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992 results for “Imagery”
Capturing land cover and land use with street level imagery
<p>This dataset, collected in September 2018, contains street-level photographs captured by three cameras fixed on the roof of car. A field survey was focused in the Vojvodina, Serbia to more closely examine land cover/land use within croplands monitored by LandSense citizen scientists (March-September 2018).</p> <p>The dataset has the following characteristics:</p> <ul> <li>Time period of data collection: Sep 2018</li> <li>Total number of photographs: 26759</li> <li>Region of interest: Vojvodina - Ruma municipality (Serbia)</li> </ul> <p>Associated files: Serbia Streetlevelimagery2018 – Attributes.txt, Serbia Streetlevelimagery2018.csv, Serbia Streetlevelimagery2018.zip</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://ec.europa.eu/jrc/en">Joint Research Centre</a> and <a href="https://inosens.rs/">InoSens</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery
<p>A novel global 30-m land-cover product with a fine classification system for the year 2015 (GLC_FCS30-2015). The product was produced by combining time-series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the MCD43A4 NBAR and CCI_LC land-cover products. Secondly, a local adaptive random forest model was built for each 5°×5° geographical tile by using the multi-temporal Landsat spectral and textures features of the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile.</p>
UAV Imagery Dataset for Paddy Rice Panicle Detection
<p>Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based on high spatial resolution images provide a potential solution to increase the throughput as well as the accuracy of panicle identification. The quality and volume of the dataset are crucial to training an accurate and robust deep learning model. Panicle segmentation tasks require particularly costly annotations. Here we open a paddy rice panicle dataset, acquired by DJI Mavic Pro in 2018, to public use for rice panicle phenotyping. </p> <pre>@article{wang2021paddy, title={Paddy Rice Imagery Dataset for Panicle Segmentation}, author={Wang, Hao and Lyu, Suxing and Ren, Yaxin}, journal={Agronomy}, volume={11}, number={8}, pages={1542}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} }</pre>
Water vapor database for atmospheric correction of Landsat imagery
<p>Atmospheric correction is a crucial preprocessing step for the analysis of optical satellite imagery like Landsat. Among the radiance-modifying gases, atmospheric water vapor is spatially and temporally variable, and cannot be measured reliably from the Landsat sensors. As such, atmospheric correction of Landsat data requires spatially and temporally explicit auxiliary information about atmospheric water vapor content.</p> <p>We have compiled a water vapor dataset that can be readily used to perform atmospheric correction of Landsat images. The dataset was generated by a global processing of the MODIS MOD05/MYD05 collection 6.1 products (<a href="https://doi.org/10.1029/2002JD003023">https://doi.org/10.1029/2002JD003023</a>; MODIS Atmosphere L2 Water Vapor Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA: <a href="https://doi.pangaea.de/10.5067/MODIS/MOD05_L2.006">doi:10.5067/MODIS/MOD05_L2.006</a>, <a href="https://doi.pangaea.de/10.5067/MODIS/MYD05_L2.006">doi:10.5067/MODIS/MYD05_L2.006</a>). The dataset is comprised of daily global water vapor data for February 2000 to December 2020 for each land-intersecting Worldwide Reference System 2 (WRS-2) scene, as well as a monthly climatology that can be used if no daily value is available.</p> <p>The dataset was generated by the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE v. 2.0 / 3.6, <a href="https://github.com/davidfrantz/force">https://github.com/davidfrantz/force</a>, <a href="https://doi.org/10.3390/rs11091124">https://doi.org/10.3390/rs11091124</a>), which is freely available software under the terms of the GNU General Public License v. >= 3. The water vapor dataset can be readily ingested into the FORCE Level 2 Processing System (Frantz et al. 2016, <a href="https://doi.pangaea.de/10.1109/TGRS.2016.2530856">doi:10.1109/TGRS.2016.2530856</a>) to perform atmospheric correction of Landsat imagery. This dataset is an update of <a href="https://doi.pangaea.de/10.1594/PANGAEA.893109">https://doi.pangaea.de/10.1594/PANGAEA.893109</a> and should be used from now on.</p>
Global Pasture Watch - Grassland reference samples based on visual interpretation of VHR imagery and harmonized datasets (2000–2024)
