Dataset Open Access

spotGEO dataset

Chen, Bo; Liu, Daqi; Chin, Tat-Jun; Rutten, Mark; Derksen, Dawa; Märtens, Marcus; von Looz, Moritz; Lecuyer, Gurvan; Izzo, Dario

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Chen, Bo</dc:creator>
  <dc:creator>Liu, Daqi</dc:creator>
  <dc:creator>Chin, Tat-Jun</dc:creator>
  <dc:creator>Rutten, Mark</dc:creator>
  <dc:creator>Derksen, Dawa</dc:creator>
  <dc:creator>Märtens, Marcus</dc:creator>
  <dc:creator>von Looz, Moritz</dc:creator>
  <dc:creator>Lecuyer, Gurvan</dc:creator>
  <dc:creator>Izzo, Dario</dc:creator>
  <dc:description>The spotGEO dataset is the official dataset of ESA's Kelvins competition for "spotting GEO satellites". It consists of 6 400 grayscale image sequences of the night sky. These sequences were acquired from multiple positions using a low-cost ground based telescope. Each sequence consists of 5 frames, so in total this dataset consists of 32 000 grayscale .png-images.

The goal of the competition is to find geostationary objects, which often appear as very faint and smeared out blobs. The occurence of stars (streaks), clouds and other sources of noise in the image add - among other factors - to the difficulty of this task. Part of the dataset has been annotated to support machine learning approaches. Participants of the challenge are tasked to reconstruct the annotations for the remaining sequences.

For a detailed description on the challenge and this dataset, visit

Version 2: This version adds the annotations for the test folder which were used to evaluate the final score. Additionally, a small set of missing annotations that were discovered during the competition for the train part have been added.</dc:description>
  <dc:subject>computer vision</dc:subject>
  <dc:subject>sky observation</dc:subject>
  <dc:title>spotGEO dataset</dc:title>
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