2024

• Ahmed Emam, Timo T. Stomberg and Ribana Roscher (2024) Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery, IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, 2024, Art no. 8500105

• Bolmer, E.; Abulaitijiang, A.; Kusche, J; Roscher, R. (2024) Estimating daily semantic segmentation maps of classified ocean eddies using sea level anomaly data from along-track altimetry, Front. Artif. Intell. 7:1298283

2023

• Burak Ekim, Timo T. Stomberg, Ribana Roscher, Michael Schmitt (2023) MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets], IEEE Geoscience and Remote Sensing Magazine, vol. 11, no. 1

• Kierdorf, J., Roscher, R. (2023) Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower,  IEEE Geoscience and Remote Sensing Letters

• Mohamed Farag, Jana Kierdorf and Ribana Roscher (2023) Inductive Conformal Prediction for Harvest-Readiness Classification of Cauliflower Plants: A Comparative Study of Uncertainty Quantification Methods, 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

• Stomberg, T.T.; Leonhardt, J.; Weber, I.; Roscher, R. (2023) Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery, Front. Artif. Intell. 6:1278118

2022

• Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, Scarlet Stadtler (2022) Global, high-resolution mapping of tropospheric ozone - explainable machine learning and impact of uncertainties, Geoscientific Model Development, vol. 15, pp. 4331-4354

• Drees, L., Weber, I., Rußwurm, M., Roscher, R (2022) Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer, Pattern Recognition: 44th DAGM German Conference, DAGM GCPR 2022, Konstanz, Germany, September 27–30, 2022, Proceedings (pp. 495-510). Cham: Springer International Publishing

• Eike Bolmer, Adili Abulaitijiang, Jürgen Kusche, Ribana Roscher (2022) Occlusion sensitivity analysis of neural network architectures for eddy detection, IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

• Günder, M., Ispizua Yamati, F.R., Kierdorf, J., Roscher, R., Mahlein, A.K., Bauckhage, C. (2022) Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision, GigaScience, Volume 11

• Kierdorf, J., Junker‐Frohn, L.V., Delaney, M., Donoso Olave, M., Burkart, A., Jaenicke, H., Muller, O., Rascher, U., Roscher, R. (2022) GrowliFlower: An image time‐series dataset for GROWth analysis of cauLIFLOWER, Journal of Field Robotics

• Kierdorf, J., Weber, I., Kicherer, A., Zabawa, L., Drees, L., Roscher, R. (2022) Behind the Leaves: Estimation of Occluded Grapevine Berries With Conditional Generative Adversarial Networks, Frontiers in Artificial Intelligence

• Leonhardt, J., Drees, L., Jung, P., Roscher, R. (2022) Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks, Andres, B., Bernard, F., Cremers, D., Frintrop, S., Goldlücke, B., Ihrke, I. (eds) Pattern Recognition. DAGM GCPR 2022. Lecture Notes in Computer Science, vol 13485. Springer, Cham.

• M. Babadi, S. I. Pour, R. Roscher, A. Amiri-Simkooei, and H. Karimi (2022) Long-term drought monitoring of the Zayandehrud River basin (central Iran) using hydroclimatological models and satellite observations, Journal of Applied Remote Sensing, vol. 16, iss. 1, p. 14504

• Marashdeh, Q., Drees, L., Roscher, R. (2022) Semantic UAV Image Segmentation of Mixed Cropping Fields, Dreiländertagung der DGPF, der OVG und der SGPF in Dresden - Publikationen der DGPF, Band 30, S. 140-148

• Miranda, M., Drees, L., Roscher, R. (2022) Controlled Multi-modal Image Generation for Plant Growth Modeling, 26th International Conference on Pattern Recognition (ICPR) (pp. 5118-5124). IEEE.

