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501 results for “Remote Sensing”
Datasets of Radwin 2023 Great Salt Lake Remote Sensing Historical Assessment
<p>Included are the culminated datasets for an article in review to be published with the Utah Geological Association. This data helps an investigator reproduce or utilize the data. There are datasets for both Landsat and Sentinel, for both the North and South arms of the Great Salt Lake. Additionally, there are datasets documenting the NDWI threshold used for each Landsat image, the outlier images not used in analyses, NDWI error assessment, and calculation of stats/facts.</p> <p>Paper abstract:</p> <p>The Great Salt Lake has been rapidly shrinking since the highstand of the mid-1980s, creating cause for concern in recent decades as the lake has reached historic lows. Many investigators have assessed the evolution of lake elevation, geochemistry, anthropogenic impacts, and links to climate and atmospheric processes; however, the use of remote sensing to study the evolution of the lake has been significantly limited. Harnessing recent advancements in cloud-processing, specifically Google Earth Engine cloud computing, this study utilizes over 600 Landsat TM/OLI and Sentinel MSI satellite images from 1984-2023 to present time-series analyses of remotely sensed Great Salt Lake water area, exposed lakebed area, surface cover types, and chlorophyll-a analyses paired with modelled estimates for water and exposed lakebed area. Results show that since the highstand of 1986-1987, the water area has declined by 45% (~3,000 km<sup>2</sup>) and the exposed lakebed area has increased to ~3,500 km<sup>2</sup> from ~500 km<sup>2</sup>. The area of unconsolidated sediments not protected by vegetation or halite crusts has risen to ~2,400 km<sup>2</sup>. Significant halite crusts are observed in the North Arm, having a max extent of ~150 km<sup>2</sup> between 2002 and 2003, while only small extents of halite crusts are observed for the South Arm. Vegetation is more prevalent in the Bear River Bay and South Arm, with surface area increases over 400% since 1990. Gypsum is widely observed independent of halite crusts. The results highlight multiple instances of land-use/water-management that led to observable changes in water/exposed lakebed area and halite crust extent. This study demonstrates the important benefits of maintaining a lake elevation above ~4,194 ft to maximize lake and halite crust area, which would help mitigate possible dust events and maintain broad lake extent.</p> <p> </p> <p>Files should be self-explanatory based on filename, where BRB means Bear River Bay. Note there are two video files animating the evolution of the North and South Arms of the Great Salt Lake using satellite imagery from 1984 to 2023. </p> <p> </p> <p>Visit https://github.com/radwinskis for details on code used for this study.</p> <p>Please contact me at markradwin@gmail.com with any questions.</p> <p> </p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)
<p>Based on <em>in situ</em> seawater <em>p</em>CO<sub>2</sub> data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater <em>p</em>CO<sub>2</sub> was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2,therm</sub> ) and upwelling index (<em>UI<sub>SST</sub></em>), to characterise the combined effect of atmospheric CO<sub>2</sub> forcing, thermodynamic effects, and multiple mixing processes on seawater <em>p</em>CO<sub>2</sub>. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived <em>p</em>CO<sub>2</sub>, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater <em>p</em>CO<sub>2</sub>. </p>
Remotely sensed geoarchaeological marks in the hinterland of Ravenna
<p>GeoPackage database with 994 fluvial and anthropogenic traces mapped in the hinterland of Ravenna through historical imagery, satellite images and drone photos, together with related metadata.</p> <p> </p> <p>Sources of aerial and satellite images analysed include:</p> <ul> <li>Istituto Geografico Militare Italiano (IGMI), Volo GAI (Gruppo Aeronautico Italiano) aerial photos are available at https://servizimoka.regione.emilia-romagna.it/mokaApp/apps/VIGMIGAI1954_H5/index.html (last accessed on 31 January 2025).</li> <li>Ministero dell'Ambiente e della Tutela del Territorio e del Mare (MATTM) aerial photos are available at http://www.pcn.minambiente.it/viewer/ (last accessed on 31 January 2025).</li> <li>Agenzia per le Erogazioni in Agricoltura (AGEA) aerial photos are available at https://servizimoka.regione.emilia-romagna.it/mokaApp/apps/CORERH5/index.html (last accessed on 31 January 2025).