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34 results for “aerial imagery”

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zenodo36/100

Quantifying pine processionary moth defoliation in a pine-oak mixed forest using unmanned aerial systems and multispectral imagery (dataset, paper published in PLOS ONE)

Data processed to analyze pine processionary moth defoliation. Digital surface model and orthomosaics derived from UAS

opencc-by-4.0Jan 2019View details →

zenodo36/100

Building Object and Outdoor Scene Segmentation (BOOSS) - Multi-channel (RGB + Thermal) Aerial Imagery Datasets

The dataset of Building Object and Outdoor Scene Segmentation (BOOSS) is based on multi-channel aerial imagery data. It covers  - Ground Truth - RGB - Thermal The annotations in version 1.0 include roofs, facades, cars, roof equipment, and ground equipment Please cite as: Hou, Yu, Meida Chen, Rebekka Volk, and Lucio Soibelman. "An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets." Remote Sensing 13, no. 21 (2021): 4357.

openJul 2021View details →

zenodo36/100

Automated processing of aerial imagery for geohazards monitoring: Results from Fagradalsfjall eruption, SW Iceland, August 2022

1- Dataset Summary Here we present a dataset of DEMs (Digital Elevation Models), orthomosaics, and lava area outlines for the August 2022 eruption at Fagradalsfjall, SW Iceland. The dataset consists of: (1) five aerial surveys collected over the course of the August 2022 Fagradalsfjall eruption, (2) one survey carried out on 14 August 2022 using Pléiades satellite stereo images, and (3) a larger aerial survey, covering the 2021 and 2022 eruption sites in late September 2022 after the volcanic activity concluded. 2- Background The volcano at Fagradalsfjall, SW-Iceland, began erupting on 3 August 2022 at 13:20 following 10 months of quiescence. As part of the response plan, a series of photogrammetric surveys were conducted in rapid, operational mode throughout the duration of the eruption. Subsequent production of data products for natural hazards monitoring (lava maps, lava volumes, effusion rates) were calculated within hours and reported to the Icelandic Civil Defense, following a similar approach that described in Pedersen et al., 2022a and in Gouhier et al., 2022. At the start of the 2022 eruption, GCPs had not yet been placed around the new fissure, but reference data (orthomosaics and DEMs) which had been georeferenced using targets measured with differential GNSS existed of the eruption site from September 2021 from Pedersen et al. (2022b) were available to use as a reference in the new workflow instead of GCPs. Due to the urgent need from authorities for information about the new eruption, a processing method that avoids the time-consuming task of manual GCP selection using a reference image for georeferencing was preferable in this instance. Besides the acquisition of aerial photographs, the CIEST2 initiative was also re-activated to collect Pléiades stereo images in emergency mode (Gouhier et al., 2022). 3 – Overview of data collection Table 1 contains the overview of the surveys collected and presented in this repository.                   