• S. Stadtler, C. Betancourt, and R. Roscher (2022) Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset, Machine Learning and Knowledge Extraction, vol. 4, iss. 1, p. 150–171

2021

• C. Betancourt, T. Stomberg, S. Stadtler, R. Roscher, M. G. Schultz (2021) AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics, Earth System Science Data Discussions

• D. Tuia, R. Roscher, J. D. Wegner, N. Jacobs, X. Zhu, G. Camps-Valls (2021) Toward a Collective Agenda on AI for Earth Science Data Analysis, IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 2, pp. 88-104

• Drees, L., Junker-Frohn, L. V., Kierdorf, J., Roscher, R. (2021) Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks, Computers and electronics in agriculture 190, 106415

• I. Weber, J. Bongartz, R. Roscher (2021) Artificial and beneficial–Exploiting artificial images for aerial vehicle detection, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 175, p. 158–170

• Mersch, B., Höllen, T., Zhao, K., Stachniss, C., Roscher, R. (2021) Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4888-4895)

• R. Roscher (2021) Deep Self-taught Learning in Remote Sensing, Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, 37-45.

• Stomberg, T., Weber, I., Schmitt, M., Roscher, R. (2021) jUngle-Net: Using explainable machine learning to gain new insights into the appearance of wilderness in satellite imagery, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 317-324

• Weber, I., Bongartz, J., Roscher, R. (2021) ArtifiVe-Potsdam: A Benchmark for Learning with Artificial Objects for Improved Aerial Vehicle Detection, 2021 IEEE International Geoscience and Remote Sensing Symposium (pp. 1214-1217)

2020

• I. Weber, J. Bongartz, R. Roscher (2020) Learning with real-world and artificial data for improved vehicle detection in aerial imagery, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science

• J. Kierdorf, J. Garcke, J. Behley, T. Cheeseman, R. Roscher (2020) What identifies a whale by its fluke? On the benefit of interpretable machine learning for whale identification, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science

• L. Drees, J. Kusche, R. Roscher (2020) Multi-Modal Deep Learning with Sentinel-3 Observations for the Detection of Oceanic Internal Waves, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science

• L. Zabawa, A. Kicherer, L. Klingbeil, R. Töpfer, H. Kuhlmann, R. Roscher (2020) Counting of grapevine berries in images via semantic segmentation using convolutional neural networks, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 164, p. 73–83

• R. Roscher, B. Bohn, M. Duarte, J. Garcke (2020) Explain it to me – Facing Remote Sensing Challenges in the Bio- and Geosciences with Explainable Machine Learning, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science

• R. Roscher, B. Bohn, M. F. Duarte, J. Garcke (2020) Explainable Machine Learning for Scientific Insights and Discoveries, IEEE Access, vol. 8, iss. 1, pp. 42200-42216

• R. Roscher, M. Volpi, C. Mallet, L. Drees, J. D. Wegner (2020) SemCity Toulouse: A Benchmark for Building Instance Segmentation in Satellite Images, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science

2019

• A. Förster, J. Behley, J. Behmann, R. Roscher (2019) Hyperspectral plant disease forecasting using generative adversarial networks, International Geoscience and Remote Sensing Symposium

• L. Strothmann, U. Rascher, R. Roscher (2019) Detection of anomalous grapevine berries using all-convolutional autoencoders, International Geoscience and Remote Sensing Symposium

• L. Zabawa, A. Kicherer, L. Klingbeil, A. Milioto, R. Töpfer, H. Kuhlmann, R. Roscher (2019) Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks, CVPR Workshop on Computer Vision Problems in Plant Phenotyping

2018

• Johannes Oehrlein, Alina Förster, David Schunck, Youness Dehbi, Ribana Roscher, Jan-Henrik Haunert (2018) Inferring Routing Preferences of Bicyclists from Sparse Sets of Trajectories, Conference on Smart Data and Smart Cities

• Katharina Franz, Ribana Roscher, Andres Milioto, Susanne Wenzel, and Jürgen Kusche (2018) Ocean Eddy Identification and Tracking using Neural Networks, IEEE International Geoscience and Remote Sensing Symposium

• Lukas Drees, Ribana Roscher, and Susanne Wenzel (2018) Archetypal Analysis for Sparse Representation-based Hyperspectral Sub-Pixel Quantification, Photogrammetric Engineering & Remote Sensing