</li> <li>Google Earth satellite images, accessed through Google Earth Pro for desktop, which can be downloaded from https://www.google.it/earth/versions/ (last accessed on 31 January 2025).</li> <li>Microsoft Bing satellite images are available at https://www.bing.com/maps/aerial (last accessed on 31 January 2025). Historical imagery has been accessed through a third-party website freely available at https://zoom.earth/, but this service has been discontinued (last accessed on 31 January 2025).</li> <li>Esri World Imagery satellite images are available at https://livingatlas.arcgis.com/wayback/ (last accessed on 31 January 2025).</li> <li>Consorzio Telerilevamento Agricoltura (TeA) are available at https://servizimoka.regione.emilia-romagna.it/mokaApp/apps/CORERH5/index.html (last accessed on 31 January 2025).</li> <li>Compagnia Generale Riprese Aeree (CGR) are available at https://servizimoka.regione.emilia-romagna.it/mokaApp/apps/CORERH5/index.html (last accessed on 31 January 2025).</li> <li>PlanetScope satellite images were made available through Planet Education and Research Program: more information is available at https://www.planet.com/industries/education-and-research/ (last accessed on 31 January 2025).</li> </ul>
Supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations"
<p>This dataset is a supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations", available at <a href="http://doi.org/10.5281/zenodo.7803064">doi.org/10.5281/zenodo.7803064</a>. The additional variables here included are: slow edge velocity, fast edge velocity, and eddy dissipation rate (EDR). All variables are stored on the same time and range grids adopted for the main dataset. Similarly, the event selection and file structure are identical to those of the main dataset.<br><br>Slow and fast edge velocities are derived from Doppler spectra recorded by the zenith-pointing 94-GHz cloud radar. The slow (fast) edge velocity is calculated as the velocity associated with the slowest (fastest) Doppler bin above the peak noise level, belonging to a spectral cluster whose width is at least 5 Doppler bins.<br><br>The EDR is retrieved following the approach by Borque et al. (2016; <a href="http://doi.org/10.1002/2015JD024543">doi.org/10.1002/2015JD024543</a>), using as input the slow edge velocity, and model horizontal wind speed from the main dataset. EDR is retrieved in 5 minute intervals, up to a maximum range of 3 km.<br><br>The detailed documentation of the variables here included can be found in the Supporting Information to the following publication: <a href="https://doi.org/10.1029/2023GL106599" target="_blank" rel="noopener">doi.org/10.1029/2023GL106599</a>.</p>
Pond Area Estimates: Nine Study Regions in Alaska for 3 time periods (1950s, 1978-1982, 1999-2001) using remotely sensed images
The data are ArcGIS shapefiles by USGS quadrangle within 9 study regions: Arctic Coastal Plain, Stevens Village area, Yukon Flats, Minto Flats, Denali Flats, Talkeetna, Innoko Flats, Tetlin Flats, and Copper River Basin. Each shapefile polygon represents the shoreline of a pond as visually interpreted from each georectified remotely sensed image. All images were rectified based on at least 25 control points from 1:63 360 USGS digital raster graphics topographic maps using a second-order polynomial with a RMS error of less than one satellite image pixel (30 meters). All closed-basin ponds greater than 0.2 hectares were visually delineated and manually traced as polygons using ArcGIS. Each pond polygon has an ID and Hectares field representing the pond ID and area in hectares for the time period of the remotely sensed image.
Tree ring, leaf mining, climate, and remote sensing data from aspen leaf miner survey sites: III - Climate, leaf mining, and NDVI data
This dataset contiains annual site-level measurements from 2004 - 2015 of growing season climate moisture index ( GS CMI; summed CMI from May - September), average site level leaf mining, and mean July - August normalized difference vegetation index (NDVI) derived from Landsat, GIMMS3g, MODIS Aqua, and MODIS Terra
MCR LTER: Reference: Satellite Remote Sensing: Landsat ETM from 2001 to 2010
This LTER Remote Sensing spatial raster dataset consists of Landsat Enhanced Thematic Mapper image data collected between 2001 and 2010 over the island of Moorea and Tahiti. These are reference data, from the USGS EROS archive, not data generated by MCR LTER. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2009, Landsat ETM+ scene 20010429-LE70540722001119EDC00, LPGS_11.2.1, USGS, Sioux Falls, 04/29/2001.
