Yet Another Computer Vision Index To Datasets (YACVID) - Details

Stand: 2020-07-05 000000m 02:03:11 - Overview

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Name (Institute + Shorttitle)CITY-OSM - ETH Zurich 
Description (include details on usage, files and paper references)# Learning Aerial Image Segmentation From Online Maps

This is the ground truth data generated for the publication

Learning Aerial Image Segmentation From Online Maps
Pascal Kaiser, Jan Dirk Wegner, Aurélien Lucchi, Martin Jaggi, Thomas Hofmann, Konrad Schindler
IEEE Transactions on Geoscience and Remote Sensing 55 (11), 6054-6068, 2017/11

Ground truth of Berlin, Chicago, Paris, Potsdam, and Zurich consist of aerial images from Google Maps
and pixel-wise building, road, and background labels from OpenStreetMap. Ground truth of Tokyo consists
of one aerial image from Google Maps and manually generated, pixel-wise building, road, and background labels.

Pixel-wise labels are provided as PNG images in RGB order. Pixels labeled as building, road, and
background are indicated by RGB colors [255,0,0], [0,0,255], and [255,255,255].

RGB channel means of aerial images
Berlin R: 79.94162, G: 84.72064, B: 78.94711
Chicago R: 86.46459, G: 85.73488, B: 77.14777
Paris R: 82.46727, G: 92.82243, B: 88.05664
Potsdam R: 74.85480, G: 77.37761, B: 70.22035
Tokyo R: 96.96883, G: 98.44344, B: 108.60135
Zurich R: 62.36962, G: 66.11001, B: 60.32863

Ground truth was generated in
Berlin Spring 2016
Chicago Autumn 2015
Paris Autumn 2015
Potsdam Spring 2016
Tokyo Spring 2017
Zurich Autumn 2015

Ground truth of Potsdam covers the same area as the publicly avaialble, manually labeled ISPRS ground

ISPRS semantic labeling challenge 
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References (SKIPPED)
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Tags (single words, spaced)semantic computer vision aerial image segmentation map geoscience remote sensing deep learning berlin chicaco paris potsdam tokyo zurich 
Last Changed2020-07-05 
Turing (2.12+3.25=?) :-)