2017

• Anika Bettge, Ribana Roscher, Susanne Wenzel (2017) Deep self-taught learning for remote sensing image classification, Proceedings of the 2017 Conference on Big Data from Space

• Anne Braakmann-Folgmann, Ribana Roscher, Susanne Wenzel, Bernd Uebbing, Jürgen Kusche (2017) Sea Level Anomaly Prediction using Recurrent Neural Networks, Proceedings of the 2017 Conference on Big Data from Space

• J. Rosentreter, R. Hagensieker, A. Okujeni, R. Roscher, B. Waske (2017) Sub-pixel mapping of urban areas using EnMAP data and multioutput support vector regression, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

• Lukas Drees, Ribana Roscher (2017) Archetypal analysis for sparse representation-based hyperspectral sub-pixel quantification, ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences (4)

• Ribana Roscher, Bernd Uebbing, Jürgen Kusche (2017) STAR: Spatio-temporal altimeter waveform retracking using sparse representation and conditional random fields, Remote Sensing of Environment (201)

• Ribana Roscher, Lukas Drees, Susanne Wenzel (2017) Sparse Representation-based Archetypal Graphs for Spectral Clustering, IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

• Ron Hagensieker, Ribana Roscher, Johannes Rosentreter, Benjamin Jakimow, Björn Waske (2017) Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data, International Journal of Applied Earth Observation and Geoinformation (63)

2016

• B. Franke, J. Plante, R. Roscher, A. Lee, C. Smyth, A. Hatefi, F. Chen, E. Gil, A. Schwing, A. Selvitella, M. M. Hoffman, R. Grosse, D. Hendricks, and N. Reid (2016) Statistical Inference, Learning and Models in Big Data, International Statistical Review

• B. Mack, R. Roscher, S. Stenzel, H. Feilhauer, S. Schmidtlein, B. Waske (2016) Mapping raised bogs with an iterative one-class classification approach, ISPRS Journal of Photogrammetry and Remote Sensing

• R. Roscher, J. Behmann, A.-K. Mahlein, and L. Plümer (2016) On the Benefit of Topographic Dictionaries for Detecting Disease Symptoms on Hyperspectral 3D Plant Models, Workshop on Hyperspectral Image and Signal Processing

• R. Roscher, J. Behmann, A.-K. Mahlein, J. Dupuis, H. Kuhlmann, and L. Plümer (2016) Detection of Disease Symptoms on Hyperspectral 3D Plant Models, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences

2015

• B. Mack, R. Roscher, B. Waske (2015) Can I Trust My One-Class Classification?, Remote Sensing

• R. Roscher, B. Uebbing, J. Kusche (2015) Spatio-temporal altimeter waveform retracking via sparse representation and conditional random fields, IEEE International Geoscience and Remote Sensing Symposium

• R. Roscher, C. Römer, B. Waske, L. Plümer (2015) Landcover Classification with Self-taught Learning on Archetypal Dictionaries, IEEE International Geoscience and Remote Sensing Symposium

• Ribana Roscher, Björn Waske (2015) Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing

2014

• A. Kicherer, R. Roscher, K. Herzog, W. Förstner, R. Töpfer (2014) Image based Evaluation for the Detection of Cluster Parameters in Grapevine, International Conference on Grapevine Breeding and Genetics

• K. Herzog, R. Roscher, M. Wieland, A. Kicherer, T. Läbe, W. Förstner, H. Kuhlmann, R. Töpfer (2014) Initial steps for high-throughput phenotyping in vineyards, VITIS-Journal of Grapevine Research

• R. Roscher, B. Waske (2014) Superpixel-based classification of hyperspectral data using sparse representation and conditional random fields, IEEE International Geoscience and Remote Sensing Symposium

• R. Roscher, K. Herzog, A. Kunkel, A. Kicherer, R. Töpfer, W. Förstner (2014) Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields, Computers and Electronics in Agriculture