MCR LTER: Reference: Satellite Remote Sensing: Landsat 7 in 1999 and 2000
This LTER Remote Sensing spatial raster dataset consists of Landsat 7 Thematic Mapper image data collected in 1999 and 2000 over the island of Moorea and Tahiti. These are reference data, from the USGS EROS archive, not data generated by MCR LTER. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2001, Landsat ETM+ scene RNL71054072_07220000426, Unspecified processing, USGS, Sioux Falls, 04/26/2000. NASA Landsat Program, 2003, Landsat ETM+ scene L7054072_19990830, Ortho product, USGS, Sioux Falls, 08/30/1999.
MCR LTER: Reference: Satellite Remote Sensing: Landsat MSS in 1979
This LTER Remote Sensing spatial raster dataset consists of Landsat Multi-Spectral Scanner (MSS) image data collected in 1979 over the island of Moorea and Tahiti. These are reference data, from the USGS EROS archive, not data generated by MCR LTER. This product was created by the U.S. Geological Survey (USGS) and contains Landsat data files in Geographic Tagged Image-File Format (GeoTIFF). NASA Landsat Program, 2010, Landsat MSS scene L3057072_07219790206_MTL, LPGS_11.2.1, USGS, Sioux Falls, 1979-02-06.
Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)
<p>Manuscript submitted to Earth and Space Science AGU entitled with <br> (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City)<br> by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao</p> <p>The STEC data inside two compressed folders named as stec059 and stec061, respectively.<br> The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year.<br> For example:<br> ES010590.18STEC<br> ES01 is the station name<br> 059 is the day of year (DOY), 2018</p>
Isoprene in the Southern Ocean & remote sensed variables
<pre>%%%%%% Variables' names contained in "rodriguezrosetal_2020_isorems_data.csv" %%%%%% "id" = source of the data ("peg" = PEGASO cruise, "ace" = ACE Expedition, "pml" = ANDREXII, "ooki" = Ooki et al. 2015, "hack" = Hackemberg et al. 2017) "solar_time" = solar time estimated with solaR package on R. "month" = month of the year. "iso_pm" = Isoprene concentration (pM) "chla_fluo" = Chlorophyll-a (fluorometric) "chla_matchup" = Chlorophyll-a (MODIS Aqua) "sst_matchup" = Sea Surface Temperature (MODIS Aqua) "zeu_matchup" = Depth of the Euphotic Layer (MODIS Aqua) "poc_matchup" = Particulate Organic Carbon (MODIS Aqua) "pic_matchup" = Particulate Inorganic Carbon (MODIS Aqua) "mld_matchup" = Mixing Layer Depth (Holte et al. 2017) "par_matchup" = PAR radiation (MODIS Aqua) "lat" = Latitude (decimal degrees) "lon" = Longitude (decimal degrees)</pre>
Supplementary files for ground-based remote sensing of sesame drydown
<p>This is a collection of supplementary materials in support of the article entitled "Using normalized difference vegetation index to estimate sesame drydown and seed yield," which was published on Dec. 10, 2020 in Journal of Crop Improvement, 35:4, 508-521. </p> <p>Supplementary file 1 - list of computer program. This list documents Minitab macro, dryrate.mac, that was written to do regression analysis for sesame drydown data collected at the Texas A&M AgriLife Research Center at Uvalde. The macro automates the process of running m x 9 linear regressions, where m represents the number of sesame plots/genotypes and 9 the number of vegetation indices.</p> <p>Supplementary file 2 - sample input data to macro dryrate.mac. The file contains nine vegetation indices measured five times from six plots during sesame drydown in the 2019 growing season.</p> <p>Supplementary file 3 - sample output data. This file contains the outputs of dryrate, including selected regression coefficients for<br>each of the six sesame plots.</p> <p>Supplementary file 4 - Figure S1. Scatter plots of average values of nine vegetation indices for 60 sesame genotypes measured five<br>times from 6 September 2019 to 6 October 2019.</p> <p>Supplementary file 5 - Table S1. Values of the slopes depicting the regressions between nine vegetation indices and time for 60 sesame genotypes.</p> <p>Supplementary file 6 - Table S2. Average values of different vegetation indices measured on Day 9 during drydown for 60 sesame<br>genotypes.</p> <p>Supplementary file 7 - Figure S2. Relationships between sesame seed yield and the slopes of regressions between nine vegetation indices and time during the drydown period.</p> <p>Supplementary file 8 - Figure S3. Histograms of the coefficients of determinations for the 60 linear regressions relating different<br>vegetation indices with time (days during sesame drydown).</p> <p>Supplementary file 9 - Figure S4. Ranking from the highest to the lowest of the maximum NDVI (normalized difference vegetation index) for the 60 sesame genotypes, along with the measured seed yields for the corresponding genotypes.<br> </p>
