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Accessing and using data cubes: spatial overlay, visualization and modeling – Python tutorial

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Název:	Accessing and using data cubes: spatial overlay, visualization and modeling – Python tutorial
Autoři:	Leandro Parente
Přispěvatelé:	Kompetenzzentrum für nicht-textuelle Materialien
Informace o vydavateli:	OpenGeoHub Foundation
Rok vydání:	2022
Témata:	Computer science, Open Data, Open science, Studienbereich Informatik, Ingenieurwissenschaften
Popis:	(en)In this training session you will learn about the main concepts / aspects related to raster data cubes, cloud-optimized geotiff (COG) and SpatioTemporal Asset Catalog (STAC), working with a practical example in Python. Using the eumap library and the training samples provided in the hackathon, you will perform a complete workflow for spatial predictive mapping, including:  Spacetime overlay (through STAC + COG),  Train a Random Forest classifier (with hyper-parameter optimization),  produce a classification output (also through STAC + COG). All the steps were executed in Google Colab and all the data (points and rasters) accessed directly from the cloud (http://stac.ecodatacube.eu).
Druh dokumentu:	course material moving image (video)
Jazyk:	English
Relation:	https://av.tib.eu/media/59408
Dostupnost:	https://av.tib.eu/media/59408
Rights:	https://creativecommons.org/licenses/by/3.0/de
Přístupové číslo:	edsbas.7321970C
Databáze:	BASE

Popis
Abstrakt:	(en)In this training session you will learn about the main concepts / aspects related to raster data cubes, cloud-optimized geotiff (COG) and SpatioTemporal Asset Catalog (STAC), working with a practical example in Python. Using the eumap library and the training samples provided in the hackathon, you will perform a complete workflow for spatial predictive mapping, including:  Spacetime overlay (through STAC + COG),  Train a Random Forest classifier (with hyper-parameter optimization),  produce a classification output (also through STAC + COG). All the steps were executed in Google Colab and all the data (points and rasters) accessed directly from the cloud (http://stac.ecodatacube.eu).