Extraction of Non-forest Trees for Biomass Assessment Based on Airborne and Terrestrial LiDAR Data. - Matthias Rentsch; Alfons Krismann; Peter Krzystek (PIA, 2011)

Active learning approach to detecting standing dead trees from ALS point clouds combined with aerial infrared imagery. - Przemyslaw Polewski; Wei Yao 008; Marco Heurich; Peter Krzystek; Uwe Stilla (CVPR Workshops, 2015)

Estimation of regeneration coverage in a temperate forest by 3D segmentation using airborne laser scanning data. - Nina Amiri; Wei Yao 008; Marco Heurich; Peter Krzystek; Andrew K. Skidmore (Int. J. Appl. Earth Obs. Geoinformation, 2016)

Combining Active and Semisupervised Learning of Remote Sensing Data Within a Renyi Entropy Regularization Framework. - Przemyslaw Polewski; Wei Yao 008; Marco Heurich; Peter Krzystek; Uwe Stilla (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2016)

Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest. - Nina Amiri; Peter Krzystek; Marco Heurich; Andrew K. Skidmore (Remote. Sens., 2019)

Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery. - Peter Krzystek; Alla Serebryanyk; Claudius Schnörr; Jaroslav Cervenka; Marco Heurich (Remote. Sens., 2020)

Silvi-Net - A dual-CNN approach for combined classification of tree species and standing dead trees from remote sensing data. - Sebastian Briechle; Peter Krzystek; George Vosselman (Int. J. Appl. Earth Obs. Geoinformation, 2021)

An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series. - Sarah Hauser; Michael Ruhhammer; Andreas Schmitt; Peter Krzystek (Remote. Sens., 2024)