<p>Reference point samples used in the production of the <a href="https://doi.org/10.5281/zenodo.13890401">global maps of annual grassland class and extent for 2000—2022</a><strong> </strong>within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Wath</a> initiative. </p> <p>The reference samples (estabilished by Feature Space Coverage Sampling-FSCS) comprises <strong>2.3M points</strong> visually classified (<em>using Very High Resolution imagery</em>) in:</p> <ol> <li><strong>Cultivated grassland,</strong></li> <li><strong>Natural/semi-natural grassland</strong></li> <li><strong>Other land cover</strong></li> </ol> <p>The file <code>gpw_grassland_fscs.vi.vhr_tile.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> aggregates the samples by visual interpretation units ( 1x1 km) and includes the follow collumns:</p> <ul> <li>cluster_id: Cluster id defined by k-means (FSCS),</li> <li>cluster_distance: Distance from the sample tile to center of the cluster (FSCS),</li> <li>cluster_size: Size of cluster (strata) defined by the FSCS,</li> <li>priority: Priority used by the visual interpretation,</li> <li>tile_id: Sample tile id,</li> <li>imagery: VHR reference images used by the visual interpretation,</li> <li>min_year: Minimum of year covered by the reference samples,</li> <li>max_year: Maximum of year covered by the reference samples,</li> <li>n_years: Number of years covered by the reference samples,</li> <li>n_samples_c1: Number of reference samples for "Cultivated grass" (1),</li> <li>n_samples_c2: Number of reference samples for "Natural / Semi-natural grass" (2),</li> <li>n_samples_c3: Number of reference samples for "Open Shrubland" (2),</li> <li>n_samples_c4: Number of reference samples for "Not grass" (3),</li> <li>n_samples_all: Total number of reference samples,</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_point.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides individual points (with 60-m spatial support) and include the follow collumns:</p> <ul> <li>sample_id: Sample id deribed by MD5 Hash of columns x, y, imagery and year,</li> <li>x: Longitude in WGS84 (EPSG:4326),</li> <li>y: Latitude in WGS84 (EPSG:4326),</li> <li>vi_tile_id: 1-km tile id,</li> <li>tile_id: GLAD tile id (1x1 degree)</li> <li>imagery: VHR Reference image used by the visual interpretation (Google; Bing; Interpolated),</li> <li>ref_date: Reference date of GPW samples (based on VHR image) and of other existing datasets,</li> <li>year: Reference year of GPW samples (based on VHR image) and of other existing datasets,</li> <li>class: Class id (1: Cultivated grassland; 2: Natural/semi-natural grassland; 3: Open shrubland; 4: Other land cover) ,</li> <li>class_label: Class labels (Cultivated grassland; Natural/semi-natural grassland; Open shrubland; Other land cover) ,</li> <li>dataset_name: Existing dataset names (CGLS-LC, EuroCrops, GeoWiki, GeoWiki-feedback, LCMap-Conus, LUCAS, MapBiomas, WorldCereal, GPW) <br>dataset_class: Original land cover class provided by the maintainer of existing dataset</li> <li>esa_worldcover_2020: Land cover class labels extracted from ESA WorldCover 2020,</li> <li>glad_glcluc_yyyy: Land cover class labels extracted from UMD GLAD GLCLUC for the reference date,</li> <li>glc_fcs30d_yyyy: Land cover class labels extracted from GLC_FCS30D for the reference date,</li> <li>gpw_fscs_cluster: K-Means output ranging from 0—9999 according to Feature Space Coverage Sampling (FSCS),</li> <li>ml_cv_group: spatial block CV group (based on vi_tile_id),</li> <li>ml_type: specify if the sample was used for (1) training or (2) calibration.</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_grid.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides the grid samples (with 10-m spatial support) and include the follow collumns:</p> <ul> <li>tile_id: 1-km tile id,</li> <li>bing_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Bing Maps Images,</li> <li>bing_image_start_date: Start date of the Bing Maps Images used in the visual interpretation,</li> <li>bing_image_end_date: End date of the Bing Maps Images used in the visual interpretation,</li> <li>google_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Google Maps Images,</li> <li>google_image_start_date: Start date of the Google Maps Images used in the visual interpretation,</li> <li>google_image_end_date: End date of the Google Maps Images used in the visual interpretation,</li> <li>missing_image_date: No images available,</li> <li>same_image_bing_google: Images from the same date available in Google and Bing Maps.</li> </ul> <p>The dataset was produced through the <a href="https://plugins.qgis.org/plugins/qgis-fgi-plugin/">QGIS plugin Fast Grid Inspection</a>.</p> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="https://zenodo.org/records/13890400">2000-2002</a> <a href="https://zenodo.org/records/13890402">2003-2005</a> <a href="https://zenodo.org/records/13890404">2006-2008</a> <a href="https://zenodo.org/records/13890408">2009-2011</a> <a href="https://zenodo.org/records/13890410">2012-2014</a> <a href="https://zenodo.org/records/13890412">2015-2017</a> <a href="https://zenodo.org/records/13890414">2018-2020</a> <a href="https://zenodo.org/records/13890416">2021-2022</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Grassland reference samples based on VHR imagery (2000–2022):</strong><br><a href="https://doi.org/10.5281/zenodo.11281157">GeoPackage files</a></li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <h3>Support</h3> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></p>