Table 1. Summary of surveys included in this dataset, by survey date. <table align="center"> <tbody> <tr> <td> Date & Time YYYYMMDD HH:MM </td> <td> Sensor </td> <td> Platform </td> <td> Flight alt. (m asl) </td> <td> Images </td> <td> Surveyed km2 </td> </tr> <tr> <td> 20220803 17:05 </td> <td> A6D </td> <td> TF-203* </td> <td> ~ 850 </td> <td> 46 </td> <td> 4 </td> </tr> <tr> <td> 20220804 11:00 </td> <td> A6D </td> <td> TF-203 </td> <td> ~ 2100 </td> <td> 32 </td> <td> 35 </td> </tr> <tr> <td> 20220813 09:00 </td> <td> A6D </td> <td> TF-203 </td> <td> ~ 750 </td> <td> 123 </td> <td> 9 </td> </tr> <tr> <td> 20220814 13:00 </td> <td> Pléiades </td> <td> PHR1B </td> <td> n/a </td> <td> 2 </td> <td> 14 </td> </tr> <tr> <td> 20220815 08:15 </td> <td> A6D </td> <td> TF-203 </td> <td> 2100 </td> <td> 20 </td> <td> 23 </td> </tr> <tr> <td> 20220816 10:06 </td> <td> A6D </td> <td> TF-203 </td> <td> 2100 </td> <td> 19 </td> <td> 26 </td> </tr> <tr> <td> 20220926 12:00 </td> <td> A6D </td> <td> TF-BMW** </td> <td> 2100 </td> <td> ~20 </td> <td> 18 </td> </tr> </tbody> </table> * TF-203: Savannah S aircraft ** TF-BMW: Vulcanair P68 Observer 2 aircraft, operated by Garðaflug ehf. 4- Methods 4.1 Processing of the aerial photographs from 3-16 Aug 2022 Throughout the eruption, aerial surveys were conducted using a Hasselblad A6D 100 MP camera with 35 mm focal lens, from a height of 750 – 2,100 m above ground over the active lava field from an ultralight aircraft with a window in the bottom to allow for vertical photos to be taken (see supplement of Pedersen et al., 2022a for details and images of the setup). The camera was manually triggered to give ~70% overlap, and approximate flight lines were prepared beforehand for use with a handheld GPS during the flight to give ~30 % side overlap. An automated processing pipeline was created in python, which leverages tools from the Ames Stereo Pipeline (ASP, Shean et al., 2016) and Agisoft Metashape stand-alone Python API (v. 1.8.4). The processing and georeferencing of the aerial data were done in three steps, with all steps being automated except for the digitization of lava outlines. First, using a very high-resolution reference orthomosaic and DEM created in September 2021 and georeferenced with ground control points (Pedersen et al., 2022b), interest points (IPs) in each image were matched with the reference dataset, using the ASP routine ipfind. This created GCPs for each image over stable terrain. Second, hillshades were created from both the reference DEM and the source dataset DEM and matches in IPs were found in both, creating a second round of ground control points to refine the georeferencing of the entire block. Finally, the alignment of the source DEM was refined using the dem_align (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM. 4.2 Processing of the Pléiades stereo images The Pléiades stereo images were processed using the Ames Stereo Pipeline, using the general workflow of mapproject and parallel_stereo (e.g., Deschamps-Berger et al., 2020). The parallel_stereo routine used default arguments, plus the following arguments: --stereo-algorithm asp_mgm -t rpcmaprpc --corr-seed-mode 3 --corr-max-levels 2 --cost-mode 3 --subpixel-mode 9 --corr-kernel 7 7 --subpixel-kernel 15 15 We used the DEM from 4 Aug 2022 as the reference for mapproject and for the final DEM co-registration applied to the produced Pléiades DEM. 4.3 Processing of the 26 September 2022 dataset The survey from 26 September 2022 was collected and processed using direct georeferencing from an on-board GPS antenna. The final alignment of the block was refined using the dem_align (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM. Because this survey covered a much larger area, the reference DEM for the final coregistration was the <a href="https://atlas.lmi.is/mapview/?application=DEM">ÍslandsDEM </a>v.1.0 (Landmælingar Íslands, 2022). 4.4. Maps of the lava outlines, lava thickness, lava volume, Time Average Effusion Rate (TADR) For each survey, a differential DEM (dDEM) showing elevation changes since the 2021 eruption was created by subtracting the reference DEM (ÍslandsDEM v.1.0, which includes the post-eruption DEM from Pedersen et al., 2022a) from the source DEM. Lava outlines, lava thickness lava volume, TADR and uncertainties were calculated using the methods described in Pedersen et al., 2022a. Table 2 summarizes calculations from this dataset.   