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Mögliche Themen:

Bitte beachten Sie, dass bei mir eine Version der Arbeit als gedruckte Version abgegeben werden muss.

Im Bereich der Umweltferenerkundung sind Themen zu Weinbau, Forst und Klimatologie möglich. 

Zudem interessieren mich momentan Anwendungen von Foundation AI in Bezug auf räumliche Fragestellungen. 

Promotionen:

Maximilian Hell (M.Eng.):  Kl an Radardaten (Zeitreihen) und Spektraldaten des Copernicus Programms zur Landnutzungsklassifikation

Abgeschlossene Arbeiten:

Masterarbeiten:

2023

Madeleine Speck (B.Eng.): Habitat characteristics and the decline of Erebia epistygne population - a Remote Sensing and GIS study.

Anja Kraus (B.Eng.): Impact of Climate Change on Soil Moisturein viticulture: A time series analysis

Adrian Meyer-Spelbrink (B.Eng.): Monitoring Vineyard Plant Health through Time-Series Analysis of Vegetation Indices and In-Situ Soil Moisture Measurements

Julia Anwander (B.Eng.): Evaluating different Deep Learning and Machine Learning approaches for tree health classification in the Black Forest, Harz region and Göttinger Forest

Levin Krämer (B.Eng.): Analyse der visuellen Auswirkungen des Ausbaus von Windenergieanlagen in der Planungsregion Mittelhessen im Rahmen des Wind-an-Land-Gesetzes anhand von Sichtbarkeitsanalysen, Landnutzungsklassen und Schutzgebieten

2021

Maximilian Hell (B.Eng.): Tree Species Classification using Deep Neural Networks on Lidar Point Clouds. (cooperation with Prof. Dr. Peter Krzystek)

2020

Wolfgang Deigele (BSc): Wind throw detection using deep learning on PlanetScope and high-resolution aerial images (cooperation with TUM. Prof. T. Kolbe)

Daniel Scharvogel (BSc): Detection of windthrows from Sentinel-2 data based on Deep Learning Algorithms (cooperation with University of Applied Sciences Weihnstephan-Triesdorf)

Eya Cherif (BSc): Synergetic use of Sentinel-2 and multi-temporal Sentinel-1 data for Land cover classification using advanced Deep Learning algorithms (cooperation with University of Passau, Prof. H. Kosch)

2019

Yuanze Chen (BSc): Lithological Classification based on Convolutional Neural Networks (CNNs) using multi-sensor data (coopertion with TUM, Prof. T. Huckle)

Nikolaos-Ioannis Bountos (BSc): Subpixel Classification of anthropogenic features using Deep-Learning on Sentinel-2 data (coopertion with TUM, Prof. T. Huckle)

Arthur Freddy Tchuente Tagne (BSc): Evaluation of boosting algorithms for exploration targeting using ArcGIS in the Mount Isa Inlier, Australia (cooperation with TU Clausthal, Prof. W. Busch)

2018

Zayd Mahmoud Hamdi (BSc): Forest Damage Assessment Using Deep Lerning on high-resolution Remote Sensing Data (cooperation with TUM, Prof. D. Straub)

2017

Mathias Wessel (BSc): The potential of Sentinel-2 data to classify tree species (cooperation with the University of Salzburg, Dirk Tiede)

Irving Gibran Cabrera Zamora (BSc): The use of Boosting Methods for Mineral Prospectivity Mapping within the ArcGIS Platform (cooperation with TUM, Prof. T. Huckle)

Bachelor Thesis:

2023

Marco Lutz: Die Austrocknung des Aralsees und ihre Flogen. Zeitreihenanalyse anhand Landsat Collection 2 Daten in der Google Earth Engine.

Emilie Lüdicke: Evaluierung vortrainierter KI-Algorithmen zur automatischen Erkennung von Siedlungen in historischen Topographischen Karten.

2022

Louisa Rall: Multitemporal Vocano Monitoring at Cumbre Vieja Volcano using Sentinel-2 data.

