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nd – A Framework for the Analysis of n-dimensional Earth Observation Data Cover

nd – A Framework for the Analysis of n-dimensional Earth Observation Data

Open Access
|Mar 2022

Figures & Tables

Figure 1

This diagram illustrates the principle ideas behind the software architecture.

Figure 2

Reprojection of a subset of GHRSST data from EPSG:4326 (WGS84) to EPSG:2163 (US National Atlas Equal Area).

Figure 3

This figure shows the hottest month for each pixel in this subset of the GHRSST data. The result was obtained by mapping np.argmax over the dataset using ds.nd.apply().

Figure 4

This figure demonstrates a sample classification outcome using Sentinel-1 data (a) with polygon labels (b). The classification outcome is shown in (c).

DOI: https://doi.org/10.5334/jors.377 | Journal eISSN: 2049-9647
Language: English
Submitted on: Apr 15, 2021
Accepted on: Mar 3, 2022
Published on: Mar 11, 2022
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2022 Johannes N. Hansen, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.