Table 2. Summary of survey results calculated from August 2022 Fagradalsfjall eruption DEMs and orthomosaics. <table align="center"> <thead> <tr> <th>  Date Start </th> <th> Date End </th> <th> Time Difference </th> <th> Lava Area* End (km2) </th> <th> dh** (m) </th> <th> Volume+ End (1e+6 m3) </th> <th> TADR++ (m3/s) </th> </tr> </thead> <tbody> <tr> <td> 20220803 13:20 </td> <td> 20220803 17:05 </td> <td> 0d 03h 45m </td> <td> 0.07 </td> <td> 5.88 </td> <td> 0.43 ± 0.03 </td> <td> 32.1 ± 1.5 </td> </tr> <tr> <td> 20220803 17:05 </td> <td> 20220804 11:00 </td> <td> 0d 21h 40m </td> <td> 0.14 </td> <td> 11.13  </td> <td> 1.57 ± 0.05 </td> <td> 17.7 ± 0.8 </td> </tr> <tr> <td> 20220804 11:00 </td> <td> 20220813 09:00 </td> <td> 8d 22h 00m </td> <td> 1.27# </td> <td> 6.90 </td> <td> 10.33 ± 0.6 </td> <td> 11.4 ± 0.7 </td> </tr> <tr> <td> 20220813 13:08 </td> <td> 20220814 13:00 </td> <td> 0d 23h 52m </td> <td> 1.24 </td> <td> 7.28 </td> <td> 10.62 ± 0.70 </td> <td> 2.8 ± 0.8 </td> </tr> <tr> <td> 20220814 13:00 </td> <td> 20220815 08:15 </td> <td> 0d 19h 15m </td> <td> 1.26 </td> <td> 7.46 </td> <td> 10.99 ± 0.55 </td> <td> 4.1 ± 0.8 </td> </tr> <tr> <td> 20220815 08:15 </td> <td> 20220816 10:16 </td> <td> 1d 2h 01m </td> <td> 1.28 </td> <td> 7.49 </td> <td> 11.13 ± 0.53 </td> <td> 2.0 ± 0.7 </td> </tr> <tr> <td> 20220816 10:16 </td> <td> 20220821 06:00## </td> <td> 4d 19h 44m </td> <td> 1.28 </td> <td> 7.69 </td> <td> 11.39 ± 0.44 </td> <td> 0.653 ± 0.10 </td> </tr> </tbody> </table> *Total area of the lava field since 2022-08-03 before activity started. **dh end is the mean thickness of the lava flow-field in the end of the given period. +Volume erupted since 2022-08-03 before activity started. ++Time-averaged discharge rate for the given period #Extrapolated value. Survey does not cover entire active lava area. ##End Time: 21 August 2022, 6:00. This time deduced from field observations from members of the Institute of Earth Sciences, University of Iceland. Values calculated from 26 September 2022 dataset. Figures and visual summaries of the processing method, resulting lava volumes, and uncertainties can be found in this poster: <a href="https://ftp.lmi.is/stm/Sydney/Fagradalsfjall_Aug2022/poster_faf_automated_proc2_srg_2022.pdf">Fagradalsfjall August 2022</a>. Orthomosaics from this dataset are viewable online at: <a href="https://atlas.lmi.is/mapview/?application=umbrotasja">https://atlas.lmi.is/mapview/?application=umbrotasja</a> Data naming conventions: <ul> <li>Data type: DEM, ortho, outline, diffDEM, lavafree, lava</li> <li>Acquisition date: YYYYMMDD_HHMM</li> <li>Platform/Sensor for data collection: Pléiades (PLE), Hasselblad A6D from aircraft (A6D)</li> <li>Resolution: 2x2 m (DEMs) and 30x30 cm (Orthomosaics)</li> <li>Folders (by survey): YYYYMMDD_HHMM_platform (during eruption) or 'posteruption'_platform (sensors/platform: A6D or PLE)</li> </ul> Data Specifications: <ul> <li>Cartographic projection: ISN93 / Lambert 1993 (EPSG:3057, <a href="http://https:/epsg.io/3057">https://epsg.io/3057</a>)</li> <li>Origin of Elevation: meters above GRS80 ellipsoid (WGS84)</li> <li>Raster data format: GeoTIFF</li> <li>Raster compression system: ZSTD (<a href="http://facebook.github.io/zstd/">http://facebook.github.io/zstd/</a>)</li> <li>Vector data format: GeoPackage (<a href="https://www.geopackage.org/">https://www.geopackage.org/</a>)</li> <li>Pléiades dataset includes only DEMs because the Pléiades ortho imagery is for licensed use only. Please contact the authors for further information on this.</li> </ul>

