@article{CherifHellBrandmeier, author = {Cherif, Eya and Hell, Maximilian and Brandmeier, Melanie}, title = {DeepForest: novel deep learning models for land use and land cover classification using multi-temporal and -modal sentinel data of the amazon basin}, series = {Remote Sensing}, volume = {14}, journal = {Remote Sensing}, number = {19}, issn = {2072-4292}, doi = {10.3390/rs14195000}, abstract = {Land use and land cover (LULC) mapping is a powerful tool for monitoring large areas. For the Amazon rainforest, automated mapping is of critical importance, as land cover is changing rapidly due to forest degradation and deforestation. Several research groups have addressed this challenge by conducting local surveys and producing maps using freely available remote sensing data. However, automating the process of large-scale land cover mapping remains one of the biggest challenges in the remote sensing community. One issue when using supervised learning is the scarcity of labeled training data. One way to address this problem is to make use of already available maps produced with (semi-) automated classifiers. This is also known as weakly supervised learning. The present study aims to develop novel methods for automated LULC classification in the cloud-prone Amazon basin (Brazil) based on the labels from the MapBiomas project, which include twelve classes. We investigate different fusion techniques for multi-spectral Sentinel-2 data and synthetic aperture radar Sentinel-1 time-series from 2018. The newly designed deep learning architectures—DeepForest-1 and DeepForest-2—utilize spatiotemporal characteristics, as well as multi-scale representations of the data. In several data scenarios, the models are compared to state-of-the-art (SotA) models, such as U-Net and DeepLab. The proposed networks reach an overall accuracy of up to 75.0\%, similar to the SotA models. However, the novel approaches outperform the SotA models with respect to underrepresented classes. Forest, savanna and crop were mapped best, with F1 scores up to 85.0\% when combining multi-modal data, compared to 81.6\% reached by DeepLab. Furthermore, in a qualitative analysis, we highlight that the classifiers sometimes outperform the inaccurate labels.}, language = {en} } @article{HellBrandmeierBriechleetal., author = {Hell, Maximilian and Brandmeier, Melanie and Briechle, Sebastian and Krzystek, Peter}, title = {Classification of Tree Species and Standing Dead Trees with Lidar Point Clouds Using Two Deep Neural Networks: PointCNN and 3DmFV-Net}, series = {PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science}, volume = {90}, journal = {PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science}, issn = {2512-2819}, doi = {10.1007/s41064-022-00200-4}, pages = {103 -- 121}, abstract = {Knowledge about tree species distribution is important for forest management and for modeling and protecting biodiversity in forests. Methods based on images are inherently limited to the forest canopy. Airborne lidar data provide information about the trees' geometric structure, as well as trees beneath the upper canopy layer. In this paper, the potential of two deep learning architectures (PointCNN, 3DmFV-Net) for classification of four different tree classes is evaluated using a lidar dataset acquired at the Bavarian Forest National Park (BFNP) in a leaf-on situation with a maximum point density of about 80 pts/m2. Especially in the case of BFNP, dead wood plays a key role in forest biodiversity. Thus, the presented approaches are applied to the combined classification of living and dead trees. A total of 2721 single trees were delineated in advance using a normalized cut segmentation. The trees were manually labeled into four tree classes (coniferous, deciduous, standing dead tree with crown, and snag). Moreover, a multispectral orthophoto provided additional features, namely the Normalized Difference Vegetation Index. PointCNN with 3D points, laser intensity, and multispectral features resulted in a test accuracy of up to 87.0\%. This highlights the potential of deep learning on point clouds in forestry. In contrast, 3DmFV-Net achieved a test accuracy of 73.2\% for the same dataset using only the 3D coordinates of the laser points. The results show that the data fusion of lidar and multispectral data is invaluable for differentiation of the tree classes. Classification accuracy increases by up to 16.3\% points when adding features generated from the multispectral orthophoto.}, language = {en} } @inproceedings{ErbeBrandmeierSchmittetal., author = {Erbe, Karin and Brandmeier, Melanie and Schmitt, Michael and Donbauer, Andreas and Liebscher, Jan-Andreas and Kolbe, Thomas}, title = {Detektion von Fahrradst{\"a}ndern in Luftbildern mittels Deep Learning}, series = {42. Wissenschaftlich-Technische Jahrestagung der DGPF. 5.-6. Oktober 2022 in Dresden}, volume = {30}, booktitle = {42. Wissenschaftlich-Technische Jahrestagung der DGPF. 5.