Towards an open pipeline for the detection of Critical Infrastructure from satellite imagery – A case study on electrical substations in The Netherlands
<p><strong>Abstract.</strong> Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI prevents their incorporation in large-scale risk assessment studies. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMaps, processes the imagery and assets' masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25%, 50% and 100%) and hyperparameters settings (batch size and learning rate). The best metrics achieved are an Average Precision at an Intersection over Union of 50% of 30.93 and a tile F-score of 89.88%. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable AI.</p>
Data for publication 'Detection of Artificial Seed-like Objects from UAV Imagery'
<p>This resource contains the datasets supporting the model development as published in the article 'Detection of Artificial Seed-like Objects from UAV Imagery' (https://doi.org/10.3390/rs15061637).</p> <p>In the last two decades, unmanned aerial vehicle (UAV) technology has been widely utilized as an aerial survey method. Recently, a unique system of self-deployable and biodegradable microrobots akin to winged achene seeds was introduced to monitor environmental parameters in the air above the soil interface, which requires geo-localization. This research focuses on detecting these artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once). Three environmental parameters, namely, daylight condition, background type, and flying altitude, were investigated to encompass varying data acquisition situations and their influence on detection accuracy. Artificial seeds were detected using four variants of the YOLO version 5 (YOLOv5) algorithm, which were compared in terms of accuracy and speed. The most accurate model variant was used in combination with slice-aided hyper inference (SAHI) on full resolution images to evaluate the model’s performance. It was found that the YOLOv5n variant had the highest accuracy and fastest inference speed. After model training, the best conditions for detecting artificial seed-like objects were found at a flight altitude of 4 m, on an overcast day, and against a concrete background, obtaining accuracies of 0.91, 0.90, and 0.99, respectively. YOLOv5n outperformed the other models by achieving a mAP0.5 score of 84.6% on the validation set and 83.2% on the test set. This study can be used as a baseline for detecting seed-like objects under the tested conditions in future studies.</p>
Manually Annotated Drone Imagery (RGB) Dataset for automatic coastline delineation of Southern Baltic Sea, Poland with polyline annotations (0.1.1)
<p><strong>Overview:</strong></p> <p>The Manually Annotated Drone Imagery Dataset (MADRID) consists of hand annotated high resolution RGB images taken in two different types of coasts in Poland, Miedzyzdroje - cliff coast and in Mrzezyno - dune coast in 2022-2023. All images were converted into a uniform format of 1440x2560 pixels, polyline annotated and set into file structure format suited for semantic segmentation tasks (See "Usage" notes below for more details).</p> <p>The raw images of our dataset were captured Zenmuse L1 Sensor (RGB) mounted on a DJI Matrice 300 RTK Drone. Total of 4895 images were captured, however the dataset contains 3876 images with each image annotated with coastline. The dataset only include images with coastlines that are visually identifiable with the human eye. For the annotations of the images, CVAT v2.13 open-source software was utilized.</p> <p><strong>Usage:</strong></p> <p>The compressed RAR file contains two folders train and test. Each folder contains the file that represents the date at which the image was captured in the format of (year, month, day), number of the image and the name of the drone utilized to capture the image. For example, DJI_20220111140051_0051_Zenmuse-L1-mission and DJI_20220111140105_0053_Zenmuse-L1-mission. Additionally, the test folder contains annotations (one per image) which are extracted from the original XML annotation file provided in the CVAT 1.1 image format.</p> <p>Archives were compressed using RAR compression. They can be decompressed in a terminal by opening and extracting Madrid_v0.1_Data.zip.</p> <p>The subset of the data with the name Madrid_subset_data.zip has been added which contains a small portion of train and test images for purpose of inspecting the dataset without downloading the entire dataset.</p> <p>The training images for both training data and testing data are structured as follows.</p> <pre><code>Train/ └── images/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.JPG └── DJI_20220111140105_0053_Zenmuse-L1-mission.JPG └── ...<br>└── masks/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.PNG └── DJI_20220111140105_0053_Zenmuse-L1-mission.PNG └── ...<br><br>Test/ └── images/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.JPG └── DJI_20220111140105_0053_Zenmuse-L1-mission.JPG └── ...<br>└── masks/ └── DJI_20220111140051_0051_Zenmuse-L1-mission.PNG └── DJI_20220111140105_0053_Zenmuse-L1-mission.PNG └── ...</code></pre> <p> </p>
Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
<p>Damage estimation status of coconut trees plantation in the Phillippines derived from Sentinel-2 Imagery. Overall we estimated that 14.1 M coconut trees were affected by the typhoon inside our area of study. Please refer to our <a href="https://www.mdpi.com/2072-4292/13/21/4302">original</a> publication for more details.</p> <p>0: Uncertain</p> <p>1: No Data</p> <p>2: Background class</p> <p>3: Unchanged coconut plantation</p> <p>4: Damaged coconut plantation</p> <p>5: New coconut plantation</p> <p> </p> <p> </p> <p> </p>
Reference dataset for comparison of cloud detection algorithms for Sentinel-2 imagery
<p>Sentinel-2 cloud mask reference dataset generated and analyzed as part of Tarrio, K., Tang, X., Masek, J.G., Claverie, M., Ju, J., Qiu, S., Zhu, Z. and Woodcock, C.E., 2020. Comparison of cloud detection algorithms for Sentinel-2 imagery. Science of Remote Sensing, 2, p.100010. [https://www.sciencedirect.com/science/article/pii/S2666017220300092](https://www.sciencedirect.com/science/article/pii/S2666017220300092)</p> <p><strong>1. Reference masks</strong></p> <p><strong>Algorithms:</strong></p> <ul> <li>Fmask 1.x</li> <li>Fmask 2.x</li> <li>Fmask 4.x</li> <li>Tmask</li> <li>Sen2Cor</li> <li>MAJA</li> <li>LaSRC</li> </ul> <p><strong>Locations:</strong></p> <ul> <li>South Africa (35JPM)</li> <li>Senegal (28PDC)</li> <li>Switzerland (32TLT)</li> <li>France (31TCJ, 31TFJ)</li> <li>Morocco (29RNQ)</li> </ul> <p><strong>Standardized legend:</strong></p> <p>Original algorithm outputs were standardized to the same categorical legend.</p> <ul> <li>0 = clear land</li> <li>1 = clear water</li> <li>2 = cloud shadow</li> <li>3 = snow/ice</li> <li>4 = cloud</li> </ul> <p>All reference masks processed to both 10m and 30m resolution, with the exception of Tmask, which is available only at a 30m resolution.</p> <p><strong>Mask naming convention:</strong></p> <p>All processed masks are named according to the following convention:<br> M<*resolution*><*S2 MGRS tile ID*><*YYYY*><*DOY*><*algorithm*><br> e.g. **M30T28PDC2016351TMASK**</p> <p><br> <strong>2. Interpreted sample points</strong></p> <p>Sample points were selected based on agreement among different map products. This record includes a shapefile with the final interpretations for each of the sampled sites. (See publication for additional information.)</p>
Global leaf chlorophyll content (LCC) product from MODIS imagery (2000-2020)
<p>The spatial and temporal distribution of leaf chlorophyll content (LCC) is critical for understanding the capacity of vegetation photosynthesis. Here, a global 8-day leaf chlorophyll content (LCC) dataset at 500-m resolution was generated from MODIS data using a matrix system with two pairs of vegetation indices. </p> <p><strong>The following paper should be cited when using the data:</strong></p> <p>Xu, M., Liu, R., Chen, J.M., Liu, Y., Wolanin, A., Croft, H., He, L., Shang, R., Ju, W., Zhang, Y., He, Y., Wang, R., 2022. A 21-year time-series of global leaf chlorophyll content maps from MODIS imagery. IEEE Trans. Geosci. Remote Sens. <a href="https://doi.org/10.1109/TGRS.2022.3204185">https://doi.org/10.1109/TGRS.2022.3204185</a>.</p> <p>Detailed description of data organization can be found in the uploaded document "1Readme.docx".</p> <p>The whole dataset of MODIS LCC product is from 2000 to 2020. <strong>Due to the data volume limitation of Zenodo, currently the data from 2000-2010 can be downloaded through Google Drive sharing link:</strong></p> <p><a href="https://drive.google.com/drive/folders/11eXetjsAB_ZjFqGs8SXEWzd6byr_8LoW?usp=sharing">https://drive.google.com/drive/folders/11eXetjsAB_ZjFqGs8SXEWzd6byr_8LoW?usp=sharing</a></p> <p><strong>Related dataset: </strong>Mingzhu, X., Liu, R., Chen, J. M., Shang, R., & Liu, Y. (2022). Global leaf chlorophyll content product from MERIS imagery (GLOBMAP MERIS LCC) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10467919">https://doi.org/10.5281/zenodo.10467919</a></p> <p>For any other questions, please send email to Mingzhu Xu (<a href="mailto:xumzhu@gmail.com">xumzhu@gmail.com</a>).</p>
Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery (Polygons)
<p>Automatically generated dataset of glacier outlines from the journal article: "Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery"</p> <p>Research paper: https://www.sciencedirect.com/science/article/pii/S0034425721005824</p> <p>More information can be found here: https://github.com/bevingtona/glacier_change_western_canada</p>
Outputs of the Jupyter Notebook - Detecting floating objects using Deep Learning and Sentinel-2 imagery
