scanned historical aerial photographs from the North American Glacier Aerial Photography program available from the NSF Arctic Data Center (ADC, arcticdata.io). </p> <p>The scanned images were preprocessed using the <a href="https://github.com/friedrichknuth/hipp">Historical Image Pre-Processing</a> v0.1 software. Photogrammetric processing was performed with the <a href="https://github.com/friedrichknuth/hsfm">Historical Structure from Motion</a> v0.1 software. </p> <p>All DEM and orthoimage products are provided in the UTM Zone 10N (EPSG:32610) projected coordinate system. Elevation values are in meters above the WGS84 ellipsoid. </p> <p>See <a href="https://www.sciencedirect.com/science/article/pii/S0034425722004850">manuscript</a> and <a href="https://ars.els-cdn.com/content/image/1-s2.0-S0034425722004850-mmc1.pdf">supplement</a> for processing details and further dataset description.</p> <p>This release contains data products for two study sites in Washington state, USA:</p> <p><strong>Mount Baker</strong><br> 1970-09-09<br> 1970-09-29<br> 1974-08-10<br> 1977-09-27<br> 1979-10-06<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-09-15<br> 1992-09-18</p> <p><strong>South Cascade</strong><br> 1967-09-21<br> 1970-09-29<br> 1974-08-10<br> 1977-10-03<br> 1979-08-20<br> 1979-10-06<br> 1984-08-14<br> 1986-09-05<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-07-28<br> 1992-09-15<br> 1992-09-18<br> 1992-10-06<br> 1994-09-06<br> 1996-09-10<br> 1997-09-23</p> <p>The 00_thumbnails.jpg provides a quicklook overview at both sites.</p> <p><strong>The DEM and ortho file names are structured as follows:</strong><br> hsfm_NAGAP_[site-name]_[date]_[type].tif</p> <p><strong>For example:</strong><br> hsfm_NAGAP_south-cascade_19670921_ortho.tif</p> <p><strong>Where:</strong><br> [site-name] = Either mount-baker or south-cascade<br> [date] = Image acquisition date in YYYYMMDD format<br> [type] = File type</p> <p><strong>For each DEM and ortho pair, we provide the following:</strong><br> _1m_dem.tif = Digital elevation model posted at 1 m resolution <br> _ortho.tif = Orthoimage mosaic posted at the median image ground sample distance, rounded up to the nearest second decimal place.<br> _metadata.tar.gz = Metadata tarball containing:<br> _ortho_footprints.geojson = Orthoimage mosaic footprint polygons provided in GeoJSON format (EPSG:4326)<br> _dem_footprints.geojson = DEM footprint polygons provided in GeoJSON format (EPSG:4326)<br> _cameras.csv = Image file names, positions, and orientations</p>
An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery: Dataset
<p>This archive contains code and data to go with the paper <em>*An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery*</em>.</p> <p> </p> <p>This archive contains geospatial data, as well as the code used to generate the geospatial data.</p> <p>The geospatial data consists of georeferenced polygons identifying areas which are covered by green roofs in London (GBR) generated from 2019 aerial imagery.</p> <p>The data is described in detail in the manuscript <em>*An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery*</em>. See abstract below.</p> <p> </p> <p>GeoJSON format:</p> <p>GeoJSON is a format for encoding geospatial data, see https://geojson.org/.</p> <p>GeoJSON can be read using GIS programs including ArcGIS, QGIS, OGR.</p> <p> </p> <p>Contents:</p> <p>`geospatial_data/buffered_polygons_2021.zip` a zip archive containing a geojson file. It is the estimated locations of green roofs in London in 2021 and is the main result, which can be opened in any GIS program after being unzipped.</p> <p>`geospatial_data/buffered_polygons_2019.zip` a zip archive containing a geojson file. It is the estimated locations of green roofs in London in 2019 and is a secondary result, which can be opened in any GIS program after being unzipped. The predictions were made with the same model as the 2021 results.</p> <p>`geospatial_data/labelled_area.zip` a zip archive containing a geojson file. Identifies the area which was hand-labelled.</p> <p>`geospatial_data/manual_2021.zip` a zip archive containing a geojson file. Manually labelled green roof from 2021 imagery.</p> <p>`geospatial_data/manual_2019.zip` a zip archive containing a geojson file. Manually labelled green roof from 2019 imagery.</p> <p>`segmentation_code` contains the code used to produce the segmentation from the aerial imagery.</p> <p>`analysis_code` contains the code used to produce the plots and tables for the paper.</p> <p> </p> <p>Imagery availability:</p> <p>Unfortunately the aerial imagery and building footprint data cannot be shared directly, as you will require the proper license. Both can be found at [Digimap](https://digimap.edina.ac.uk) provided your institution has the license.