Raster Time Series: Learning and Processing

Raster Time Series: Learning and Processing

As the amount of remote sensing data is increasing at a high rate, due to great improvements in sensor technology, efficient processing capabilities are of utmost importance. Remote sensing data from satellites is crucial in many scientific domains, like biodiversity and climate research. Because we...

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Bibliografiska uppgifter
Huvudupphovsman: Drönner, Johannes
Övriga upphovsmän: Seeger, Bernhard (Prof. Dr.) (BetreuerIn (Doktorarbeit))
Materialtyp: Dissertation
Språk:engelska
Publicerad: Philipps-Universität Marburg 2019
Ämnen:
Rasterdaten
Meteosat Second Generation
Rasterdata, Spatio-Temporal Data
Convolutional Neural Networks
Informatik
Tiefe neuronale Netze
Raum-zeitliche Daten
Data processing Computer science
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