LimnoSat-US: A Remote Sensing Dataset for U.S. Lakes from 1984-2020
<p>LimnoSat-US is an analysis-ready remote sensing database that includes reflectance values spanning 36 years for 56,792 lakes across > 328,000 Landsat scenes. The database comes pre-processed with cross-sensor standardization and the effects of clouds, cloud shadows, snow, ice, and macrophytes removed. In total, it contains over 22 million individual lake observations with an average of 393 +/- 233 (mean +/- standard deviation) observations per lake over the 36 year period. The data and code contained within this repository are as follows:</p> <p><em>HydroLakes_DP.shp: </em>A shapefile containing the deepest points for all U.S. lakes within HydroLakes. For more information on the deepest point see https://doi.org/10.5281/zenodo.4136754 and Shen et al (2015).</p> <p><em>LakeExport.py</em>: Python code to extract reflectance values for U.S. lakes from Google Earth Engine.</p> <p><em>GEE_pull_functions.py: </em>Functions called within LakeExport.py</p> <p><em>01_LakeExtractor.Rmd:</em> An R Markdown file that takes the raw data from LakeExport.py and processes it for the final database.</p> <p><em>SceneMetadata.csv:</em> A file containing additional information such as scene cloud cover and sun angle for all Landsat scenes within the database. Can be joined to the final database using LandsatID.</p> <p><em>srCorrected_us_hydrolakes_dp_20200628: </em>The final LimnoSat-US database containing all cloud free observations of U.S. lakes from 1984-2020. Missing values for bands not shared between sensors (Aerosol and TIR2) are denoted by -99. dWL is the dominant wavelength calculated following Wang et al. (2015). pCount_dswe1 represents the number of high confidence water pixels within 120 meters of the deepest point. pCount_dswe3 represents the number of vegetated water pixels within 120 meters and can be used as a flag for potential reflectance noise. All reflectance values represent the median value of high confidence water pixels within 120 meters. The final database is provided in both as a .csv and .feather formats. It can be linked to SceneMetadata.cvs using LandsatID. All reflectance values are derived from USGS T1-SR Landsat scenes.</p>
Reconstructed remote sensing land surface temperature data in North America in 2002-2018
<p>In order to more accurately study the change trend of land surface temperature in North America in recent years, we combined remote sensing and meteorological station data and used various restoration models to generate more accurate and more complete remote sensing land surface temperature data. Our data covered the North American continent from 2002 to 2018, with a spatial resolution of 0.05°×0.05°. In order to facilitate the statistics of the data, we set the projection mode of the data as World_Cylindrical_Equal_Area. We collated the data from different time dimensions, including month, season and year.</p>
Survey data for "Remote Sensing & GIS Training in Ecology and Conservation"
<p>This file provides the raw data of an online survey intended at gathering information regarding remote sensing (RS) and Geographical Information Systems (GIS) for conservation in academic education. The aim was to unfold best practices as well as gaps in teaching methods of remote sensing/GIS, and to help inform how these may be adapted and improved. A total of 73 people answered the survey, which was distributed through closed mailing lists of universities and conservation groups.</p>
MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters
<p><strong>The dataset</strong></p> <p>MagicBathyNet is a benchmark dataset made up of image patches of Sentinel-2, SPOT-6 and aerial imagery, bathymetry in raster format and seabed classes annotations. MagicBathyNet has been designed to be geographically well distributed. It’s coverage includes two very different coastal areas (in terms of water column characteristics and bottom type): i) Agia Napa area in Cyprus, covering a wide range of typical Mediterranean waters and seabed types, and ii) Puck Lagoon area in Poland, representing in a great degree Baltic Sea waters and bottom.</p> <p>MagicBathyNet contains 3355 RGB co-registered triplets of Sentinel-2 (S2), SPOT-6, and aerial image patches, complemented by 1244 RGB co-registered S2 and SPOT-6 doublets, 3354 DSM (Digital Surface Model) raster patches for the aerial patches and 3396 DSM raster patches for S2 and SPOT-6. Additionally, it contains 533 annotated raster patches for seabed habitat and type, facilitating supervised pixel-based classification. Each patch covers 180x180m, represented by 18x18 pixels in S2 imagery, 30x30 pixels in SPOT-6 imagery and 720x720 pixels in airborne imagery. </p> <p>For the implementation code and pre-trained models visit our project page: <a href="https://www.magicbathy.eu/magicbathynet.html">https://www.magicbathy.eu/magicbathynet.html</a> </p> <p><strong>MagicBathyNet.zip </strong>file contains the original dataset presented in the respective paper.</p> <p><strong>MagicBathyNet_extension_for_Swin-BathyUNet.zip</strong> file is added in the new version to support the experiments and the results presented in "Agrafiotis, P., & Demir, B. (2025). Deep learning-based bathymetry retrieval without in-situ depths using remote sensing imagery and SfM-MVS DSMs with data gaps. <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>, <em>225</em>, 341-361. <a href="https://doi.org/10.1016/j.isprsjprs.2025.04.020">https://doi.org/10.1016/j.isprsjprs.2025.04.020</a> "</p> <p> </p> <p> </p> <p><strong>Citation</strong></p> <p>If you use the code in this repository or the dataset please cite our paper:</p> <p>P. Agrafiotis, L. Janowski, D. Skarlatos, and B. Demir, <a href="https://arxiv.org/abs/2405.15477" target="_blank" rel="noopener noreferrer">"MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters"</a>, arXiv:2405.15477, 2024.</p> <p>or </p> <p>P. Agrafiotis, Ł. Janowski, D. Skarlatos and B. Demir, "MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-Based Classification in Shallow Waters," <em>IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium</em>, Athens, Greece, 2024, pp. 249-253, doi: 10.1109/IGARSS53475.2024.10641355.</p> <p><strong>Folder structure</strong></p> <p>┗ 📂 magicbathynet/<br> ┣ 📂 agia_napa/<br> ┃ ┣ 📂 img/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 img_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 depth/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 depth_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 gts/<br> ┃ ┃ ┣ 📂 aerial/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 s2/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┃ ┣ 📂 spot6/<br> ┃ ┃ ┃ ┣ 📜 gts_339.tif<br> ┃ ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📜 [modality]_split_bathymetry.txt<br> ┃ ┣ 📜 [modality]_split_pixel_class.txt<br> ┃ ┣ 📜 norm_param_[modality]_an.txt<br> ┃<br> ┣ 📂 puck_lagoon/<br> ┃ ┣ 📂 img/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 depth/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📂 gts/<br> ┃ ┃ ┣ 📜 ...<br> ┃ ┣ 📜 [modality]_split_bathymetry.txt<br> ┃ ┣ 📜 [modality]_split_pixel_class.txt<br> ┃ ┣ 📜 norm_param_[modality]_pl.txt</p> <p> </p> <p><strong>Package for benchmarking MagicBathyNet dataset</strong></p> <p>Donwload the package for benchmarking MagicBathyNet dataset in learning-based bathymetry and pixel-based classification here:</p> <p><a href="https://github.com/pagraf/MagicBathyNet">https://github.com/pagraf/MagicBathyNet</a></p> <p> </p> <p><strong>Version history</strong></p> <p>v1.0.0 - First release</p> <p> </p> <p><strong>License</strong></p> <p>Dataset: Creative Commons Attribution Non Commercial 4.0 International</p> <p>Code: Attribution-NonCommercial-ShareAlike 4.0 International License</p> <p>Copyright (c) 2024 The MagicBathyNet Authors</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This work was part of the project MagicBathy which is a research project funded by the European Commission for the period 2023-2025. It is funded under the HORIZON Europe MSCA Postdoctoral Fellowships - European Fellowships (GA 101063294).</p> <p>European Space Agency (ESA) is also acknowledged for providing the SPOT-6 images within its TPM programme in the frame of proposal PP0092443 and Airbus for being the provider of the original SPOT-6 images. The Dep. of Land and Surveys of Cyprus is acknowledged for providing the LiDAR reference data for Cyprus.</p>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
