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13,117 results for “mapping”
Land-cover mapping of the central Arizona region based on 2015 National Agriculture Imagery Program (NAIP) imagery
Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science, especially for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) program. This project provides a 1-meter land-cover mapping of the CAP LTER study area (greater Phoenix metropolitan area and surrounding Sonoran desert). The mapping is generated primarily using 2015 National Agriculture Imagery Program (NAIP) four-band data, with auxiliary GIS data used to improve accuracy. Auxiliary data include the 2015 cadastral parcel data, the 2014 USGS LiDAR data (1-meter), the 2014 Microsoft/OpenStreetMap Building Footprint data, the 2015 Street TIGER/Line, and a previous (2010) NAIP-based land-cover map of the study area (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=623). Among auxiliary data, building footprints and LiDAR data significantly improved the boundary detection of above-ground objects. Post-classification, manual editing was applied to minimize classification errors. As a result, the land-cover map achieves an overall accuracy of 94 per cent. The map contains eight land cover classes, including: (1) building, (2) asphalt, (3) bare soil and concrete, (4) tree and shrub, (5) grass, (6) water, (7) active cropland, and (8) fallow. When compared to the aforementioned, previous (2010) NAIP-based land-cover map for the study area, buildings and tree canopies are classified more accurately in this 2015 land-cover map.
May to July 2018 regions of interest (ROIs) of tidal marsh and tidal forest plant species to be used as ground reference data in habitat mapping
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground reference data for habitat mapping. Regions of interest (ROIs) for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were generated near ground control points (GCP) by digitizing vegetation areas in ArcGIS 10.4 based on field maps.These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
Map of ecological sites and ecological states for the USDA Jornada Experimental Range
This data package includes an ArcMap geodatabase: a polygon feature class, associated attribute table and metadata. The spatial data, JERStateMap_v1.gdb.zip, represents the ecological sites and states on the Jornada Experimental Range. The attribute table for the spatial data, JERStateMap.csv, and a summary of the spatial metadata, JERStateMapMetadata.pdf, are also included.
Cerebellum Retinotopic Mapping
Open the record for dataset details and reuse information.
Inventory maps of hazardous geological processes_Transcarpathia, Ukraine
<p>Under the ImProDiReT Project running at Regional Transcarpathia level an Inventory maps of the hazardous geological processes’ manifestations for the Transcarpathia (landslides, mudflows, flooding and flash floods, karst) have been created.</p>
Robust framework and software implementation for fast speciation mapping
<p>R script and raw data to test the sparse excitation energy XAS procedure.</p>
High-mountain Asia glacier elevation change trend (dh/dt) map for the period spanning 2000 to 2018
<p>See manuscript for methodology and dataset description:</p> <p>Shean DE, Bhushan S, Montesano P, Rounce DR, Arendt A and Osmanoglu B (2020) A Systematic, Regional Assessment of High-Mountain Asia Glacier Mass Balance. Front. Earth Sci. 7:363. DOI: 10.3389/feart.2019.00363</p> <p>https://www.frontiersin.org/articles/10.3389/feart.2019.00363/full</p> <p>GeoTiff header contains relevant metadata and georeferencing information (30 m pixel size, Albers Equal Area projection). Proj string is '+proj=aea +lat_1=25 +lat_2=47 +lat_0=36 +lon_0=85 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'</p> <p>External overview file (.ovr) contains pyramidal overviews for improved visualization performance at different zoom levels.</p>
Global consensus map of human transcription factor footprints