</p> <p> </p> <p>Abstract:</p> <p>Green roofs can mitigate heat, increase biodiversity, and attenuate storm water, giving some of the benefits of natural vegetation in an urban context where ground space is scarce. To guide the design of more sustainable and climate resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is needed, but this information is currently lacking. Segmentation algorithms have been used widely to identify buildings and land cover in aerial imagery. Using a machine-learning algorithm based on U-Net to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset \cite[]{simpson_charles_2022_6861929}. We estimate that there was 0.23 km^2 of green roof in the Central Activities Zone (CAZ) of London, (1.07 km^2) in Inner London, and (1.89 km^2) in Greater London in the year 2021. This corresponds to 2.0% of the total building footprint area in the CAZ, and 1.3% in Inner London. There is a relatively higher concentration of green roofs in the City of London, covering 3.9% of the total building footprint area. Test set accuracy was 0.99, with an f-score of 0.58. When tested against imagery and labels from a different year (2019), the model performed just as well as a model trained on the imagery and labels from that year, showing that the model generalised well between different imagery. We improve on previous studies by including more negative examples in the training data, and by requiring coincidence between vector building footprints and green roof patches. We experimented with different data augmentation methods, and found a small improvement in performance when applying random elastic deformations, colour shifts, gamma adjustments, and rotations to the imagery. The survey covers 1558 km^2 of Greater London, making this the largest open automatic survey of green roofs in any city. The geospatial dataset is at the single-building level, providing a higher level of detail over the larger area compared to what was already available. This dataset will enable future work exploring the potential of green roofs in London and on urban climate modelling.</p>
Aerial Water Buoys Dataset
<p><strong>Aerial Water Buoys Dataset:</strong></p> <p>Over the past few years, a plethora of advancements in Unmanned Areal Vehicle (UAV) technologies have made possible advanced UAV-based search and rescue operations with transformative impact on the outcome of critical life-saving missions. This dataset aims into helping the challenging task of multi-castaway tracking and following using a single UAV. Due to the difficulty and data protection of capturing footage of people in the sea, we have captured a dataset of buoys in order to conduct experiments on multi-castaway tracking and following. A paper on multi-castaway tracking and following technical details and experiments will be published soon using this dataset.</p> <p>The dataset consists of top-view images of buoys from various altitudes on the coasts of Larnaca and Protaras in Cyprus. Images were captured at different altitudes in order to challenge object detectors to be able to detect smaller objects in case a UAV needs to track multiple targets, which leads to flying at a higher altitude. There is only one class annotated on all images which is labeled as 'buoy'. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (buoys):</p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>Buoys</td> </tr> <tr> <td>Training</td> <td>10814</td> <td>14811</td> </tr> <tr> <td>Validation</td> <td>1350</td> <td>1865</td> </tr> <tr> <td>Testing</td> <td>1352</td> <td>1827</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p> <p> </p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong></p> <blockquote> <p>Antreas Anastasiou, Rafael Makrigiorgis, & Panayiotis Kolios. (2022). Aerial Water Buoys Dataset (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7288444</p> </blockquote>
Unmanned Aerial Vehicle Image Dataset of the Built Environment for 3D reconstruction (UAVID3D)
