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Satellite data is for everyone: Insights into modern remote sensing research with open data and Python

Riese, Felix M.; Leitloff, Jens

Abstract (englisch):

The largest earth observation programme Copernicus ( makes it possible to perform terrestrial observations providing data for all kinds of purposes. One important objective is to monitor the land-use and land-cover changes with the Sentinel-2 satellite mission. These satellites measure the sun reflectance on the earth surface with multispectral cameras (13 channels between 440 nm to 2190 nm). Machine learning techniques like convolutional neural networks (CNN) are able to learn the link between the satellite image (spectrum) and the ground truth (land use class). In this talk, we give an overview about the state-of-the-art land-use classification with CNNs based on an open dataset. > > We use different out-of-box CNNs for the Keras deep learning library ( All networks are either included in Keras itself or are available from Github repositories. We show the process of transfer learning for the RGB datasets. Furthermore, the minimal changes required to apply commonly used CNNs to multispectral data are demonstrated. Thus, the interested audience will be able to perform their own classification of remote sensing data within a very short time. ... mehr

Link zum Medienportal
DOI: 10.5281/zenodo.4056516
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Audio & Video
Publikationsdatum 05.11.2018
Erstellungsdatum 26.10.2018
Sprache Englisch
DOI 10.5281/zenodo.4056516
Identifikator KITopen-ID: 1000118365
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