| Evaluation of different machine learning algorithms for scalable classification of tree types and tree species based on Sentinel-2 data M Wessel, M Brandmeier, D Tiede Remote Sensing 10 (9), 1419, 2018 | 160 | 2018 |
| Forest damage assessment using deep learning on high resolution remote sensing data ZM Hamdi, M Brandmeier, C Straub Remote Sensing 11 (17), 1976, 2019 | 113 | 2019 |
| The origin and crust/mantle mass balance of Central Andean ignimbrite magmatism constrained by oxygen and strontium isotopes and erupted volumes H Freymuth, M Brandmeier, G Wörner Contributions to Mineralogy and Petrology 169, 1-24, 2015 | 60 | 2015 |
| Compositional variations of ignimbrite magmas in the Central Andes over the past 26 Ma—A multivariate statistical perspective M Brandmeier, G Woerner Lithos 262, 713-728, 2016 | 47 | 2016 |
| New challenges for tafoni research. A new approach to understand processes and weathering rates M Brandmeier, J Kuhlemann, I Krumrei, A Kappler, PW Kubik Earth Surface Processes and Landforms 36 (6), 839-852, 2011 | 47 | 2011 |
| Remote sensing exploration of Nb-Ta-LREE-enriched carbonatite (Epembe/Namibia) R Zimmermann, M Brandmeier, L Andreani, K Mhopjeni, R Gloaguen Remote Sensing 8 (8), 620, 2016 | 44 | 2016 |
| Boosting for mineral prospectivity modeling: A new GIS toolbox M Brandmeier, IG Cabrera Zamora, V Nykänen, M Middleton Natural Resources Research 29 (1), 71-88, 2020 | 37 | 2020 |
| Classification of tree species and standing dead trees with lidar point clouds using two deep neural networks: pointcnn and 3dmfv-net M Hell, M Brandmeier, S Briechle, P Krzystek PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 90 …, 2022 | 21 | 2022 |
| Lithological classification using multi-sensor data and convolutional neural networks M Brandmeier, Y Chen The International Archives of the Photogrammetry, Remote Sensing and Spatial …, 2019 | 16 | 2019 |
| Remote sensing of Carhuarazo volcanic complex using ASTER imagery in Southern Peru to detect alteration zones and volcanic structures–a combined approach of image processing in … M Brandmeier Geocarto International 25 (8), 629-648, 2010 | 16 | 2010 |
| Mapping patterns of mineral alteration in volcanic terrains using ASTER data and field spectrometry in Southern Peru M Brandmeier, S Erasmi, C Hansen, A Höweling, K Nitzsche, T Ohlendorf, ... Journal of South American Earth Sciences 48, 296-314, 2013 | 14 | 2013 |
| DeepForest: Novel deep learning models for land use and land cover classification using multi-temporal and-modal sentinel data of the amazon basin E Cherif, M Hell, M Brandmeier Remote Sensing 14 (19), 5000, 2022 | 13 | 2022 |
| A deep learning approach for calamity assessment using sentinel-2 data D Scharvogel, M Brandmeier, M Weis Forests 11 (12), 1239, 2020 | 13 | 2020 |
| A hierarchical deep-learning approach for rapid windthrow detection on planetscope and high-resolution aerial image data W Deigele, M Brandmeier, C Straub Remote Sensing 12 (13), 2121, 2020 | 13 | 2020 |
| Automated recognition of quasi‐planar ignimbrite sheets as paleosurfaces via robust segmentation of digital elevation models: an example from the Central Andes B Székely, Z Koma, D Karátson, P Dorninger, G Wörner, M Brandmeier, ... Earth Surface Processes and Landforms 39 (10), 1386-1399, 2014 | 9 | 2014 |
| A remote sensing and geospatial statistical approach to understanding distribution and evolution of ignimbrites in the Central Andes with a focus on Southern Peru M Brandmeier | 5 | 2014 |
| New challenges for tafoni research M Brandmeier, J Kuhlemann, I Krumrei, A Kappler, PW Kubik A new approach, 2011 | 4 | 2011 |
| Comparison of different machine-learning algorithms for tree species classification based on sentinel data M Wessel, M Brandmeier, D Tiede, R Seitz, C Straub PFGK18, 2018 | 2 | 2018 |
| Hyperspectral remote sensing exploration of carbonatite-an example from Epembe, Kunene region, Namibia R Zimmermann, M Brandmeier, L Andreani, R Gloaguen EGU General Assembly Conference Abstracts, 8836, 2015 | 2 | 2015 |
| Coupling Deep Learning and GIS for forest damage assessment based on high-resolution remote sensing data. Z Hamdi, M Brandmeier, C Straub Geophysical Research Abstracts 21, 2019 | 1 | 2019 |