<p>Vierstra, J. <em>et al.</em> <strong>Global reference mapping of human transcription factor footprints.</strong> <em>Nature</em><strong> </strong>583, 729–736 (2020). <a href="https://doi.org/10.1038/s41586-020-2528-x">https://doi.org/10.1038/s41586-020-2528-x</a></p> <p>Preprint @ bioRxiv: <a href="https://doi.org/10.1101/2020.01.31.927798">https://doi.org/10.1101/2020.01.31.927798</a></p> <p><strong>Contact:</strong> Jeff Vierstra (<a href="mailto:jvierstra@altius.org?subject=Consensus%20DNase%20I%20footprints">jvierstra@altius.org</a>)</p> <p>Genomic DNase I footprinting enables quantitative, nucleotide-resolution delineation of sites of transcription factor occupancy within native chromatin. We combined sampling of >67 billion uniquely mapping DNase I cleavages from >240 human cell types and states to index, with unprecedented accuracy and resolution, human genomic footprints and thereby the sequence elements that encode transcription factor recognition sites.</p> <p>Please see <a href="http://vierstra.org/resources/dgf">http://vierstra.org/resources/dgf </a>for additional information and a complete set of raw DNase I data for individual datasets. Additionally, raw data can also be accessed via the ENCODE data portal (<a href="http://encodeproject.org">http://encodeproject.org</a>) using the dataset accessions found in Supplementary Table 1.</p> <p>Code for footprint analysis and tutorials on how to access and manipulate digital genomic footprint data can be found at <a href="https://footprint-tools.readthedocs.io/en/latest/">https://footprint-tools.readthedocs.io/en/latest/</a>.</p> <p>All files herein correspond to human genome build version GRCh38 (UCSC hg38).</p> <p><strong>Dataset contents:</strong></p> <ul> <li><strong>Biosample metadata</strong> – Supplementary_Table_1.xlsx</li> <li><strong>Motif clustering metadata </strong>– Supplementary_Table_2.xlsx</li> <li><strong>ChIP-seq validation metadata </strong>–<strong> </strong>Supplementary_Table_3.xlsx</li> <li><strong>Consensus footprint coordinates and assigned motif archetypes</strong><br> TSV file (BED-format) with consensus footprint (posterior probability>0.99) coordinates and overlaps with matches to motif model clusters. The legend file contains column definitions in detail. <ul> <li>consensus_footprints_and_motifs_hg38.bed.gz</li> <li>consensus_footprints_and_motifs_legend.txt</li> </ul> </li> <li><strong>Motif archetype matches overlapping consensus footprints</strong><br> TSV file (BED-format) containing the coordinates for clustered motif model matches that overlap consensus footprints <ul> <li>collapsed_motifs_overlaping_consensus_footprints.bed.gz</li> <li>collapsed_motifs_overlaping_consensus_footprints_legend.txt</li> </ul> </li> <li><strong>Footprint occupancy matrix of consensus footprints</strong><br> Rows are same order as the consensus footprint file and columns are same order as in the metadata files. <ul> <li>consensus_index_matrix_full_hg38.txt.gz (Values are –log(1-posterior))</li> <li>consensus_index_matrix_binary_hg38.txt.gz (binary occupancy matrix, where footprints with posterior footprint probability >0.99 are considered occupied)</li> </ul> </li> <li><strong>Single nucleotide variants tested for allelic imbalance </strong><br> The legend file contains column definitions in detail. <ul> <li>genotypes.vcf.gz - Genotyping and allelic read depth for each biosample (see header for more information)</li> <li>tested_snvs_padj.bed.gz - SNVs tested for imbalance (TSV, BED-format)</li> <li>tested_snvs_padj_legend.txt</li> </ul> </li> </ul>
Augmented emission maps: the 1461 cc 81 kW Euro 6 diesel engine: update 1
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1461 cc 81 kW Euro 6 diesel engine that has been applied in the several Renault, Nissan and Mercedes-Benz models (Kadjar, Megane, Scenic, Talisman, Captur, Clio, Kangoo; Qashqai, Juke, Pulsar, NV200; Citan).</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions
<p>This dataset provides various acquisitions for T2 mapping of the MnCl2 array of the NIST phantom at 1.5T. Data were acquired on a MAGNETOM Sola (Siemens Healthcare, Erlangen, Germany), with an 18-channel body coil and a 32-channel spine coil (12 elements used). It gathers original acquisitions from Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12.</p> <p>The dataset is composed of DICOM images from:</p> <p>i) Gold-standard single-echo spin echo (SE) sequences acquired at variable TE;</p> <p>ii) Alternative reference multi-echo spin echo (MESE) acquisitions;</p> <p>iii) Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) images at variable TE in three orthogonal orientations.</p> <p>The acquisition parameters are further detailed in the ReadMe.txt file provided along with the images.</p> <p>These acquisitions were repeated independently on three different days during the month of January 2020.</p> <p>These data are made publicly available as a support for further reproducibility studies as well as for the validation of new T2 relaxometry strategies.</p> <p>Works using any of these data should cite the following two references:</p> <p>- Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12</p> <p>- Lajous, Hélène, Ledoux, Jean-Baptiste, Hilbert, Tom, van Heeswijk, Ruud B., & Bach Cuadra, Meritxell. (2020). Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3931812</p>
AIOps Systematic Mapping Study - Results
<p>Results from our work "A Systematic Mapping Study in AIOps" (https://arxiv.org/abs/2012.09108).</p> <p>The file 'papers.csv' contains the complete list of AIOps papers identified, with corresponding metadata (author, year, citations, venue) and indexing annotations (macro-area, category, data sources, etc.) as columns. This file enables to explore and reproduce our results from scratch. For a quick and interactive exploration of the results without coding, you can check out the same dataset on Exploratory.io (https://exploratory.io/project/EDK0DNx1Qe/AIOps___Mapping_Study_pXe9pUn2). </p>
SILC cosmic microwave background (CMB) maps R1 of Planck PR2 data
<p>Clean maps of the CMB temperature anisotropies (as measured by Planck; public data release PR2) constructed with a novel internal linear combination (ILC) algorithm using directional, scale-discretised wavelets – Scale-discretised, directional wavelet ILC or SILC.</p>
Spin-SILC cosmic microwave background (CMB) polarisation maps R1 of Planck PR2 data
<p>Clean maps of the CMB linear polarisation <em>E</em>/<em>B</em> and Stokes <em>Q</em>/<em>U</em> fields (as measured by Planck; public data release PR2). They are constructed with a novel internal linear combination (ILC) algorithm using spin, directional, scale-discretised wavelets – Spin, Scale-discretised, directional wavelet ILC or Spin-SILC.</p>
US Atlantic and Gulf Coast Annual Wetland Land Cover and Change Maps, 1985 to 2022
<h3>This dataset is associated with the following article published in Remote Sensing Applications: Society and Environment, which can be accessed here: https://doi.org/10.1016/j.rsase.2024.101392</h3> <p>Shortly after publishing version 2, errors in the map projections were identified and corrected. Please use version 3 instead of version 2.</p> <p>Updates to version 2 were as follows:</p> <ul> <li>Includes watersheds in Texas that were not included in Version 1.</li> <li>A color map (using ArcPro) was added for improved interpretation.</li> <li>A sub-pixel scale offset in the change type map, related to map projection errors, was corrected.</li> </ul> <h2><strong>Mapping Coastal Wetland Changes from 1985 to 2022 in the US Atlantic and Gulf Coasts using Landsat Time Series and National Wetland Inventories</strong></h2> <p>Courtney A. Di Vittorio<sup>1</sup>, Melita Wiles<sup>2</sup>, Yasin W. Rabby<sup>2</sup>, Saeed Movahedi<sup>2</sup>, Jacob Louie<sup>1</sup>, Lily Hezrony<sup>1</sup>, Esteban Coyoy Cifuentes<sup>1</sup>, Wes Hinchman<sup>1</sup>, Alex Schluter<sup>1</sup></p> <p><sup>1</sup>Department of Engineering, Wake Forest University, Winston-Salem, North Carolina, USA.</p> <p><sup>2</sup>Department of Statistics, Wake Forest University, Winston-Salem, North Carolina, USA.</p> <h3>Abstract</h3> <p>The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), the widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution, and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses Google Earth Engine and the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing imagery with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The average overall accuracy of the algorithm is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsat-derived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the magnitude of severe changes matches that of the existing inventory and the magnitude of the moderate changes matches that of the more comparable product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Genome Mapping
