HEIBRiDS

Contact

Supervisors:

Martin Herold (GFZ)

Nadja Klein (KIT)

Weeds are significant contributors (about 12% of global crop production) to crop yield and quality decline. Farmers use different approaches such as chemical or biological herbicides to eliminate weeds. However, excess use of herbicides leads to the pollution of soils, water, and air, putting the above and below ground wildlife biodiversity at risk. Alternative weed mitigation strategies must be designed and promoted. The site-specific weed management (SSWM) approach has been proposed consisting of varying weed management strategies within a crop field to suit the weed population's variation in density, location, and composition. The first step in implementing a SSWM strategy is accurate and timely detection and mapping of weeds. The high temporal, spatial, and spectral remote sensing information is required to capture detailed within-field variability, which can only be met by using the Low Altitude Remote Sensing platform and lightweight hyperspectral imaging sensors that can be deployed locally and at varying conditions.

This research aims to use hyperspectral multi-temporal Low altitude remote-based time-series imagery combined with field data and advanced machine learning (ML) techniques to detect and discriminate weeds in croplands. To achieve this aim, the project will i) monitor the weed population during different growth stages and preprocess of hyperspectral data and field data for better detecting weeds, ii) test a combination of ML and image processing algorithms for detecting weeds across a range of field and growing stage conditions, iii) apply the framework for multi-temporal analysis to track weed distribution and conditions for theuptake by precision farming techniques in our study regions.

Full-length publications

1. E. Celikkan, M. Saberioon, M. Herold and N. Klein (2023). Semantic Segmentation of Crops and Weeds with Probabilistic Modeling and Uncertainty Quantification. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 582-592.
2. E. Celikkan, T. Kunzmann, Y. Yeskaliyev, S. Itzerott, N. Klein, and M. Herold (2025). WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields. Proceedings of the Winter Conference on Applications of Computer Vision (WACV), pp. 4767-4777.

Conference presentations

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Contact

Supervisors:

Yuri Shprits (GFZ)

Guillermo Gallego (TU)

Space weather is a term used to describe hazardous events in the near-Earth space environment that can have adverse effects. Power grids, telecommunication infrastructure and space assets show significant vulnerability to space weather events originating from the Sun. While these effects are largely invisible to the naked eye, in the 21st century operations in space and on the ground significantly depend on the accurate knowledge and forecast of the conditions in the space environment.

Using ground observations of the Sun and physics-based models of the Solar System, it is possible to quantify space weather hazards (e.g., solar wind speed and density) and risks on Earth’s technology. The output of this method provides an estimate of the desired variables. These methods, however do not utilize the vast amount of observations that are available from space, and they are computationally demanding and dominantly physics-based, making them difficult to run in real-time and use all available measurements.

We propose to leverage the growing number of highly detailed multi-spectral images of the Sun to improve predictions of the solar wind streams arriving at the Earth. We propose to exploit the capabilities of modern computer vision and machine learning (ML) techniques to register solar images, analyze them and assimilate them in an empirical (data-driven) model. Through our approach we will develop a novel, data-driven framework directly connecting solar disturbances to its consequences for space weather.

Full-length publications

1. D. Collin, Y. Shprits, S.J. Hofmeister, S. Bianco, and G. Gallego (2025). Forecasting High-Speed Solar Wind Streams from Solar Images. Space Weather. https://doi.org/10.1029/2024SW004125

Conference presentations

1. D. Collin, S. Bianco, G. Gallego, and Y. Shprits. Forecasting solar wind speed from solar EUV images. (Oral and poster presentation), International Workshop on Machine Learning and Computer Vision in Heliophysics, Sofia, Bulgaria, 19-21 April 2023.
2. D. Collin, S. Bianco, G. Gallego, and Y. Shprits. Forecasting solar wind speed by machine learning based on coronal hole characteristics. (Poster presentation), EGU General Assembly , Vienna, Austria, 24–28 April 2023. https://doi.org/10.5194/egusphere-egu23-6968
3. D. Collin, S. Bianco, G. Gallego, and Y. Shprits. Forecasting solar wind speed from solar EUV images. (Oral presentation), IUGG General Assembly, Berlin, Germany, 11-20 July 2023. https://doi.org/10.57757/IUGG23-2070
4. D. Collin, S. Bianco, G. Gallego, and Y. Shprits. Forecasting solar wind speed from coronal holes. (Oral presentation), AGU Fall Meeting, San Francisco, USA, 11–15 December 2023.
5. D. Collin, Y. Shprits, S. Bianco, F. Inceoglu, S. Hofmeister, and G. Gallego. Forecasting solar wind speed from coronal holes and active regions. (Poster presentation), EGU General Assembly, Vienna, Austria, 14–19 April 2024. https://doi.org/10.5194/egusphere-egu24-18676
6. D. Collin, Y. Shprits, S. Hofmeister, S. Bianco, and G. Gallego. Forecasting solar wind speed with solar images. (Oral and poster presentation), International Magnetosphere Coupling Workshop IV, Potsdam, Germany, 3–7 June 2024.
7. D. Collin, Y. Shprits, S. Bianco, S. Hofmeister, and G. Gallego. Forecasting Solar Wind Speed from Solar EUV Images. (Poster presentation), Helmholtz AI Conference, Düsseldorf, Germany, 12-14 Jun 2024.
8. D. Collin, Y. Shprits, S. Bianco, S. Hofmeister, and G. Gallego. Using Distributional Regression to Improve Solar Wind Speed Forecasting from Solar Images. (Oral presentation), European Space Weather Week, Coimbra, Portugal, 4-8 Nov 2024.