<p>Unmanned Aerial Vehicles (UAV) provide increased access to unique types of urban imagery traditionally not available. Advanced machine learning and computer vision techniques when applied to UAV RGB image data can be used for automated extraction of building asset information and if applied to UAV thermal imagery data can detect potential thermal anomalies. However, these UAV datasets are not easily available to researchers, thereby creating a barrier to accelerating research in this area. </p> <p>To assist researchers with added data to develop machine learning algorithms, we present UAVID3D (Unmanned Aerial Vehicle (UAV) Image Dataset of the Built Environment for 3D reconstruction). The raw images for our dataset were recorded with a Zenmuse XT2 visual (RGB) and a FLIR Tau 2 (thermal, https://flir.netx.net/file/asset/15598/original/) camera on a DJI Mavic 2 pro drone (https://www.dji.com/matrice-200-series). The thermal camera is factory calibrated. All data is organized and structured to comply with FAIR principles, i.e. being findable, accessible, interoperable, and reusable. It is publicly available and can be downloaded from the Zenodo data repository. </p> <p>RGB images were recorded during UAV fly-overs of two different commercial buildings in Northern California. In addition, thermographic images were recorded during 2 subsequent UAV fly-overs of the same two buildings. UAV flights were recorded at flight heights between 60–80 m above ground with a flight speed of 1 m s and contain GPS information. All images were recorded during drone flights on May 10, 2021 between 8:45 am and 10:30 am and on May 19, 2021 between 2:15 pm and 4:30 pm. Outdoor air temperatures on these two days during the flights were between 78 and 83 degree fahrenheit and between 58 and 65 degree fahrenheit respectively. </p> <p>For the RGB flights, UAV path was planned and captured using an orbital flight plan in PIX4D capture at normal flight speed and overlap angle of 10 degree. Thermal images were captured by manual flights approximately 5 m away from each building facade. Due to the high overlap of images, similarities from feature points identified in each image can be extracted to conduct photogrammetry. Photogrammetry allows estimation of the three-dimensional coordinates of points on an object in a generated 3D space involving measurements made on images taken with a high overlap rate. Photogrammetry can be used to create a 3D point cloud model of the recorded region. UAVID3D dataset is a series of compressed archive files totaling 21GB. Useful pipelines to process these images can be found at these two repositories <a href="https://github.com/LBNL-ETA/a3dbr">https://github.com/LBNL-ETA/a3dbr</a>, and <a href="https://github.com/LBNL-ETA/AutoBFE">https://github.com/LBNL-ETA/AutoBFE</a></p> <p>This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Program, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. </p> <p> </p> <p> </p>
Doodleverse/Segmentation Gym SegFormer models for 2-class (wood, other) segmentation of RGB aerial orthomosaic imagery
<p><strong>Doodleverse/Segmentation Gym SegFormer models for 2-class (wood, other) segmentation of RGB aerial orthomosaic imagery</strong></p> <p>This model release is part of the Doodleverse: https://github.com/Doodleverse</p> <p>These Residual-UNet model data are based on RGB (red, green, and blue) images of alluvial river corridors and associated labels. Models are designed to identify subaerial accumulations of large woody debris in orthomosaic imagery. Models have been created using Segmentation Gym* using a dataset of images published here:</p> <p><em>Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC.</em></p> <p>Classes: {0=other, 1=large woody debris / driftwood}. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference</p> <p><strong>File descriptions</strong></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, </p> <p>1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU<br> 2. sample_images.zip contains a few example input files, for model testing</p> <p>3. example_validation_outputs.zip contain 50 example validation outputs, consisting of images of ground truth (right) and model output (left). This provides a visually interpretable product to assess model accuracy</p> <p> </p> <p>References</p> <p>*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>**<em>Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC.</em></p>
Doodleverse/Segmentation Gym Residual Unet models for 2-class (alluvial sediment, other) segmentation of RGB aerial orthomosaic imagery
<p><strong>Doodleverse/Segmentation Gym Residual Unet models for 2-class (alluvial sediment, other) segmentation of RGB aerial orthomosaic imagery</strong></p> <p>This model release is part of the Doodleverse: https://github.com/Doodleverse</p> <p>These Residual-UNet model data are based on RGB (red, green, and blue) images of alluvial river corridors and associated labels. Models are designed to identify subaerial alluvial sediment (sand, gravel, etc) in orthomosaic imagery. Models have been created using Segmentation Gym* using a dataset of images published here:</p> <p>Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC.</p> <p>Classes: {0=other, 1=sediment}. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference</p> <p>File descriptions</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, </p> <p>1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU<br> 2. sample_images.zip contains a few example input files, for model testing</p> <p>References</p> <p>*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>
Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery
<p><strong>Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery</strong></p> <p>This model release is part of the Doodleverse: https://github.com/Doodleverse</p> <p>These Residual-UNet model data are based on RGB (red, green, and blue) images of alluvial river corridors and associated labels. Models are designed to identify water, wood, sediment, and other in orthomosaic imagery. Models have been created using Segmentation Gym* using a dataset of images published here:</p> <p><em>Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC.</em></p> <p>Classes: {0=other, 1=water, 2=sediment, 3=large woody debris / driftwood}. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference</p> <p>File descriptions</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, </p> <p>1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU<br> 2. sample_images.zip contains a few example input files, for model testing</p> <p>References</p> <p>*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>
Doodleverse/Segmentation Gym SegFormer models for 2-class (other, sediment) segmentation of RGB aerial orthomosaic imagery
<p><strong>Doodleverse/Segmentation Gym SegFormer models for 2-class (other, sediment) segmentation of RGB aerial orthomosaic imagery</strong></p> <p>This model release is part of the Doodleverse: https://github.com/Doodleverse</p> <p>These Residual-UNet model data are based on RGB (red, green, and blue) images of alluvial river corridors and associated labels. Models are designed to identify water, wood, sediment, and other in orthomosaic imagery. Models have been created using Segmentation Gym* using a dataset of images published here:</p> <p><em>Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC.</em></p> <p>Classes: {0=other, 1=sediment}. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference</p> <p>File descriptions</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, </p> <p>1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU<br> 2. sample_images.zip contains a few example input files, for model testing</p> <p>References</p> <p>*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>
Georectified Mosaic of Aerial Images of Baltimore City in 1953
Landscape analyses are typically done using spatially explicit color aerial imagery. However, working with non-spatial black and white historical aerial photographs presents several challenges that require a combination of techniques and approaches. We analyzed 113 aerial images covering approx. 700 km2 (270 mi2) including all of Baltimore City, and a portion of Baltimore County surrounding the City. The images were taken between August 23rd 1952 and February 14th 1953. High-resolution scans were georeferenced and georectified against modern satellite imagery of the area and then combined to create a single raster mosaic. This process converted the images from a disparate set of photographs into a spatially explicit GIS data set that can be used to observe changes in land patches over time—and ultimately integrated with other long-term social, economic, and ecological data.
Kelp canopy chlorophyll to carbon ratio derived from aerial hyperspectral imagery
This dataset represents a time series of giant kelp canopy chlorophyll to carbon ratio (Chl:C) derived from aerial hyperspectral imagery, along with associated environmental, canopy age determinations, and validation datasets. Additional data from a frond cohort experiment are also presented, representing empirical observations of the decline in chlorophyll pigment concentration with blade age. This dataset contains eight geotiff rasters showing canopy Chl:C at a 30-meter pixel resolution for giant kelp forests in the Santa Barbara Channel during the months of April, June, and August from 2013 – 2015.
Aerial view of part of the Bisti badlands from an elevation of approximately 8500 feet. Exposed here are the Upper Cretaceous Fruitland and Kirtland Formations. Photograph taken the morning of 13 April 1992. Copyright © Paul L. Sealey. 1992. in Stratigraphy, paleontology and age of the Fruitland and Kirtland Formations (upper Cretaceous), San Juan Basin, New Mexico
Aerial view of part of the Bisti badlands from an elevation of approximately 8500 feet. Exposed here are the Upper Cretaceous Fruitland and Kirtland Formations. Photograph taken the morning of 13 April 1992. Copyright © Paul L. Sealey. 1992.