<p>The dataset contains the outputs of the notebook "Detecting floating objects using Deep Learning and Sentinel-2 imagery" published in the ocean modelling section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a></p> </li> <li> <p>Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Modelling codebase</em></p> <ul> <li> <p>Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a></p> </li> <li> <p>Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a></p> </li> <li> <p>Marc Rußwurm (author), EPFL-ECEO, <a href="https://github.com/MarcCoru">@marccoru</a></p> </li> </ul>
Hyperspectral imagery Research Products - Toulouse urban area 2015 (French ANR HYEP project)
<p>The HYEP project (ANR 14-CE22-0016-01) main goal was to propose a panel of methods and processes designed for hyperspectral imaging, which specificity makes a weighty auxiliary for the monitoring of the elements of the urban area. The main results of the project can be found at</p> <ul> <li><a href="http://doi.org/10.1080/01431161.2017.1410247">G. Roussel, C. Weber, X. Briottet and X. Ceamanos, "Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution", International Journal of Remote Sensing, vol. 39(5), pp. 1593-1614, 2018.</a></li> <li><a href="http://doi.org/10.1109/ECMSM.2017.7945884">F. Z. Benhalouche, M. S. Karoui, Y. Deville, I. Boukerch, A. Ouamri, ``Multi-sharpening hyperspectral remote sensing data by multiplicative joint-criterion linear-quadratic nonnegative matrix factorization'', Proceedings of the 2017 IEEE International Workshop on Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM 2017), May 24-26, 2017, Donostia - San Sebastian</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01903469">Gintautas Mozgeris, Vytaut ̇e Juodkien ̇e, Donatas Jonikaviˇcius, Lina Straigyt ̇e, S ́ebastien Gadal, and Walid Ouerghemmi. Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment. Remote Sensing, 10(10), October 2018.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-02281003">Christiane Weber, Thomas Houet, S ́ebastien Gadal, Rahim Aguejdad, Grzegorz Skupinski, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Cl ́ement Mallet, and Arnaud Le Bris. HYEP HYperspectral imagery for Environmental urban Planning : principaux résultats. In 7ème colloque scientifique du groupe SFPT-GH, Toulouse, France, July 2019. ONERA - SFTP.</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01852844">Christiane Weber, Rahim Aguejdad, Xavier Briottet, Josselin Aval, Sophie Fabre, Jean Demuynck, Emmanuel Zenou, Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra, Sébastien Gadal, Walid Ouerghemmi, Clément Mallet, Arnaud Le Bris, and Nesrine CHEHATA. Hyperspectral Imagery for Environmental Urban Planning. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2018, pages 1628–1631, Valencia, Spain, July 2018a. IEEE.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01854904">Christiane Weber, Rahim Aguejdad, X Briottet, J Avala, S. Fabre, J Demuynck, E Zenou, Y. Deville, M. Karoui, F Z Benhalouche, S Gadal, W Ourghemmi, C. Mallet, A. Le Bris, and N. Chehata. HYPERSPECTRAL IMAGERY FOR ENVIRONMENTAL URBAN PLANNING. In IGARSS 2018, Valencia, Spain, 2018b. </a></li> <li><a href="http://doi.org/10.5194/isprs-archives-XLII-1-W1-167-201">W. Ouerghemmi, A. Le Bris, Nesrine CHEHATA, and Clément Mallet. A two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, volume XLII-1/W1, pages 167–174, Hanover, Germany, May 2017. Copernicus GmbH (Copernicus Publications).</a></li> <li><a href="https://hal.inria.fr/hal-02384455">Christiane Weber, Sébastien GADAL, Xavier Briottet, and Clément Mallet. Apport de l’imagerie hyperspectrale pour la planification urbaine. In Karine Emsellem, Diego Moreno, Christine Voiron-Canicio, and Didier Josselin, editors, SAGEO 2016 - Spatial Analysis and Geomatics, Actes de la conférence SAGEO’2016 - Spatial Analysis and GEOmatics, pages 454–462, Nice, France, December 2016. </a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01359643">Gintautas Mozgeris, S ́ebastien Gadal, Donatas Jonikaviˇcius, Lina Straigyte, Walid Ouerghemmi, and Vytaut ̇e Juodkiene. Hyperspectral and color-infrared imaging from ultra-light aircraft: Potential to recognize tree species in urban environments. In University of California Los Angeles, editor, 8th Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pages 542–546, Los Angeles, United States, August 2016.</a></li> <li><a href="https://hal.inria.fr/hal-02384458">Alexandre Hervieu, Arnaud Le Bris, and Cl ́ement Mallet. Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas. In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, volume III-3, pages 457–464, Prague, Czech Republic, July 2016.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01888126">Christiane Weber, Thomas Houet, Sebastien GADAL, Rahim Aguejdad, Grzegorz Skupinski, Aziz Serradj, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Clément Mallet, and Arnaud Le Bris. ANR HYEP ANR 14-CE22-0016-01Hyperspectral imagery for Environmental urban Planning HyepProgramme Mobilité et systèmes urbains 2014. Research report, CNRS UMR TETIS, ESPACE, LETG ; ONERA ; GIPSA-lab ; IRAP ; IGN, October 2018c. </a></li> <li><a href="https://doi.org/10.1080/01431161.2019.1579937">Josselin Aval, Sophie Fabre, Emmanuel Zenou, David Sheeren, Mathieu Fauvel & Xavier Briottet (2019) Object-based fusion for urban tree species classification from hyperspectral, panchromatic and nDSM data, International Journal of Remote Sensing, 40:14, 5339-5365, DOI: 10.1080/01431161.2019.1579937 </a></li> <li><a href="https://doi.org/10.3390/rs11111269">Charlotte Brabant, Emilien Alvarez-Vanhard, Achour Laribi, Gwenaël Morin, Kim Thanh Nguyen et al. Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification Remote Sensing, MDPI, 2019, 11 (11), pp.1269. ⟨10.3390/rs11111269⟩ hal-02191084v1 </a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191363v1">C. Brabant, Emilien Alvarez-Vanhard, Gwenaël Morin, Thanh Ngoc Nguyen, Achour Laribi et al. Evaluation of dimensional reduction methods on urban vegetation classification performance using hyperspectral data IGARSS 2018, Jul 2018, Valencia, Spain halshs-02191363v1</a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191097v1">Charlotte Brabant, Emilien Alvarez-Vanhard, Thomas Houet. Improving the classification of urban tree diversity from Very High Spatial Resolution hyperspectral images: comparison of multiples techniques Joint Urban Remote Sensing Event (JURSE 2019), May 2019, Vannes, France halshs-02191097v1</a></li> </ul> <p>This Dataset contains five research outputs of this project that were produced on the basis of Hyperspectral data obtained during an acquisition campaign led on Toulouse (France) urban area on July 2015 using Hyspex instrument which provides 408 spectral bands spread over 0.4 – 2.5 μ. Flight altitude lead to 2 m spatial resolution images.</p> <ul> <li><strong>Fields_samples.7z: </strong> ESRI Shape Format. Supervised SVN classification results for 600 urban trees according to a 3 level nomenclature: leaf type (5 classes), family (12 & 19 classes) and species (14 & 27 classes). The number of classes differ for the two latter as they depend on the minimum number of individuals considered (4 and 10 individuals per class respectively). Trees positions have been acquired using differential GPS and are given with centimetric to decimetric precision. A randomly selected subset of these trees has been used to train machine SVM and Random Forest classification algorithms. Those algorithms were applied to hyperspectral images using a number of classes for family (12 & 19 classes) and species (14 & 27 classes) levels defined according to the minimum number of individuals considered during training/validation process (4 and 10 individuals per class, respectively). Global classification precision for several training subsets is given by Brabant et al, 2019 (<a href="https://www.mdpi.com/470202">https://www.mdpi.com/470202</a>) in terms of averaged overall accuracy (AOA) and averaged kappa index of agreement (AKIA).</li> <li><strong>HySPex-2m.7z: </strong>full hyperspectral VNIR-SWIR ENVI standard image obtained from the coregistration of both VNIR and SWIR ones through a signal aggregation process that allowed to obtain a synthetic VNIR 1.6 m spatial resolution image, with pixels exactly corresponding to natif SWIR image ones. First, a spatially resampled 1.6 m VNIR image was built, where output pixel values were calculated as the average of the VNIR 0.8 m pixel values that spatially contribute to it. Then, ground control points (GCP) were selected over both images and SWIR one was tied to the VNIR 1.6 m image using a bilinear resampling method using ENVI tool. This lead to a 1.6 m spatial resolution full VNIR-SWIR image.</li> <li><strong>HYPXIM-4m.7z, HYPXIM-8m.7z, Sentinel2-10m.7z</strong>: hyperspectral ENVI standard simulated images. Spatial and spectral configurations generated correspond to ESA SENTINEL-2 instrument that was lunched on 2015, and HYPXIM sensor which was under study at that time. </li> </ul>
Satellite-derived chlorophyll-a concentrations for Lake Harsha (USA) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Harsha (USA) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
SeasoNet: A Seasonal Scene Classification, Segmentation and Retrieval Dataset for Satellite Imagery over Germany
<p>This dataset consists of 1,759,830 multi-spectral image patches from the Sentinel-2 mission, annotated with image- and pixel-level land cover and land usage labels from the German land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database (CLC) 2018. It includes pixel synchronous examples from each of the four seasons, plus an additional snowy set, spanning the time from April 2018 to February 2019. The patches were taken from 519,547 unique locations, covering the whole surface area of Germany, with each patch covering an area of 1.2km x 1.2km. The set is split into two overlapping grids, consisting of roughly 880,000 samples each, which are shifted by half the patch size in both dimensions. The images in each of the both grids themselves do not overlap.