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

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Bibliographic Details
Title: Accessing and using data cubes: spatial overlay, visualization and modeling – Python tutorial
Authors: Leandro Parente
Contributors: Kompetenzzentrum für nicht-textuelle Materialien
Publisher Information: OpenGeoHub Foundation
Publication Year: 2022
Subject Terms: Computer science, Open Data, Open science, Studienbereich Informatik, Ingenieurwissenschaften
Description: (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).
Document Type: course material
moving image (video)
Language: English
Relation: https://av.tib.eu/media/59408
Availability: https://av.tib.eu/media/59408
Rights: https://creativecommons.org/licenses/by/3.0/de
Accession Number: edsbas.7321970C
Database: BASE
Description
Abstract:(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).