<p>Data and conda software environment file for the chapter 'Genome Mapping' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
Redistribution of the Natura 2000 habitat map of Flanders (version 2023)
<p>This is a <i>redistribution</i> of a <i>subdataset</i> of the data source <a href="https://www.vlaanderen.be/DataCatalogRecord/9fffedd9-5076-4310-a366-198947717725">Biologische Waarderingskaart en Natura 2000 Habitatkaart - Toestand 2023</a>, originally published by the Research Institute for Nature and Forest (INBO) (De Saeger et al. 2023; see also De Saeger et al. 2017) and distributed by 'Digitaal Vlaanderen' under a CC-BY compatible license. It was redistributed in order to make it more easily findable and useful (in the long run) for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.</p><p>The subdataset is a shapefile of geospatial polygons of BWK and Natura 2000 habitat types in the Flemish Region, identical to the shapefile 'BwkHab' in the original data source.</p>
Interactive maps for the visualization of ESRIUM automated driving tests with various EGNSS localization solutions
<p>In order to make the test results available to a broader audience in an easy manner, we have generated interactive maps. These maps are attached to this report and can be viewed in a web-browser. </p><p>Due to the large number of datasets, we have color-coded them on the map and in the menu. An arbitrary number of datasets can be selected at a time.</p><p>Due to the high accuracy of the EGNSS receivers, one can clearly identify the lane on which the vehicle was driving, and where the vehicle was performing a lane-change. However, the satellite/areal-images are not perfectly geo-referenced, thus one can notice a slight offset between satellite/areal-images and real-world lanes.</p><p> </p><p><strong>How to use the map?</strong></p><ul><li>The map can be used in a similar manner than other map-applications, such as google maps. By using the mouse, you can set the focus on the area of your interest. By using the +/- buttons (top left), you can zoom in/out.</li><li>By hovering over the layer-symbol (top right), a popup emerges. Here, you can select different background-tiles (such as satellite/areal-images). In addition, you can select different datasets which should be visualized on the map.</li></ul><p><strong>Background-tiles:</strong></p><ul><li>Basemap – Sat - Satellite/Areal images (from Basemap) -Symbolic map with high resolution (from Basemap)</li><li>Basemap – HighDPI Symbolic map with high resolution (from Basemap)</li><li>OpenStreetMap - Symbolic map (from OpenStreetMap)</li><li>OpenTopoMap - Symbolic map including topology information (from OpenTopoMap)</li></ul><p><strong>Datasets:</strong></p><ul><li>GNSS (Vehicle) - Position of vehicle, according to on-board GPS receiver</li><li>EGNSS (AsteRx SB3 Pro+) - Position of vehicle, according to AsteRx SB3 Pro+ receiver</li><li>EGNSS (mosaic-X5) - Position of vehicle, according to mosaic-X5 receiver</li><li>EGNSS (mosaic-H) - Position of vehicle, according to mosaic-H receiver</li><li>PVT Mode: EGNSS (AsteRx SB3 Pro+) - PVT Mode of AsteRx SB3 Pro+ receiver</li><li>PVT Mode: EGNSS (mosaic-X5) - PVT Mode of mosaic-X5 receiver</li><li>PVT Mode: EGNSS (mosaic-H) - PVT Mode of mosaic-H receiver</li><li>in-lane Offset Change-Request - Position, at which an in-lane offset change (relative to middle of the current lane) was requested via C-ITS</li><li>Lane Change to left - Position, at which a lane-change towards left was performed </li><li>Lane Change to right - Position, at which a lane-change towards right was performed</li></ul><p>Interactive maps are attached are two precision levels one with 4 and the other in 7 digits. The list files and the corresponding test conditions are listed below. </p><p>Test velocities [km/h]: 90, 110, 130 </p><p>interactive map files: </p><p>speed: 90 km/h</p><ul><li>Testrun_01.html</li><li>Testrun_03.html</li><li>Testrun_04.html</li></ul><p>speed: 110 km/h</p><ul><li>Testrun_05.html</li><li>Testrun_06.html</li><li>Testrun_07.html</li></ul><p>speed: 130 km/h </p><ul><li>Testrun_08.html</li><li>Testrun_09.html</li><li>Testrun_10.html</li></ul>
