Training data semantic segmentation historical Images

DOI:10.4121/052fa1dc-cf10-4bef-8b64-aeda99fbe3a4.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/052fa1dc-cf10-4bef-8b64-aeda99fbe3a4

Datacite citation style

Dahle, Felix (2024): Training data semantic segmentation historical Images. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/052fa1dc-cf10-4bef-8b64-aeda99fbe3a4.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This dataset contains historical images and their labelled counterparts used to train a network for semantic segmentation of historical images. The images are historical aerial imagery from Antarctica, made between 1940 and 1990.

The images are black and white and can be nadir (V) or oblique (L or R).

The labelled images are labelled manually and are the segmented equivalents of the historical images. They contain 6 classes:

1: ice, 2: snow, 3: rocks, 4: water, 5: clouds, 6:sky, 7: unknown.

A pixel value of 0 means unlabelled.


History

  • 2024-01-15 first online, published, posted

Publisher

4TU.ResearchData

Format

image/tiff

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience & Remote Sensing

DATA

Files (161)