</p> <p><strong>Contents</strong></p> <p>Each sample includes:</p> <ul> <li>3 10m resolution bands (RGB), 120px x 120px</li> <li>1 10m resolution band (infrared), 120px x 120px</li> <li>6 20m resolution bands, 60px x 60px</li> <li>2 60m resolution bands, 20xp x 20px</li> <li>1 pixel-level label map</li> <li>2 binary masks for cloud and snow coverage</li> <li>2 binary masks for easy and medium segmentation difficulties, marks areas <300px and <100px respectively</li> <li>1 JSON-file containing additional meta-information</li> </ul> <p>The meta.csv contains the following information about each sample:</p> <ul> <li>Which season it belongs to</li> <li>Which of the two grids it belongs to</li> <li>Coordinates of the patch center</li> <li>Whether it was acquired from Sentinel-2 Satellite A or B</li> <li>Date and time of image acquisition</li> <li>Snow and cloud coverage percentages</li> <li>Image-level multi-class labels</li> <li>Three additional image-level urbanization labels, based on the center pixel (details below)</li> <li>The path to the sample</li> </ul> <p><strong>Classes</strong></p> <table> <thead> <tr> <th scope="col">ID</th> <th scope="col">Class</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Continuous urban fabric</td> </tr> <tr> <td>2</td> <td>Discontinuous urban fabric</td> </tr> <tr> <td>3</td> <td>Industrial or commercial units</td> </tr> <tr> <td>4</td> <td>Road and rail networks and associated land</td> </tr> <tr> <td>5</td> <td>Port areas</td> </tr> <tr> <td>6</td> <td>Airports</td> </tr> <tr> <td>7</td> <td>Mineral extraction sites</td> </tr> <tr> <td>8</td> <td>Dump sites</td> </tr> <tr> <td>9</td> <td>Construction sites</td> </tr> <tr> <td>10</td> <td>Green urban areas</td> </tr> <tr> <td>11</td> <td>Sport and leisure facilities</td> </tr> <tr> <td>12</td> <td>Non-irrigated arable land</td> </tr> <tr> <td>13</td> <td>Vineyards</td> </tr> <tr> <td>14</td> <td>Fruit trees and berry plantations</td> </tr> <tr> <td>15</td> <td>Pastures</td> </tr> <tr> <td>16</td> <td>Broad-leaved forest</td> </tr> <tr> <td>17</td> <td>Coniferous forest</td> </tr> <tr> <td>18</td> <td>Mixed forest</td> </tr> <tr> <td>19</td> <td>Natural grasslands</td> </tr> <tr> <td>20</td> <td>Moors and heathland</td> </tr> <tr> <td>21</td> <td>Transitional woodland/shrub</td> </tr> <tr> <td>22</td> <td>Beaches, dunes, sands</td> </tr> <tr> <td>23</td> <td>Bare rock</td> </tr> <tr> <td>24</td> <td>Sparsely vegetated areas</td> </tr> <tr> <td>25</td> <td>Inland marshes</td> </tr> <tr> <td>26</td> <td>Peat bogs</td> </tr> <tr> <td>27</td> <td>Salt marshes</td> </tr> <tr> <td>28</td> <td>Intertidal flats</td> </tr> <tr> <td>29</td> <td>Water courses</td> </tr> <tr> <td>30</td> <td>Water bodies</td> </tr> <tr> <td>31</td> <td>Coastal lagoons</td> </tr> <tr> <td>32</td> <td>Estuaries</td> </tr> <tr> <td>33</td> <td>Sea and ocean</td> </tr> </tbody> </table> <p><strong>Urbanization classes</strong></p> <ul> <li><strong>SLRAUM</strong> <ul> <li>0: None</li> <li>1: Ländlicher Raum (~ rural area)</li> <li>2: Städtischer Raum (~ urban area)</li> </ul> </li> <li><strong>RTYP3</strong> <ul> <li>0: None</li> <li>1: Ländliche Regionen (~ rural areas)</li> <li>2: Regionen mit Verstädterungsansätzen (~ urbanizing areas)</li> <li>3: Städtische Regionen (~ urban areas)</li> </ul> </li> <li><strong>KTYP4</strong> <ul> <li>0: None</li> <li>1: Dünn besiedelte ländliche Kreise</li> <li>2: Kreisfreie Großstädte</li> <li>3: Ländliche Kreise mit Verdichtungsansätzen</li> <li>4: Städtische Kreise</li> </ul> </li> </ul> <p>Further information on the urbanization classes can be found here:</p> <p><strong>SLRAUM</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html</a></p> <p><strong>RTYP3</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html</a></p> <p><strong>KTYP4</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html</a></p> <p><strong>License of landcover model</strong></p> <p>Bundesamt für Kartographie und Geodäsie</p> <p>dl-de/by-2-0 from <a href="https://www.govdata.de/dl-de/by-2-0">https://www.govdata.de/dl-de/by-2-0</a></p> <p>© GeoBasis-DE / <strong>BKG</strong> 2022</p> <p><strong>Source of landcover model</strong></p> <p><a href="https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/">https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/</a></p>
Drone aerial imagery of a small island in Kobbefjord (SW Greenland) acquired during Mission Arctic 2017
<p><code>Aerial images of a small island in Kobbefjord (SW Greenland) were acquired on 5 July 2017 using a DJI Phantom 3 Standard drone during the <a href="https://www.frontiersin.org/articles/10.3389/fmars.2021.665582/full">Mission Arctic citizen science expedition</a>. Images were processed in Agisoft Metashape. A digital elevation model (DEM) and an orthomosaic were exported at 5 cm resolution. For details, see the readme and processing report that accompanies this dataset. </code></p>
Drone aerial imagery of a headland in Nuup Kangerlua (Godthåbsfjord, SW Greenland) acquired during Mission Arctic 2017
<p>Aerial images of a headland in Nuup Kangerlua (Godthåbsfjord, Greenland) were acquired on 11 July 2017 using a DJI Phantom 3 Standard drone during the <a href="https://www.frontiersin.org/articles/10.3389/fmars.2021.665582/full">Mission Arctic citizen science expedition</a>. Images were processed in Agisoft Metashape. A digital elevation model (DEM) and an orthomosaic were exported at 2.5 cm resolution. A 5 cm resolution orthomosaic is also included. For details, see the readme and processing report that accompanies this dataset.</p>