Collection of spatial information and maps of human past and environment in the Uralic languages speaker area
<p>The collection of spatial information and maps of the past and environment in the Uralic languages speaker area consists excessive amount of multidisciplinary data related to the vast region extending from Eastern Europe to Siberia, encompassing countries like Russia, Finland, and parts of Scandinavia. Uralic speakers are predominantly found in this region, with historical roots in areas around the Ural Mountains and adjacent territories. These datasets can be integrated for multidisciplinary purposes, allowing to explore human-environment interactions, migration patterns, and cultural evolution over time. Datasets are collected initially by the BEDLAN team <a href="https://bedlan.net/">https://bedlan.net/</a> - a research group specialized in various disciplines - linguists, archaeologists, geneticists, and geographers. The data collection and mapmaking have grown beyond the initial stages (publications, applications, exhibitions), hence collaborative effort for data publishing is now crucial. As the data collections and mapmaking continue to evolve dynamically together with ongoing projects, the current repository will be updated accordingly.</p>
Dataset Publication for "A Comprehensive Stress Drop Map from Trench to Depth in the Northern Chilean Subduction Zone"
<p><strong>Abstract</strong>: Stress drop catalog data publication supplement for "A Comprehensive Stress Drop Map from Trench to Depth in the Northern Chilean Subduction Zone" (Folesky, J., Pennington, CN., Kummerow J., Hofman LR. (JGR: Solid Earth, 2023) <a href="https://doi.org/10.1029/2023JB027549">https://doi.org/10.1029/2023JB027549</a>), obtained from wave form analysis using the spectral decomposition technique. Time duration is 2007 to 2021. Seismic events were taken from the IPOC catalog (Sippl, C., Schurr, B., Münchmeyer, J., Barrientos, S., Oncken, O. (2023): Catalogue of Earthquake Hypocenters for Northern Chile from 2007-2021 using IPOC (plus auxiliary) seismic stations.<br><a title="Follow link" href="https://doi.org/10.5880/GFZ.4.1.2023.004" target="_blank" rel="nofollow noopener">https://doi.org/10.5880/GFZ.4.1.2023.004</a>). Wave forms were obtained from the EIDA/GEOPHONE web page (eida.gfz-potsdam.de/webdc3/ or geofon.gfz-potsdam.de/waveform/)</p> <p><strong>File descriptions</strong>: table columns <br>ID, cls, Lon, Lat, Depth, Magntiude, vssource, fc1, fcbound1, fcbound2, sd<br>------------------<br>explanation<br>ID : origin time<br>cls : event class<br>Lon : longitude <br>Lat : latitude<br>Depth : depth in km<br>Magnitude : magnitude (MA)<br>vssource. : s- wave velocity at the event location<br>fc1. : corner frequency in Hz<br>fcbound1 : lower bound for fc1 from 5% variance reduction test in Hz<br>fcbound2. : upper bound for fc2 from 5% variance reduction test in Hz<br>sd : stress drop estimate in MPa</p>
Iterative Mapping of Probabilities
<p>This repository contains data and scripts for implementing the Iterative Mapping of Probabilities (IMP) algorithm proposed in the preprint submitted to the International Journal of Applied Earth Observation and Geoinformation (JAG). The framework aims to improve the accuracy of land cover mapping by iteratively refining class maps to match independent area statistics. The experiment focuses on generating classification maps for five countries (Belgium, Czechia, Germany, Luxembourg, Netherlands) based on input probability rasters.</p> <h2>Usage</h2> <ol> <li><strong>Create a project folder</strong> where you'll store the files.</li> <li><strong>Download all the files</strong> to the project folder.</li> <li><strong>Extract the countries data</strong> into the project folder (be.zip=Belgium, cz=Czechia, de.zip=Germany, lu.zip=Luxembourg, nl.zip=Netherlands).