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Open Data Science Europe Metadata Catalog
type: Catalog
label: Open Data Science Europe Metadata Catalog (OpenGeoHub, CTU Prague, mundialis GmbH & Co KG, TerraSigna, MultiOne)
open search description: eng/portal.opensearch
uri lookup endpoint: eng/rdf.search?any=
publisher: OpenGeoHub, CTU Prague, mundialis GmbH & Co KG, TerraSigna, MultiOne
themes: INSPIRE media-types register
themes: GEMET - INSPIRE themes, version 1.0
language: eng
dataset: Urban fabric
record: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
dataset: gh:lcv-landcoverX111-lucasXcorineXrf-p-30m

OpenGeoHub, CTU Prague, mundialis GmbH & Co KG, TerraSigna, MultiOne
type: Organization

INSPIRE media-types register
type: ConceptScheme
uri: https://data.opendatascience.eu:/geonetwork/srv/eng/thesaurus.download

GEMET - INSPIRE themes, version 1.0
type: ConceptScheme
uri: https://data.opendatascience.eu:/geonetwork/srv/eng/thesaurus.download

Continents, countries, sea regions of the world.
type: ConceptScheme
uri: https://data.opendatascience.eu:/geonetwork/srv/eng/thesaurus.download

https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
type: CatalogRecord
primary topic: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
issued: 2021-01-14
modified: 2021-01-14
references: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: text/html
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https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
format: application/xml
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Urban fabric
type: Dataset
abstract: 111: Areas mainly occupied by dwellings and buildings used by administrative/public utilities, including their connected areas (associated lands, approach road network, parking lots).
keyword: Land cover, land use and administrative data
keyword: Geoharmonizer
keyword: geoharvester
keyword: geodata
keyword: Europe
theme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search
thumbnail: https://geoserver.opendatascience.eu/geoserver/gh/wms
spatial: Polygon
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temporal: 2000-01-01 / 2019-12-31
accrual periodicity: asNeeded
license: copyright
license: otherRestictions
distribution: gh:lcv-landcoverX111-lucasXcorineXrf-p-30m
data quality: derived from XY
title: ANV - Probability distribution for Quercus cerris
abstract: Overview: Actual Natural Vegetation (ANV): probability of occurrence for the Turkey oak in its realized environment for the period 2000 - 2031 Traceability (lineage): This is an original dataset produced with a machine learning framework which used a combination of point datasets and raster datasets as inputs. Point dataset is a harmonized collection of tree occurrence data, comprising observations from National Forest Inventories (EU-Forest), GBIF and LUCAS. The complete dataset is available on Zenodo. Raster datasets used as input are: harmonized and gapfilled time series of seasonal aggregates of the Landsat GLAD ARD dataset (bands and spectral indices); monthly time series air and surface temperature and precipitation from a reprocessed version of the Copernicus ERA5 dataset; long term averages of bioclimatic variables from CHELSA, tree species distribution maps from the European Atlas of Forest Tree Species; elevation, slope and other elevation-derived metrics; long term monthly averages snow probability and long term monthly averages of cloud fraction from MODIS. For a more comprehensive list refer to Bonannella et al. (2022) (in review, preprint available at: https://doi.org/10.21203/rs.3.rs-1252972/v1). Scientific methodology: Probability and uncertainty maps were the output of a spatiotemporal ensemble machine learning framework based on stacked regularization. Three base models (random forest, gradient boosted trees and generalized linear models) were first trained on the input dataset and their predictions were used to train an additional model (logistic regression) which provided the final predictions. More details on the whole workflow are available in the listed publication. Usability: Probability maps can be used to detect potential forest degradation and compositional change across the time period analyzed. Some possible applications for these topics are explained in the listed publication. Uncertainty quantification: Uncertainty is quantified by tak…
keyword: Vegetation
temporal: 2000-2002-01-01 / 2018-2020-12-31
title: Road and rail networks and associated land
abstract: 122: Motorways and railways, including associated installations (stations, platforms, embankments, linear greenery narrower than 100 m). Minimum width for inclusion: 100 m. The general requirement of 100 m delineation accuracy is not sufficient in mapping 122. The tolerable shift in delineation is maximum 50 m. In delineating 122 a maximum 15-20% exaggeration of width is allowed, meaning that real width of the road including associated land should be at least 80 m to be included in CLC. In such cases the exaggerated width should be as close as possible to 100 m.
title: OSM residental buildings
abstract: osm: Residential building aggregated and rasterized from OSM polygons, first to 10m spatial resolution and after downsampled to 30m by spatial average.
temporal: - / -
title: ANV - Probability distribution for Abies alba
abstract: Overview: Actual Natural Vegetation (ANV): probability of occurrence for the Silver fir in its realized environment for the period 2000 - 2020 Traceability (lineage): This is an original dataset produced with a machine learning framework which used a combination of point datasets and raster datasets as inputs. Point dataset is a harmonized collection of tree occurrence data, comprising observations from National Forest Inventories (EU-Forest), GBIF and LUCAS. The complete dataset is available on Zenodo. Raster datasets used as input are: harmonized and gapfilled time series of seasonal aggregates of the Landsat GLAD ARD dataset (bands and spectral indices); monthly time series air and surface temperature and precipitation from a reprocessed version of the Copernicus ERA5 dataset; long term averages of bioclimatic variables from CHELSA, tree species distribution maps from the European Atlas of Forest Tree Species; elevation, slope and other elevation-derived metrics; long term monthly averages snow probability and long term monthly averages of cloud fraction from MODIS. For a more comprehensive list refer to Bonannella et al. (2022) (in review, preprint available at: https://doi.org/10.21203/rs.3.rs-1252972/v1). Scientific methodology: Probability and uncertainty maps were the output of a spatiotemporal ensemble machine learning framework based on stacked regularization. Three base models (random forest, gradient boosted trees and generalized linear models) were first trained on the input dataset and their predictions were used to train an additional model (logistic regression) which provided the final predictions. More details on the whole workflow are available in the listed publication. Usability: Probability maps can be used to detect potential forest degradation and compositional change across the time period analyzed. Some possible applications for these topics are explained in the listed publication. Uncertainty quantification: Uncertainty is quantified by tak…

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Geographical grid systems
type: Concept
in scheme: https://data.opendatascience.eu/geonetwork/srv/eng/catalog.search

gh:lcv-landcoverX111-lucasXcorineXrf-p-30m
type: Distribution
format: OGC:WMS

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Martijn Witjes
type: Agent

Martijn Witjes
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Leandro Parente
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Carmelo Bonannella
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