openncgl-uk-2.0Mar 2023View details →

dryad36/100

Ground and aerial imagery dataset for strawberry breeding trials: Training deep learning models for runner detection and segmentation

Open the record for dataset details and reuse information.

publicSep 2025View details →

zenodo32/100

Estimating belowground carbon stocks in isolated wetlands of the Northern Everglades Watershed, central Florida, using ground penetrating radar (GPR) and aerial imagery

This data set includes raw GPR profiles for isolated wetlands in the Disney Wilderness Preserve (Kissimmee, FL) for the purpose of below ground soil C stock estimations.

opencc-by-4.0Aug 2017View details →

zenodo32/100

Generating aerial flood prediction imagery

<h1>Flood Generation Dataset</h1> <h2>Dataset Overview</h2> The dataset contains paired sets of pre- and post-flooding satellite images, which can be used for training models on flood prediction tasks, particularly on generating photorealistic visualisations of floods. Each image is also associated with topographical factors, which can be used to assist the model in making predictions. These factors are a digital elevation model (DEM), flow accumulation, distance to rivers, and cartographical map. Every image set was manually verified and adjusted, in order to ensure perfect pixel alignment, where each pixel represents the same 0.5m x 0.5m geographical area in all of the factors. The dataset contains 1229 unique image sets, 235 of Hurricane Florence, 335 of Hurricane Harvey, 147 of the Midwest floods, and 512 of the India monsoon. 408 of the image sets (from Hurricane Harvey and some of the Midwest floods) have an additional alternative version with a 1m/pixel resolution DEM. <h2>Dataset Structure</h2> The dataset input folder contains images with the pre-flooding satellite image and topographical factors represented in 9 stacked channels (see the table below) and stored as a .tif file. The dataset output folder contains the post-flooding satellite image, stored as a .tif file. The name of the file contains which disaster it represents. <table> <tbody> <tr> <td>Channel</td> <td>Contents</td> </tr> <tr> <td>0, 1, 2</td> <td>Pre-flooding satellite image</td> </tr> <tr> <td>3</td> <td>DEM</td> </tr> <tr> <td>4</td> <td>Flow accumulation</td> </tr> <tr> <td>5</td> <td>Distance to rivers</td> </tr> <tr> <td>6, 7, 8</td> <td>Cartographical map</td> </tr> </tbody> </table> <h2>Dataset Contents</h2> <h3>Pre- and post satellite images.</h3> Each image has 1024x1024 pixels in a three-band RGB format, with a resolution of approximately 0.5 metres/pixel. The satellite images were extracted from the xBD dataset, captured by the Maxar Open Data Program. It was released under the Creative Commons Attribution-Noncommercial-Sharealike 4.0 International licence (CC BY-NC-SA 4.0).  <h3>Digital elevation model and flow accumulation</h3> The USGS 3D Elevation Program DEM, which has a 1/3 arc-second (10 metre) resolution, describes the topography of the images within the USA. The elevations in this DEM represent the topographic bare-earth surface. The Copernicus GLO-30 DEM, which has a resolution of only 30 metres, was used for the flood areas in India. The flow accumulation was calculated from the DEMs. The USGS 3D Elevation Program DEM is released by the U.S. Geological Survey, 2023, 1/3rd arc-second Digital Elevation Models (DEMs) - USGS National Map 3DEP Downloadable Data Collection. All 3DEP products are public domain. The Copernicus Global Digital Elevation model was produced using Copernicus WorldDEM-30 DLR e.V. 2010-2014 and Airbus Defence and Space GmbH 2014-2018, provided under COPERNICUS by the European Union and ESA. <h3>Distance to rivers and cartographical map</h3> OpenStreetMap data was downloaded from Planet OSM and processed using the Osmium tool. The Maperitive software was then used to apply a custom ruleset to the maps' appearance, removing all of the text, and enhancing the clarity of the land use types. The distance to rivers representation was producing by creating buffer distances (at 0.5km intervals) to all major rivers and waterways, as classified by the Open Street Map. The OpenStreetMap (OSM) data is distributed under the Open Database License (ODbL). https://www.openstreetmap.org/copyright <h1>Flood Segmentation Dataset</h1> <h2>Dataset Overview</h2> In order to evalate the flood predictive accuracy of post-flooding images generated by a model that has been trained on the flood generation dataset, the synthetic and ground truth post-flooding images can be compared using a flood segmentation model trained on this dataset. A post-flooding satellite image is input into the the segmentation model, and it then classifies whether each pixel is flooded or not, and outputs a 1-channel binary flood mask, where white (1) corresponds to a predicted flooded pixel, and black (0) corresponds to a predicted non-flooded pixel. The flood masks corresponding to both real and synthetic images can then be compared using metrics such as MSE, precision, recall, etc, thus determining whether the generative model correctly flooded the same areas that are actually flooded in the real ground truth image. <h2>Dataset Structure</h2> The flood segmentation dataset contains 1028 images, depicting the same disasters as in the flood generation dataset. The masks input folder contains 256x256 pixel 3-channel RGB post-flooding .tif images, which are comprised of a mix of ground truth and synthetic images generated by different GAN architectures. The masks output folder contains the corresponding 1 channel binary flood masks. 332 of the flood masks were published openly by: https://huggingface.co/datasets/blutjens/eie-earth-intelligence-engine. The rest of the masks were manually labelled by myself.

