Author

Maximilian Hell

Observed name: Hell, Maximilian

Institution: Technical University of Applied Sciences Würzburg-Schweinfurt

Observed institutions: Munich University of Applied Sciences, Technical University of Applied Sciences Würzburg-Schweinfurt

ORCID: 0000-0003-3559-5043

Observed ORCID: 0000-0003-3559-5043

OpenAlex ID: A5031636087

Classification of Tree Species and Standing Dead Trees with Lidar Point Clouds Using Two Deep Neural Networks: PointCNN and 3DmFV-Net

2022 · Maximilian Hell, Melanie Brandmeier, et al. · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · 52

DeepForest: Novel Deep Learning Models for Land Use and Land Cover Classification Using Multi-Temporal and -Modal Sentinel Data of the Amazon Basin

2022 · Eya Cherif, Maximilian Hell, et al. · Remote Sensing · 34

Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases

2020 · Andreas Schmitt, Anna Wendleder, et al. · Remote Sensing · 26

Identifying Plausible Labels from Noisy Training Data for a Land Use and Land Cover Classification Application in Amazônia Legal

2024 · Maximilian Hell, Melanie Brandmeier · Remote Sensing · 6

Transfer Learning for LULC Classification on multi-modal data in the Amazon Basin

2023 · Maximilian Hell, Melanie Brandmeier, et al. · EGU General Assembly Conference Abstracts · 1

Synergetic use of Sentinel-1 and Sentinel-2 data for large-scale Land Use/Land Cover Mapping

2022 · Melanie Brandmeier, Maximilian Hell, et al. · EGU General Assembly Conference Abstracts · 1

Detection of noise in supervised label data: a practical approach in the Amazonas region of Brazil using land use and land cover maps

2024 · Maximilian Hell, Melanie Brandmeier · EGU General Assembly Conference Abstracts

Works count: 8

Citation count: 120

H-index: 4

I10-index: 3