<br>(Note: ensure the folder structure matches the "Data Description" section provided below) </li> <li><strong>Install Dependencies</strong> by navigating to the project folder in your terminal and install the necessary dependencies by running:<br>(Note: make sure you have Python installed on your system)<br><code>pip install -r ./requirements.txt</code></li> <li><strong>Run the script</strong> using the following command in the terminal:<br><code>python ./main.py</code></li> </ol> <h2>Data Description</h2> <p>After downloading and decompressing the files, the data must have the following structure.</p> <ul> <li><strong>area_estimates.csv: </strong>The area estimates for each land cover provided by Eurostat.</li> <li><strong>[country_code]/</strong><br> <ul> <li><strong>[model]/:</strong><br> <ul> <li><strong>classified_highest_likelihood/: </strong>Contains classification maps generated using the maximum likelihood mapping algorithm.</li> <li><strong>classified_proportional/: </strong>Stores classification maps produced using the Iterative Mapping of Probabilities algorithm.</li> <li><strong>iterations/: </strong>Stores images representing the iteration number in which each pixel was classified using the iterative proportional algorithm.</li> <li><strong>probabilities/: </strong>Contains input probability rasters for both mapping algorithms.</li> </ul> </li> </ul> </li> <li><strong>main.py</strong>: Python script implementing the Iterative Mapping of Probabilities framework.</li> <li><strong>requirements.txt</strong>: List of required libraries to run the script.</li> <li><strong>graphical_abstracl.pdf (optional)</strong>: Illustration on the IMP algorithm.</li> </ul> <h2>Script Explanation:</h2> <p>The script <strong>main.py</strong> implements IMP algorithm and process land use and land cover classification maps from probability rasters. These probabilities were generated for different countries and used two diffrent models (local and general). Please, refer to the paper for more details on how these models were trained.</p> <h3>Script Steps:</h3> <ol> <li><strong>Data Preparation:</strong><br>Loads <code>area_estimate.csv</code> file containing area estimates for different land cover classes in various countries and years.</li> <li><strong>Parameter Setup:</strong><br>Sets up parameters for each country, year, and model combination.<br>Each parameter set includes the country code, year, model type (local or general), and a list of land cover class codes.<br>(Note: you can change this section to set up parametersto process just some countries)</li> <li><strong>Processing Maps:</strong><br>Iterates over each parameter set and:<br> <ol> <li>Loads reference proportions of land cover classes for the specified country, year, and model.</li> <li>Loads probabilities from raster images.</li> <li>Runs the Iterative Mapping of Probabilities algorithm using the loaded probabilities and reference proportions.</li> <li>Saves the resulting land use and land cover classification map as an output.</li> </ol> </li> </ol> <h3>Script Inputs:</h3> <p>- CSV file containing area estimates for land cover classes (<code>./area_estimates.csv</code>).<br>- Probability raster images generated by classification models stored in <code>./[country_code]/[model]/probabilities/</code> folders.</p> <h3>Script Outputs:</h3> <p>Land use and land cover classification maps obtained by running the Iterative Mapping of Probabilities algorithm for each parameter set. The outputs are stored in <code>./[country_code]/[model]/classified_proportional_user/</code> folders.</p> <h2>Citation</h2> <p>If you use this code or data in your research, please cite the corresponding paper:</p> <p><em>Witjes, M., Herold, M., & de Bruin, S. (2024). Iterative Mapping of Probabilities: A data fusion framework for generating accurate land cover maps that match area statistics. Journal of Applied Earth Observation and Geoinformation (JAG), in review.</em></p> <h2>License</h2> <p>The code in this repository is licensed under the MIT License.</p> <p>The data provided in this repository is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.