no code implementations • 8 Dec 2023 • Ribana Roscher, Marc Rußwurm, Caroline Gevaert, Michael Kampffmeyer, Jefersson A. dos Santos, Maria Vakalopoulou, Ronny Hänsch, Stine Hansen, Keiller Nogueira, Jonathan Prexl, Devis Tuia
These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches.
no code implementations • 6 Dec 2023 • Ribana Roscher, Lukas Roth, Cyrill Stachniss, Achim Walter
In response to the increasing global demand for food, feed, fiber, and fuel, digital agriculture is rapidly evolving to meet these demands while reducing environmental impact.
1 code implementation • 6 Dec 2023 • Lukas Drees, Dereje T. Demie, Madhuri R. Paul, Johannes Leonhardt, Sabine J. Seidel, Thomas F. Döring, Ribana Roscher
A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment.
1 code implementation • 15 Nov 2023 • Ahmed Emam, Mohamed Farag, Ribana Roscher
This framework combines explainable machine learning and uncertainty quantification to assess and explain naturalness.
1 code implementation • 15 Nov 2023 • Ahmed Emam, Timo T. Stomberg, Ribana Roscher
Our proposed framework produces more precise attribution maps pinpointing the designating patterns forming the natural authenticity of protected areas.
no code implementations • 24 May 2023 • Jana Kierdorf, Ribana Roscher
Cauliflower is a hand-harvested crop that must fulfill high-quality standards in sales making the timing of harvest important.
1 code implementation • 16 Dec 2022 • Mohamad Hakam Shams Eddin, Ribana Roscher, Juergen Gall
Climate change is expected to intensify and increase extreme events in the weather cycle.
1 code implementation • 5 Dec 2022 • Burak Ekim, Timo T. Stomberg, Ribana Roscher, Michael Schmitt
Antrophonegic pressure (i. e. human influence) on the environment is one of the largest causes of the loss of biological diversity.
no code implementations • 24 Oct 2022 • Ivica Obadic, Ribana Roscher, Dario Augusto Borges Oliveira, Xiao Xiang Zhu
Using explainable machine learning to reveal the inner workings of these models is an important step towards improving stakeholders' trust and efficient agriculture monitoring.
no code implementations • 1 Apr 2022 • Jana Kierdorf, Laura Verena Junker-Frohn, Mike Delaney, Mariele Donoso Olave, Andreas Burkart, Hannah Jaenicke, Onno Muller, Uwe Rascher, Ribana Roscher
The dataset contains collected phenotypic traits of 740 plants, including the developmental stage as well as plant and cauliflower size.
1 code implementation • 1 Mar 2022 • Timo T. Stomberg, Taylor Stone, Johannes Leonhardt, Immanuel Weber, Ribana Roscher
Occluding certain activations in an interpretable artificial neural network we complete a comprehensive sensitivity analysis regarding wild and anthropogenic characteristics.
1 code implementation • 8 Jan 2022 • Maurice Günder, Facundo R. Ispizua Yamati, Jana Kierdorf, Ribana Roscher, Anne-Katrin Mahlein, Christian Bauckhage
Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.
no code implementations • 7 Jul 2021 • Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, JongSeok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, Xiao Xiang Zhu
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
no code implementations • 21 May 2021 • Jana Kierdorf, Immanuel Weber, Anna Kicherer, Laura Zabawa, Lukas Drees, Ribana Roscher
In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest.
no code implementations • 17 May 2021 • Lukas Drees, Laura Verena Junker-Frohn, Jana Kierdorf, Ribana Roscher
Farmers frequently assess plant growth and performance as basis for making decisions when to take action in the field, such as fertilization, weed control, or harvesting.
1 code implementation • 11 Apr 2021 • Devis Tuia, Ribana Roscher, Jan Dirk Wegner, Nathan Jacobs, Xiao Xiang Zhu, Gustau Camps-Valls
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer.
no code implementations • 7 Apr 2021 • Immanuel Weber, Jens Bongartz, Ribana Roscher
One of the most prominent applications is the detection of vehicles, for which deep learning approaches are increasingly used.
no code implementations • 29 Apr 2020 • Laura Zabawa, Anna Kicherer, Lasse Klingbeil, Reinhard Töpfer, Heiner Kuhlmann, Ribana Roscher
The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems.
no code implementations • 21 May 2019 • Ribana Roscher, Bastian Bohn, Marco F. Duarte, Jochen Garcke
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data.
no code implementations • 1 May 2019 • Laura Zabawa, Anna Kicherer, Lasse Klingbeil, Andres Milioto, Reinhard Töpfer, Heiner Kuhlmann, Ribana Roscher
We count single berries in images to avoid the error-prone detection of grapevine clusters.
no code implementations • 20 Mar 2018 • Katharina Franz, Ribana Roscher, Andres Milioto, Susanne Wenzel, Jürgen Kusche
We show the detection and tracking results on sea level anomalies (SLA) data from the area of Australia and the East Australia current, and compare our two eddy detection and tracking approaches to identify the most robust and objective method.
no code implementations • 8 Feb 2018 • Lukas Drees, Ribana Roscher, Susanne Wenzel
In our experiments, the estimation of the automatically derived elementary spectra is compared to the estimation obtained by a manually designed spectral library by means of reconstruction error, mean absolute error of the fraction estimates, sum of fractions, $R^2$, and the number of used elementary spectra.
no code implementations • 15 Dec 2017 • Ribana Roscher, Katja Herzog, Annemarie Kunkel, Anna Kicherer, Reinhard Töpfer, Wolfgang Förstner
In the present study an automated image analyzing framework was developed in order to estimate the size of grapevine berries from images in a high-throughput manner.
no code implementations • 19 Oct 2017 • Anika Bettge, Ribana Roscher, Susanne Wenzel
This paper addresses the land cover classification task for remote sensing images by deep self-taught learning.
no code implementations • 19 Oct 2017 • Anne Braakmann-Folgmann, Ribana Roscher, Susanne Wenzel, Bernd Uebbing, Jürgen Kusche
We develop a combination of a convolutional neural network (CNN) and a recurrent neural network (RNN) to ana-lyse both the spatial and the temporal evolution of sea level and to suggest an independent, accurate method to predict interannual sea level anomalies (SLA).
no code implementations • 22 Sep 2017 • Ron Hagensieker, Ribana Roscher, Johannes Rosentreter, Benjamin Jakimow, Björn Waske
Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information.
no code implementations • 22 Sep 2017 • Ribana Roscher, Bernd Uebbing, Jürgen Kusche
We show that STAR enables the derivation of sea surface heights over the open ocean as well as over coastal regions of at least the same quality as compared to existing retracking methods, but for a larger number of cycles and thus retaining more useful data.
no code implementations • 20 Aug 2017 • Ribana Roscher, Björn Waske, Wolfgang Förstner
The performance of the IVM in comparison to support vector machines (SVM) is evaluated in terms of accuracy and experiments are conducted to assess the potential of the probabilistic outputs of the IVM.
no code implementations • 20 Aug 2017 • Ribana Roscher, Björn Waske
A combination of shapelets and spectral information are represented in an undercomplete spatial-spectral dictionary for each individual patch, where the elements of the dictionary are linearly combined to a sparse representation of the patch.
Classification Of Hyperspectral Images General Classification +1
no code implementations • 9 Sep 2015 • Beate Franke, Jean-François Plante, Ribana Roscher, Annie Lee, Cathal Smyth, Armin Hatefi, Fuqi Chen, Einat Gil, Alexander Schwing, Alessandro Selvitella, Michael M. Hoffman, Roger Grosse, Dieter Hendricks, Nancy Reid
The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context.