Download PDFOpen PDF in browser

Building Visual Anomaly Dataset from Satellite Data using ADS-B

7 pagesPublished: December 23, 2019


In this paper, we present a novel way of obtaining extremely challenging image dataset for the purpose of benchmarking image anomaly detection methods. By definition, anomalies are rare occurrences, and therefore, annotation of anomalies using human workforce is difficult and costly, as large amounts of mostly non-anomalous data need to be checked. To alleviate this problem, we use satellite images from as the source of visual data, and combine them with ADS-B data to detect airplanes in a semi-automatic way. This way, our definition of anomaly is an appearance of an airplane on mostly airplane-free images. This not only speeds up annotation, but also provides the exact specification of what constitutes an anomaly, in an objective way. The resulting meta-dataset, containing references to imagery and accurate annotations will be published in the near future. It will include locations of nearly 100 positions of airplanes on satellite images and the corresponding references to satellite images, captured in vicinity of large airports in different parts of the world, in different climate zones.

Keyphrases: anomaly detection, computer vision, datasets

In: Christina Pöpper and Martin Strohmeier (editors). Proceedings of the 7th OpenSky Workshop 2019, vol 67, pages 44--50

BibTeX entry
  author    = {Marko Kastelic and Janez Pers},
  title     = {Building Visual Anomaly Dataset from Satellite Data using ADS-B},
  booktitle = {Proceedings of the 7th OpenSky Workshop 2019},
  editor    = {Christina P\textbackslash{}"opper and Martin Strohmeier},
  series    = {EPiC Series in Computing},
  volume    = {67},
  pages     = {44--50},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/cznf}}
Download PDFOpen PDF in browser