Lunar Reconnaissance Orbiter Imagery for LROCNet Moon Classifier
<p><strong>Summary</strong></p> <p>We provide imagery used to train LROCNet -- our Convolutional Neural Network classifier of orbital imagery of the moon. Images are divided into train, validation, and test zip files, which contain class specific sub-folders. We have three classes: "fresh crater", "old crater", and "none". Classes are described in detail in the attached labeling guide.</p> <p><strong>Directory Contents</strong></p> <p>We include the labeling guide and training, testing, and validation data. Training data was split to avoid upload timeouts.</p> <ul> <li>LROC_Labeling_Intro_for_release.ppt: Labeling guide</li> <li>val: Validation images divided into class sub-folders <ul> <li>ejecta: "fresh crater" class</li> <li>oldcrater: "old crater" class</li> <li>none: "none" class</li> </ul> </li> <li>test: Testing images divided into class sub-folders <ul> <li>ejecta: "fresh crater" class</li> <li>oldcrater: "old crater" class</li> <li>none: "none" class</li> </ul> </li> <li>ejecta_train: Training images of "fresh crater" class</li> <li>oldcrater_train: Training images of "old crater" class</li> <li>none_train1-4: Training images of "none" class (divided into 4 just for uploading)</li> </ul> <p><strong>Data Description </strong></p> <p>We use CDR (Calibrated Data Record) browse imagery (50% resolution) from the Lunar Reconnaissance Orbiter's Narrow Angle Cameras (NACs). Data we get from the NACs are 5-km swaths, at nominal orbit, so we perform a saliency detection step to find surface features of interest. A detector developed for Mars HiRISE (Wagstaff et al.) worked well for our purposes, after updating based on LROC NAC image resolution. We use this detector to create a set of image chipouts (small 227x277 cutouts) from the larger image, sampling the lunar globe.</p> <p><strong>Class Labeling</strong></p> <p>We select classes of interest based on what is visible at the NAC resolution, consulting with scientists and performing a literature review. Initially, we have 7 classes: "fresh crater", "old crater", "overlapping craters", "irregular mare patches", "rockfalls and landfalls", "of scientific interest", and "none".</p> <p>Using the Zooniverse platform, we set up a labeling tool and labeled 5,000 images. We found that "fresh crater" make up 11% of the data, "old crater" 18%, with the vast majority "none". Due to limited examples of the other classes, we reduce our initial class set to: "fresh crater" (with impact ejecta), "old crater", and "none".</p> <p>We divide the images into train/validation/test sets making sure no image swaths span multiple sets.</p> <p><strong>Data Augmentation</strong></p> <p>Using PyTorch, we apply the following augmentation on the training set only: horizontal flip, vertical flip, rotation by 90/180/270 degrees, and brightness adjustment (0.5, 2). In addition, we use weighted sampling so that each class is weighted equally. The training set included here does not include augmentation since that was performed within PyTorch.</p> <p><strong>Acknowledgements</strong></p> <p>The author would like to thank the volunteers who provided annotations for this data set, as well as others who contributed to this work (as in the Contributor list). We would also like to thank the PDS Imaging Node for support of this work.</p> <p>The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).</p> <p>CL#22-4763</p> <p>© 2022 California Institute of Technology. Government sponsorship acknowledged.</p>
UAV-based imagery for road damage detection
<p>This dataset contains a set of annotated examples of damage present to the road, obtained via an UAV in Poznań, Poland. The imagery was obtained by flying a UAV over a set of roads at approximately 70m above ground level, and further creating an ortophotomosaics of three different pieces of road. Lastly, the ortophotomosaics were patched into non-overlapping patches of 512x512 pixel size, and each patch was manually labeled with a set of objects and classes proposed in the UAPD dataset (https://doi.org/10.1016/j.autcon.2021.103991, https://github.com/tantantetetao/UAPD-Pavement-Distress-Dataset).</p> <p>Patches without a significant presence of the road-like objects were discarded, and the data includes:</p> <ul> <li>ortophotomosaics -- a set of three original ortophotomosaics with ground sampling distance of 2.54 cm/pixel in .tif format,</li> <li>labeled patches -- a set of 99 patches containing road with annotations in both PASCAL VOC (.xml) and YOLO (.txt) formats</li> </ul> <p>Lastly, this data has been used as a holdout set in a Master's thesis conducted at Poznań University of Technology.</p>
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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.