Nicola Schöpplein: Evaluating different methods for flood mapping using Sentinel-1 data from Sri Lanka

Lehrgebiete

• System Erde/Geowissenschaften
• Maschinelles Lernen/Computer Vision
• Fernerkundung/ GIS

Publikationen

Publication list

2024

Speckenwirth, S.; Brandmeier, M.; Paczkowski, S. TreeSeg—A Toolbox for Fully Automated Tree Crown Segmentation Based on High-Resolution Multispectral UAV Data. Remote Sens. 2024, 16, 3660. doi.org/10.3390/rs16193660

Hell, M.; Brandmeier, M. Identifying Plausible Labels from Noisy Training Data for a Land Use and Land Cover Classification Application in Amazônia Legal. Remote Sens. 2024, 16, 2080. doi.org/10.3390/rs16122080

Brandmeier, M.; Heßdörfer, D.; Siebenlist, P.; Meyer-Spelbrink, A.; Kraus, A. Time Series Analysis of Multisensor Data for Precision Viticulture—Assessing Microscale Variations in Plant Development with Respect to Irrigation and Topography. Remote Sens. 2024, 16, 1419. doi.org/10.3390/rs16081419

Anwander, J.; Brandmeier, M.; Paczkowski, S.; Neubert, T.; Paczkowska, M. Evaluating Different Deep Learning Approaches for Tree Health Classification Using High-Resolution Multispectral UAV Data in the Black Forest, Harz Region, and Göttinger Forest. Remote Sens. 2024, 16, 561.

2023

Hell, M., Brandmeier, M., Nüchter, A.: Transferability of Deep Learning Models for Land Use/Land Cover Classification. Publikationen der DGPF, Band 31, 2023.

I. Hahn, M. R. S. von der Wense Goncalves P. and M. Brandmeier, and G. Markl, “Habitat use of zygaena brizae and zygaena cynarae (lepidoptera, zygaenidae) in southern france.,” Nachr. entomol. Ver. Apollo, N.F. 44 (2), 65–80., 2023.

2022

Erbe, K., Brandmeier, M., Schmitt, M., Donaubauer, A., Liebscher, J.A., Kolbe, T.:  Detektion von Fahrradständern in Luftbildern mittels Deep Learning, Publikationen der DGPF, Band 30, 2022

Cherif, E.; Hell, M.; Brandmeier, M. DeepForest: Novel Deep Learning Models for Land Use and Land Cover Classification Using Multi-Temporal and -Modal Sentinel Data of the Amazon Basin. Remote Sens. 2022, 14, 5000. doi.org/10.3390/rs14195000

Hell, Maximilian, Brandmeier, M., Briechle, S., Krzystek, P.: Classification of Tree Species and Standing Dead Trees with Lidar Point Clouds Using Two Deep Neural Networks: PointCNN and 3DmFV-Net. Journal of Photogrammetry Remote Sensing and Geoinformation Science. DOI: 10.1007/s41064-022-00200-4

2020

Scharvogel, D., Brandmeier, M., Weis, M. (2020): A Deep Learning Approach for Calamity Assessment Using Sentinel-2 Data. Forests 11(12).

Deigele, W.; Brandmeier, M.; Straub, C. (2020): A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data. Remote Sensing 12(2121).

2019

Brandmeier, M., Chen, Y. (2019): Lithological classification using multi-sensor data and convolutional neural networks. ISPRS Archives. Volume: XLII-2-W16-55-2019.

Hamdi, Z., Brandmeier, M., Straub, C. (2019): Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data. Remote Sensing 11(17).

Brandmeier, M. (2019): The Anatomy of Supervolcanoes. In: GIS for Science: Applying Mapping and Spatial Analytics. Esri Press.

Brandmeier, M., Cabrera, I., Nykänen, V., Middleton, M. (2019): The potential of boosting for prospectivity modelling: Introducing a new GIS toolbox. Natural Resources Research. doi.org/10.1007/s11053-019-09483-8.