AIDER (Aerial Image Dataset for Emergency Response Applications)
<p><strong>AIDER </strong>(<strong>A</strong>erial <strong>I</strong>mage <strong>D</strong>ataset for <strong>E</strong>mergency <strong>R</strong>esponse applications): The dataset construction involved manually collecting all images for four disaster events, namely Fire/Smoke, Flood, Collapsed Building/Rubble, and Traffic Accidents, as well as one class for the Normal case.</p> <p>The aerial images for the disaster events were collected through various online sources (e.g. google images, bing images, youtube, news agencies web sites, etc.) using the keywords ”Aerial View” or ”UAV” or”Drone” and an event such as Fire”,”Earthquake”,”Highway accident”, etc. Images are initially of different sizes but are standardized prior to training. All images where manually inspected to first contain the event that was of interested and then to have the event centered at the image so that any geometric transformations during augmentation would not remove it from the image view. During the data collection process the various disaster events were captured with different resolutions and under various condition with regards to illumination and viewpoint. Finally, to replicate real world scenarios the dataset is imbalanced in the sense that it contains more images from the Normal class.</p> <p>This subset includes around 500 images for each disaster class and over 4000 images for the normal class. This makes it an imbalanced classification problem.</p> <p>It is advised to further enhance the dataset that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p>
Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>.</p> <p>The R code was written for R version 3.6.3.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p>
Drone aerial imagery of macroalgae growing in a small embayment at the western end of the Nuussuaq Peninsula in Northwest Greenland
<p>Aerial images of a small embayment at the western tip of the Nuussuaq Peninsula (NW Greenland) were acquired on 10 August 2019 using a DJI Phantom 3 Standard quadcopter drone. The imagery was acquired during the Vaigat Iceberg-Microbial Oil Degradation and Archaeological Heritage Investiga- tion (VIMOA) research cruise. Overlapping images were acquired during two consecutive flights using the 'interval' setting with a period of 5 seconds in the DJI Go app. The drone was flown manually. This drone survey was carried out with two primary objectives in mind: 1) Identify and quantify the macroalgae growing on the coast; 2) Identify any archaeological and cultural heritage sites that may be present. Images were processed using Agisoft PhotoScan Pro (v1.4.4; Linux Ubuntu). Images were aligned using the script CARMA_PhotoScan_Align.py (see this script for settings). After image alignment, the bounding box was visually inspected and the sparse point cloud was thinned using thresholds for the reconstruction uncertainty, projection accuracy, and reprojection error (gradual selection). The remaining tie points in the sparse cloud were then used to build the dense cloud. The settings used for this step can be found in the script CARMA_PhotoScan_GS_DC_v2.py. Next, the dense point cloud was used to generate a digital elevation model (DEM) and, subsequently, an orthomosaic. The Agisoft PhotoScan processing report is also included.</p>
Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil
<p>Title:</p> <p>Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170711</p> <p>Time of day (BRT = -3)</p> <p>10h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p>Breunig, Fábio Marcelo (2017, July 7). Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4327943</p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil
<p>Title:Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170707</p> <p>Time of day (BRT = -3)</p> <p>14h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High-speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p>Title:</p> <p> </p> <p>Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170707</p> <p>Time of day (BRT = -3)</p> <p>14h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p>
Drone-based aerial imagery of rivers, wetlands and agricultural systems in Zambia
<p>Unmanned aerial vehicle (UAV) imagery of rivers, wetlands and agricultural systems across Zambia. Collected during two flying seasons in March and September 2018, with a total of 122 scenes at 48 sites.</p> <p>All imagery is made available on OpenAerialMap (<a href="https://map.openaerialmap.org">https://map.openaerialmap.org</a>), a set of tools for searching, sharing, and using openly licensed satellite and UAV imagery. Detailed description of the dataset provided in a .csv file.</p>
Predictive Control of Aerial Swarms in Cluttered Environments
<p>This repository contains all data that has been used to produce the results contained in the submission to Nature Machine Intelligence titled "Predictive Control of Aerial Swarms in Cluttered Environments". It also contains the code necessary for plotting simulation and hardware experimental data.</p> <p>For the code used to run simulation and hardware experiments, please visit <a href="http://doi.org/10.5281/zenodo.4379503">doi.org/10.5281/zenodo.4379503</a>.</p>
Dataset from: Tolerance to aerial exposure influences distributional patterns in multi-species intertidal seagrass meadows