opencc-by-nc-sa-4.0Aug 2024View details →

dryad32/100

An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research

Collection of multispectral imagery from an aerial sensor is a means to obtain plot-level vegetation index (VI) values; however, post-capture image processing and analysis remain a challenge for small-plot researchers. An ArcGIS Pro workflow of two task items was developed with established routines and commands to extract plot-level VI values (Normalized Difference VI, Ratio VI, and Chlorophyll Index-Red Edge) from multispectral aerial imagery of small-plot turfgrass experiments. Users can access and download task item(s) from the ArcGIS Online platform for use in ArcGIS Pro. The workflow standardizes the processing of aerial imagery to ensure repeatability between sampling dates and across site locations. A guided workflow saves time with assigned commands, ultimately allowing users to obtain a table with plot descriptions and index values within a .csv file for statistical analysis. The workflow was used to analyze aerial imagery from a small-plot turfgrass research study evaluating herbicide effects on St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] grow-in. To compare methods, index values were extracted from the same aerial imagery by TurfScout, LLC and were obtained by handheld sensor. Index values from the three methods were correlated with visual percentage cover to determine the sensitivity (i.e., the ability to detect differences) of the different methodologies.

opencc-zeroJan 2023View details →

dryad32/100

An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research

Open the record for dataset details and reuse information.

publicJan 2023View details →

zenodo28/100

Enhancing Regional Quasi-Geoid Refinement Precision: An Analytical Approach Employing ADS80 Tri-linear Array Stereoscopic Imagery for Aerial Triangulation Densification and GNSS Gravity-Potential Leveling

Open the record for dataset details and reuse information.

opencc-by-4.0Jun 2024View details →

dryad28/100

High resolution aerial imagery of barley over a growing season

This dataset consists of unprocessed images and orthomosaic imagery of a barley field in Bozeman, Montana, collected throughout the growing season from emergence to maturity. The orthomosaics were used to develop an open-source workflow for extracting quantitative values from individual plots for downstream analysis of plant traits. This field exemplifies a challenge for plot extraction, as plots were planted with no border rows or alleys.

opencc-zeroMay 2023View details →

dryad28/100

High resolution aerial imagery of barley over a growing season

Open the record for dataset details and reuse information.

publicMay 2023View details →

zenodo24/100

Data and R code for the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery"

Data and R code used in the preprint "Downscaling digital soil maps using electromagnetic induction and aerial imagery" (Møller, 2020). This is the data and code for the preprint before submission for peer review. The data and code for the revised manuscript are available at <a href="https://doi.org/10.5281/zenodo.3959005">https://doi.org/10.5281/zenodo.3959005</a>. Code originally written for R version 3.6.2. References 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]

openMar 2020View details →

zenodo24/100

Land Cover Aerial Imagery (LICAID) dataset for semantic segmentation

Dataset Highlights: <ul> <li>Title: Land Cover Aerial Imagery Dataset (LICAID)</li> <li>Focus Area: Franciacorta wine-growing region, Lombardy, Italy</li> <li>Data Source: Satellite imagery from Google Earth Pro</li> <li>Classes and Descriptions: <ol> <li>Grasslands: Habitats dominated by grasses, with few or no trees, found in various climates from tropical to temperate regions.</li> <li>Arable Land: Land predominantly used for growing crops.</li> <li>Herb-dominated Habitats: Areas where non-woody plants (herbs) are the dominant vegetation, including meadows, prairies, marshes, and wetlands.</li> <li>Hedgerows: Linear strips of vegetation consisting of shrubs, small trees, and grasses, often used to mark boundaries or provide wildlife habitat in agricultural landscapes.</li> <li>Vineyards: Agricultural landscapes cultivated specifically for growing grapevines, typically for wine production.</li> <li>Tree-dominated Man-made Habitats: Human-modified landscapes where trees are the predominant vegetation, such as urban parks, orchards, and landscaped gardens.</li> <li>Olea europaea Groves: Groves or orchards of olive trees, primarily cultivated for the production of olives and olive oil, commonly found in Mediterranean regions.</li> </ol> </li> </ul> gy: <ol> <li> Data Acquisition: <ul> <li>18 orthophoto tiles manually selected from Franciacorta.</li> <li>Satellite imagery and corresponding shape files acquired from Google Earth Pro.</li> <li>Georeferencing of imagery using ArcGIS software.</li> </ul> </li> <li> Data Preparation: <ul> <li>Segmentation using multiresolution segmentation in eCognition software.</li> <li>Validation of segmented images by a plant expert using QGIS software.</li> <li>Manual annotation of seven land cover classes.</li> </ul> </li> </ol>

restrictedcc-by-4.0Jun 2024View details →

zenodo20/100

Treeline data based on analysis of timeseries of aerial imagery over Samaria National Park

The dataset is about the occurence of individual trees at the treeline in four locations in Samaria National Park

restrictedDec 2020View details →

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Allen Brain Atlas

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Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

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Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

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electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

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Last verified 2026-04-29Open record

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.

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neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record