-6. Oktober 2022 in Dresden}, editor = {Kersten, Thomas P. and Tilly, Nora}, issn = {0942-2870}, doi = {10.24407/KXP:1795622415}, pages = {27 -- 39}, language = {de} } @article{ZimmermannBrandmeierAndreanietal., author = {Zimmermann, Robert and Brandmeier, Melanie and Andreani, Louis and Mhopjeni, Kombada and Gloaguen, Richard}, title = {Remote Sensing Exploration of Nb-Ta-LREE-Enriched Carbonatite (Epembe/Namibia)}, series = {Remote Sensing}, volume = {8}, journal = {Remote Sensing}, number = {8}, issn = {2072-4292}, doi = {10.3390/rs8080620}, pages = {620 -- 620}, language = {en} } @article{ScharvogelBrandmeierWeis, author = {Scharvogel, Daniel and Brandmeier, Melanie and Weis, Manuel}, title = {A Deep Learning Approach for Calamity Assessment Using Sentinel-2 Data}, series = {Forests}, volume = {11}, journal = {Forests}, number = {12}, issn = {1999-4907}, doi = {10.3390/f11121239}, pages = {1239 -- 1239}, language = {en} } @inproceedings{HellBrandmeierNuechter, author = {Hell, Maximilian and Brandmeier, Melanie and N{\"u}chter, Andreas}, title = {Transferability of Deep Learning Models for Land Use/Land Cover Classification}, series = {43. Wissenschaftlich-Technische Jahrestagung der DGPF in M{\"u}nchen. 22.-23. M{\"a}rz 2023 in M{\"u}nchen - Publikationen der DGPF}, volume = {31}, booktitle = {43. Wissenschaftlich-Technische Jahrestagung der DGPF in M{\"u}nchen. 22.-23. M{\"a}rz 2023 in M{\"u}nchen - Publikationen der DGPF}, editor = {Kersten, Thomas P. and Tilly, Nora}, doi = {10.24407/KXP:1841078182}, pages = {142 -- 149}, language = {en} } @incollection{Brandmeier, author = {Brandmeier, Melanie}, title = {The Anatomy of Supervolcanoes}, series = {GIS for Science: Applying Mapping and Spatial Analytics}, booktitle = {GIS for Science: Applying Mapping and Spatial Analytics}, publisher = {Esri}, language = {en} } @article{DeigeleBrandmeierStraub, author = {Deigele, Wolfgang and Brandmeier, Melanie and Straub, Christoph}, title = {A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data}, series = {Remote Sensing}, volume = {12}, journal = {Remote Sensing}, number = {13}, issn = {2072-4292}, doi = {10.3390/rs12132121}, pages = {2121 -- 2121}, abstract = {Forest damage due to storms causes economic loss and requires a fast response to prevent further damage such as bark beetle infestations. By using Convolutional Neural Networks (CNNs) in conjunction with a GIS, we aim at completely streamlining the detection and mapping process for forest agencies. We developed and tested different CNNs for rapid windthrow detection based on PlanetScope satellite data and high-resolution aerial image data. Depending on the meteorological situation after the storm, PlanetScope data might be rapidly available due to its high temporal resolution, while the acquisition of high-resolution airborne data often takes weeks to a month and is, therefore, used in a second step for more detailed mapping. The study area is located in Bavaria, Germany (ca. 165 km2), and labels for damaged areas were provided by the Bavarian State Institute of Forestry (LWF). Modifications of a U-Net architecture were compared to other approaches using transfer learning (e.g., VGG19) to find the most efficient architecture for the task on both datasets while keeping the computational time low. A custom implementation of U-Net proved to be more accurate than transfer learning, especially on medium (3 m) resolution PlanetScope imagery (intersection over union score (IoU) 0.55) where transfer learning completely failed. Results for transfer learning based on VGG19 on high-resolution aerial image data are comparable to results from the custom U-Net architecture (IoU 0.76 vs. 0.73). When using both architectures on a dataset from a different area (located in Hesse, Germany), however, we find that the custom implementations have problems generalizing on aerial image data while VGG19 still detects most damage in these images. For PlanetScope data, VGG19 again fails while U-Net achieves reasonable mappings. Results highlight the potential of Deep Learning algorithms to detect damaged areas with an IoU of 0.73 on airborne data and 0.55 on Planet Dove data. The proposed workflow with complete integration into ArcGIS is well-suited for rapid first assessments after a storm event that allows for better planning of the flight campaign followed by detailed mapping in a second stage.}, language = {en} } @incollection{BrandmeierWessel, author = {Brandmeier, Melanie and Wessel, M.}, title = {Workflows f{\"u}r Bilddaten und Big Data Analytics - Das Potenzial von Sentinel-2-Daten zur Baumartenklassifizierung}, series = {Fl{\"a}chennutzungsmonitoring. IX: Nachhaltigkeit der Siedlungs- und Verkehrsentwicklung?}, booktitle = {Fl{\"a}chennutzungsmonitoring. IX: Nachhaltigkeit der Siedlungs- und Verkehrsentwicklung?}, number = {Band 73}, editor = {Meinel, Gotthard and Schumacher, Ulrich and Schwarz, Steffen and Richter, Benjamin and f{\"u}r {\"O}kologische Raumentwicklung, Leibniz-Institut}, publisher = {Rhombos}, address = {Berlin}, isbn = {978-3-944101-73-6}, pages = {135 -- 142}, language = {de} } @article{HamdiBrandmeierStraub, author = {Hamdi, Zayd Mahmoud and Brandmeier, Melanie and Straub, Christoph}, title = {Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {17}, issn = {2072-4292}, doi = {10.3390/rs11171976}, pages = {1976 -- 1976}, language = {en} }