<p>This is the dataset for an article published in Marine Environmental Research titled, 'Tolerance to aerial exposure influences distributional patterns in multi-species intertidal seagrass meadows', in October 2023. Following is the abstract for the paper for which this was the primary data:</p><p>Multi-specific seagrass meadow assemblages dominate most tropical intertidal regions but the relative role of environmental stress in determining distribution patterns is still uncertain. Here we combine observational and experimental approaches to examine aerial exposure as a factor driving species occurrence patterns in intertidal meadows of the Andaman archipelago, where up to 6 seagrass species co-occur. In the studied meadow, patterns of exposure did not map onto distance from the coast, instead creating a patchy matrix of exposure, based on fine-scale bathymetric differences. Distributional surveys showed that seagrass species were similarly patchy, often tracking the degree of aerial exposure during low tide. While some species (<i>Halophila ovalis, Halophila minor,</i> and <i>Thalassia hemprichii</i>) frequently occurred in submerged or subtidal areas and were rarely found in completely exposed areas, other species (<i>Cymodocea rotundata</i>, <i>Halophila beccarii,</i> and <i>Halodule uninervis</i>) also occupied areas that were subject to partial or complete aerial exposure during low tide. To confirm this pattern, we used field-based transplant experiments, employing a natural gradient of tidal exposure to subject six seagrass species to different desiccation exposure times. After a month, <i>H. beccarii</i> and <i>H. uninervis</i> transplants survived in areas that sustained more than 3 h of aerial tidal exposure without significant mortality, compared with other species (<i>H. ovalis, H. minor, T. hemprichii, C. rotundata</i>) that showed dramatic shoot mortality at the same exposure regimes. For all species, 4 h represented the upper limit of exposure, in both experimental and distributional studies. However, despite their wider tolerance of exposure to air, <i>H. beccarii</i> and <i>H. uninervis</i> did not dominate the entire meadow. This could be a result either of their poor tolerance to other environmental factors or their lower competitive abilities among other mechanisms. This suggests that in tropical multi-specific meadows, strong environmental filters could override clear intertidal zonation to create patchy matrices based on species tolerances.</p>
Data and code from: Unoccupied aerial systems adoption in agricultural research
<div> </div> <p>This repository contains data and code supporting the findings of the study on the adoption of Unoccupied Aerial Systems (UAS) in agricultural research as reported by Lachowiec et al (2024) in The Plant Phenome Journal.</p> <p>We collected data through an online survey as well as through in person interviews.</p> <div> <h2>Description of Repository Contents</h2> </div> <div> <h3>Data</h3> </div> <p>Data are in the <code>/data</code> directory:</p> <ul> <li><code>Ag_Drones_Codebook_14Jun2023.pdf</code>: Codebook providing detailed descriptions of survey questions and coding schemes. This contains detailed descriptions of the content of the two CSV files listed below.</li> <li><code>Results_Ag_Drones_2021_Survey.csv</code>: This is raw survey data collected from agricultural researchers regarding their use of UAS technology.</li> <li><code>countries_code.csv</code>: Country codes used in the survey data for respondent location.</li> <li><code>interviews/</code>: A directory containing interview transcripts and summary provided as both Microsoft Word and plain text (Markdown) formats, specifically: <ul> <li>Notes from nine one on one interviews named <code><interviewee last name>)UAS_Interview.[md|docx]</code></li> <li>A summary document, <code>Feldman_AG2PI_InterviewSummary_2022-08-10.docx</code>.</li> </ul> </li> </ul> <div> <h3>Code</h3> </div> <p>Code used to process data and generate the manuscript's analysis and figures.</p> <ul> <li><code>data_code.R</code>: R Script for preprocessing and cleaning the survey data.</li> <li><code>dataAnalysis.R</code>: R script for statistical analysis and visualization of survey results.</li> </ul> <div> <h2>Citing this work</h2> </div> <p>This repository contains data and code to support the manuscript:</p> <blockquote> <p>Lachowiec, J., Feldman, M.J., Matias, F.I., LeBauer, D., Gregory, A. (2024). Unoccupied aerial systems adoption in agricultural research. Zenodo. The Plant Phenome Journal Volume(Issue), pages 00. doi:DOI</p> </blockquote> <p>If you use the data or code from this repository, please also cite:</p> <blockquote> <p>Lachowiec, J., Feldman, M.J., Matias, F.I., LeBauer, D., Gregory, A. (2024). Data and code from: Unoccupied aerial systems adoption in agricultural research. Zenodo. doi:10.5281/zenodo.10573428</p> </blockquote> <p>And consider contributing cleaned data and code to this repository.</p> <div> <h2>Acknowlegements and Support</h2> </div> <p><strong>Acknowledgments</strong></p> <p>We thank all survey respondents for their participation. We acknowledge the Montana State University HELPS lab for aiding in the development and implementation of the survey.</p> <p><strong>Funding</strong></p> <p>This research was supported by the intramural research program of the U.S. Department of Agriculture, National Institute of Food and Agriculture, Agricultural Genome to Phenome Initiative (2020-70412-32615 and 2021-70412-35233). The findings and conclusions in this preliminary presentation have not been formally disseminated by the U. S. Department of Agriculture and should not be construed to represent any agency determination or policy.</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.