From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information? - Hanna Meyer; Lukas W. Lehnert; Yun Wang 011; Christoph Reudenbach; Thomas Nauss; Jörg Bendix (Int. J. Appl. Earth Obs. Geoinformation, 2017)

Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. - Hanna Meyer; Christoph Reudenbach; Tomislav Hengl; Marwan Katurji; Thomas Nauss (Environ. Model. Softw., 2018)

Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction. - Hanna Meyer; Christoph Reudenbach; Stephan Wöllauer; Thomas Nauss (CoRR, 2019)

Quality Assessment of Photogrammetric Methods - A Workflow for Reproducible UAS Orthomosaics. - Marvin Ludwig; Christian M. Runge; Nicolas Friess; Tiziana L. Koch; Sebastian Richter; Simon Seyfried; Luise Wraase; Agustin Lobo; Maria-Teresa Sebastià; Christoph Reudenbach; Thomas Nauss (Remote. Sens., 2020)

Semi-Automatic Generation of Training Samples for Detecting Renewable Energy Plants in High-Resolution Aerial Images. - Maximilian Kleebauer; Daniel Horst; Christoph Reudenbach (Remote. Sens., 2021)

Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro. - Alice Ziegler; Hanna Meyer; Insa Otte; Marcell K. Peters; Tim Appelhans; Christina Behler; Katrin Böhning-Gaese; Alice Classen; Florian Detsch; Jürgen Deckert; Connal Eardley; Stefan W. Ferger; et al. (Remote. Sens., 2022)

Multi-Resolution Segmentation of Solar Photovoltaic Systems Using Deep Learning. - Maximilian Kleebauer; Christopher Marz; Christoph Reudenbach; Martin Braun 002 (Remote. Sens., 2023)