2025W

• Computer Vision in Industry / 193.194 / EX
• Machine Learning for Visual Computing / 193.189 / VU

• Making Archives Searchable: Vision-Language Models for Classification of Historical Aerial Imagery / Burges, M., Zambanini, S., & Sablatnig, R. (2024). Making Archives Searchable: Vision-Language Models for Classification of Historical Aerial Imagery. In GeoSearch ’24: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data (pp. 1–8). https://doi.org/10.1145/3681769.3698578
  Project: DoRIAH (2020–2024)
• CHAI: Craters in Historical Aerial Images / Burges, M., Zambanini, S., & Pirker, P. (2024). CHAI: Craters in Historical Aerial Images. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 8241–8250). https://doi.org/10.1109/WACV57701.2024.00807
  Project: DoRIAH (2020–2024)
• Self-Supervised Transfer Learning for Historical Aerial Images / Burges, M. (2023, October 10). Self-Supervised Transfer Learning for Historical Aerial Images [Poster Presentation]. AI4EO Symposium 2023, München, Germany.
  Project: DoRIAH (2020–2024)
• Exploring Learning-Based Approaches for Bomb Crater Detection in Historical Aerial Images / Burges, M., Zambanini, S., & Sablatnig, R. (2023). Exploring Learning-Based Approaches for Bomb Crater Detection in Historical Aerial Images. In Proceedings of the OAGM Workshop 2022 (pp. 60–66). https://doi.org/10.3217/978-3-85125-954-4-09
  Project: DoRIAH (2020–2024)
• Semi-Automatic Object Detection in Historical Aerial Images with a Human in the Loop / Burges, M. (2022). Semi-Automatic Object Detection in Historical Aerial Images with a Human in the Loop [Conference Presentation]. 2. EuroSDR 2022, Rom, Italy.
  Project: DoRIAH (2020–2024)