A Scene-Level Method for Estimating Small River Widths in Complex Terrain Using Remote Sensing
<p><strong>Files and Descriptions:</strong></p> <p>1. <strong>TP_Lake.csv</strong>: This CSV file contains identified lakes in the Tibetan Plateau.</p> <p>2. <strong>TP_River_Monthly_Statistics.csv</strong>: Monthly statistics for river data on the Tibetan Plateau, including estimations like active channel percentage and width for different river orders.</p> <p>3. <strong>S2RiverWidth.py</strong>: The Python script that contains the main code for river width estimation model. This script includes the functions for preprocessing Sentinel-2 images and predicting river widths.</p> <p>4. <strong>best_model_vCloud10.pth</strong>: The pre-trained deep learning model weights used for river width estimation. This model is a ResNeXt model fine-tuned on our dataset. It accepts Sentinel-2 TOA image with cloud percentage <10% (SCL).</p> <p>5. <strong>Example.tif</strong>: A Sentinel-2 TOA image in .tif format, used for testing the river width estimation model.</p> <p><strong>Model Input Requirements:</strong><br>The model requires a Sentinel-2 TOA image in .tif format as input. The image should be scaled by a factor of 10,000 (with reflectance values range from 0 to 1). The `get_model_input` function automatically crops the image to 224x224 pixels around the center to fit the model's input requirements.</p>
OHS data provided by Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application
<p>Images from Chinese Orbita Hyperspectral Satellites (OHS) provided by <em>the Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application</em> are shared. All the images have been radiometric calibrated and atmospheric corrected by the author.</p> <p>Paper: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, "Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution," <em>IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)</em>, 2021.</p> <p>More information about the author can be found at https://jianghe96.github.io/</p> <p>If this dataset is helpful please cite as:</p> <pre>@article{he2021spectral, title={Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author={He, Jiang and Li, Jie and Yuan, Qiangqiang and Shen, Huanfeng and Zhang, Liangpei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, }</pre>
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
<p>The benchmark code is available at: <a href="https://github.com/Junjue-Wang/LoveDA">https://github.com/Junjue-Wang/LoveDA</a></p> <p><strong>Highlights: </strong></p> <ol> <li>5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan</li> <li>Focus on different geographical environments between Urban and Rural</li> <li>Advance both semantic segmentation and domain adaptation tasks</li> <li>Three considerable challenges: multi-scale objects, complex background samples, and inconsistent class distributions</li> </ol> <p><strong>Reference:</strong></p> <pre><code>@inproceedings{wang2021loveda, title={Love{DA}: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation}, author={Junjue Wang and Zhuo Zheng and Ailong Ma and Xiaoyan Lu and Yanfei Zhong}, booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks}, editor = {J. Vanschoren and S. Yeung}, year={2021}, volume = {1}, pages = {}, url={https://datasets-benchmarks proceedings.neurips.cc/paper/2021/file/4e732ced3463d06de0ca9a15b6153677-Paper-round2.pdf} }</code></pre> <p><strong>License:</strong></p> <p>The owners of the data and of the copyright on the data are RSIDEA, Wuhan University. Use of the Google Earth images must respect the "Google Earth" terms of use. All images and their associated annotations in LoveDA can be used for academic purposes only, <strong>but any commercial use is prohibited. (CC BY-NC-SA 4.0)</strong></p>
ScienceDex guides
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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.