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Big Earth Observation data analysis using satellite image time series

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Title
Big Earth Observation data analysis using satellite image time series
Title of Series
Number of Parts
44
Author
License
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
Identifiers
Publisher
Release Date2024
LanguageEnglish
Producer
Production Year2023
Production PlaceWageningen

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Genre
Abstract
This webinar introduces sits, an open-source R package for land use and land cover classification of big Earth observation data using satellite image time series. Users build regular data cubes from cloud services such as Amazon Web Services, Microsoft Planetary Computer, NASA Harmonized Landsat-Sentinel, Brazil Data Cube, Swiss Data Cube and Digital Earth Africa. The SITS API includes assessing training sample quality, machine learning and deep learning classification algorithms, and Bayesian post-processing methods for smoothing and uncertainty assessment. To evaluate results, SITS supports best practice accuracy assessments.
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