2018

Wessel, M; Brandmeier, M.; Tiede, D. (2018) Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data. Remote Sensing 10(9).

2017

Brandmeier, M., Wessel, M. (2017) Workflows für Bilddaten und Big Data Analytics - Das Potenzial von Sentinel-2-Daten zur Baumartenklassifizierung. In: Meinel, Gotthard; Schumacher, Ulrich; Schwarz, Steffen; Richter, Benjamin (Hrsg.): Flächennutzungsmonitoring IX: Nachhaltigkeit der Siedlungs- und Verkehrsentwicklung?. Berlin : Rhombos-Verlag, 2017, (IÖR-Schriften; 73), S. 135-142

2016

Brandmeier, M. and Wörner, G. (2016): Compositional variations of ignimbrite magmas in the Central Andes over the past 26 Ma — A multivariate statistical perspective. Lithos 262, 713-728.

Zimmermann, R., Brandmeier, M., Andreani, L., Mhopjeni, K. and Gloaguen, R. (2016) Remote Sensing Exploration of Nb-Ta-LREE-Enriched Carbonatite (Epembe/Namibia). Remote Sensing 8, 620.

2015

Freymuth, H., Brandmeier, M., Wörner, G. (2015): The origin and crust/mantle mass balance of Central Andean ignimbrite magmatism constrained by oxygen and strontium isotopes and erupted volumes: Contributions to Mineralogy and Petrology, v. 169, p. 1-24.

2014

Székely, B., Koma, Z., Karátson, D., Dorninger, P., Wörner, G., Brandmeier, M., Nothegger, C. (2014): Automated recognition of quasi‐planar ignimbrite sheets as paleosurfaces via robust segmentation of digital elevation models: an example from the Central Andes. Earth Surface Processes and Landforms.

2013

Brandmeier, M., Erasmi, S., Hansen, C., Höweling, A., Nitzsche, K., Ohlendorf, T., Mamani, M. and Wörner, G. (2013): Mapping patterns of mineral alteration in volcanic terrains using ASTER data and field spectrometry in Southern Peru. Journal of South American Earth Sciences 48, 296-314.

2011

Brandmeier, M., Kuhlemann, J., Krumrei, I., Kappler, A., and Kubik, P.W. (2011): New challenges for tafoni research. A new approach to understand processes and weathering rates: Earth Surface Processes and Landforms, v. 36, p. 839-852.

2010

Brandmeier, M. (2010): 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 ENVI and ArcGIS/ArcScene: Geocarto International, v. 25, p. 629-648

Vita

Curriculum vitae

2021 Professorin für Geoinformatik und Fernerkundung

2017 Associate Researcher GFZ Potsdam

2016-2021 Senior Scientist Esri Deutschland

2014-2016 Postdoc Helmholtz Institut Dresden-Rossendorf

2014 Promotion (Dr.rer.nat.), Georg-August-Universität Göttingen

Titel der Dissertation: "A remote sensing and geospatial statstical approach to understanding distribution and evolution of ignimbrites in the Central Andes with a focus on Southern Peru"

2010 Auslandsaufenthalt Australien (University of Wollongong, CSIRO)

2008 Diplom Geographie, Geologie und Geochemie, Eberhard-Karls-Universität Tübingen

Titel der Diplomarbeit: "Raten und Prozesse der Tafoniverwitterung auf Korsika"

2004/2005 Auslandssemester Universidad Nacional San Miguel de Tucumán, Argentinien

2004 Vordiplom Geographie, Lateinamerikanistik und Umweltpsychologie, Katholische Universität Eichstätt-Ingolstadt

2002 Staatlich geprüfter Management Assistant (Tourismus), Freiburg

Membership

European Geoscience Union (EGU)

International Society for Photogrammetry and Remote Sensing (ISPRS)

Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformatik (DGPF)

Member of the Editorial Board Zeitschrift für Geodäsie, Geoinformatik und Landmanagement

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