Einsatz von AMS-Daten und mapmap.js zur Erstellung von interaktiven, thematischen Karten am Beispiel des WKO Fachkräfte-Radars / Use of AMS Data and mapmap.js for the Creation of Interactive, Thematic Maps Using the WKO Skilled WorkersRadar as an Example. - Christoph Koller (AGIT Journal Angew. Geoinformatik, 2019)

Uncertainty-Guided Representation Learning in Local Climate Zone Classification. - Christoph Koller; Muhammad Shahrad; Xiao Xiang Zhu 001 (IGARSS, 2022)

Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance? - Christoph Koller; Göran Kauermann; Xiao Xiang Zhu 001 (CoRR, 2022)

Exploring Distance-Aware Uncertainty Quantification for Remote Sensing Image Classification. - Christoph Koller; Peter Jung 001; Xiao Xiang Zhu 001 (IGARSS, 2023)

Towards Label Embedding - Measuring classification difficulty. - Katharina Hechinger; Christoph Koller; Xiao Xiang Zhu 001; Göran Kauermann (CoRR, 2023)

How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning. - Yuanyuan Wang 002; Qian Song; Dawood Wasif; Muhammad Shahzad 002; Christoph Koller; Jonathan Bamber; Xiao Xiang Zhu 001 (CoRR, 2024)

Can Land Cover Classification Models Benefit From Distance-Aware Architectures? - Christoph Koller; Peter Jung 001; Xiao Xiang Zhu 001 (IEEE Geosci. Remote. Sens. Lett., 2024)

Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance in Earth Observation? - Christoph Koller; Göran Kauermann; Xiao Xiang Zhu 001 (IEEE Trans. Geosci. Remote. Sens., 2024)