Kontakt

Supervisors:

Mahdi Motagh (GFZ)

Setareh Maghsudi (Uni Tübingen)

Kontakt

Supervisors:

Natalia Kliewer (FU)

Carolin Ulrbrich (HZB)

Rutger Schlatmann (HZB)

Kontakt

Supervisors:

Julia Boike (AWI)

Guillermo Gallego (TU)

Contact

Supervisors:

Uwe Ohler (MDC)

Markus Schuelke-Gerstenfeld (Charité)

Single-cell genomics can obtain molecular data for tens of thousands of cells simultaneously. A typical experiment is carried out on a complex sample that contains different cell types, and can measure different cellular properties, such as the number of messenger RNA molecules per cell. Typical tasks include identifying distinct cell types (e.g. via unsupervised embeddings, [1]) or inferring a pseudo-temporal ordering of cells along developmental stages. A particular opportunity arises from single-cell genome accessibility data, which provides information about which of several million gene switches, so called regulatory regions, are accessible/on or inaccessible/off [2]. These data can be analyzed at multiple resolutions: At the level of whole regions, to identify where active switch regions are and to infer which genes they may regulate, or at the level of short DNA sequence patterns within the regions, which are recognized by proteins to specifically activate the switches in e.g. different cell-types. Models to utilize the power of single-cell genomics data, and accessibility in particular, are still in their infancy. The main challenge is that the higher number of cells (i.e. samples) is accompanied by high dropout: the readout covers only a few percent of all variables, and the resulting discrete count data is sparse. Additionally, ground truth experimental data only exists for a handful of scenarios, making it hard to develop practically useful methods that work beyond simulated data.

The project will utilize data from the Schuelke lab to develop deep neural network approaches in the Ohler lab that enable flexible multi-resolution analyses: the goal is to devise models that are able to infer both active regulatory regions and the functional sequence patterns in them, while (a) leveraging data from smaller or larger cell neighborhoods as needed; (b) accounting for confounders such as variable dropout and cell type mixtures; and (c) utilizing auxiliary data from other single-cell experiments.

Full-length publications

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Conference presentations

1. V. Huryn, R. Monti, A.A. Rakowski, and V. Döring. Disentanglement learning for functional genomics data. (Poster presentation), Kipoi Summit. New Horizons in Computational Regulatory Genomics. Zugspitze, Germany, 25-26 September 2023.

Kontakt

Supervisors:

Björn Scheuermann (HU)

Winfried Halle (DLR)

Contact

Supervisors:

Nikolaus Rajewsky (MDC)

Klaus-Robert Müller (TU)

Frederich Klauschen (Charité)

Despite enormous progress in the understanding, early diagnosis and treatment, solid tumors still account for a quarter of deaths. Solid tumors arise from somatic cells through the accumulation of molecular alterations that eventually lead to their uncontrolled proliferation, invasion of healthy tissues and ultimately, distant metastases. In current clinical practice, the best treatment is chosen by assessing clinical presentation, histological tumor type and molecular characteristics, such as oncogenic mutations and, in some cases, gene expression profiles. The evaluation of histomorphological tumor properties in combination with molecular profiles guides risk-adjusted, personalized therapies that aim to optimize outcome for individual patients. However, diagnostic molecular profiling currently mostly relies on techniques performed on bulk tissue without providing any spatial information about the observed molecular tumor properties. While this can already make the interpretation of mutational profiles challenging, spatially resolved profiling becomes indispensable for the molecular analysis of the tumor microenvironment composed of multiple different cell types whose complex interactions influence therapy response. In this context, single-cell sequencing techniques may offer a unique opportunity to offer both high spatial and molecular resolution in the context of complex tumor histology.

Recently, the fruitful interdisciplinary collaboration between clinical, technological, and computational researchers has cultivated rapid progress in the field of digital pathology. AI-based models provide support to diagnostic pathology, for instance by automating tumor identification and tissue classification and cell detection from tumor histology images. Current computational models, however, are limited in their ability to predict tumor molecular characteristics due to the scarcity of paired imaging and molecular training data. Development of spatial transcriptomics assays aim to fill this gap by enabling unbiased, transcriptome-wide profiling of mRNA expression in intact tissue sections. Thus, they represent an ideal source of such paired morphological and molecular data with unprecedented resolution for training the next generation of digital pathology algorithms.

The project aims to push forward the field of digital pathology by predicting gene expression in tissue space from histomorphology alone. To achieve that, we will train deep learning models on the high-resolution gene expression maps provided by spatial transcriptomics co-registered with the corresponding histomorphology images. Advances in explainable AI approaches will be leveraged to reveal which morphological features and areas are exploited by such models to predict gene expression. This would be vital to allow for a transparent decision process, crucial for medical applications, and would also deepen our understanding of tumor biology by correlating tissue and cellular composition/histomorphology of the tumor and its microenvironment with function. We anticipate such a computational approach to have a positive impact on clinical practices by facilitating the prediction of molecular properties from routine diagnostic H&E images and thus to complement or even partially replace molecular testing.

Full-length publications

1. T.M. Pentimalli, S. Schallenberg, D. León-Periñán, ..., F. Klauschen, and N. Rajewsky (2023). High-resolution molecular atlas of a lung tumor in 3D. bioRxiv. https://doi.org/10.1101/2023.05.10.539644
2. M. Schott, D. León-Periñán,.., T.M. Pentimalli, …, N. Karaiskos, and N. Rajewsky (2024). Open-ST: High-resolution spatial transcriptomics in 3D. Cell. https://doi.org/10.1016/j.cell.2024.05.055

Conference presentations

1. D. León-Periñán, N. Karaiskos, M. Schott, E. Splendiani, E. Senel, and N. Rajewsky. Computational methods for high-resolution spatial transcriptomics. (Poster presentation), VIB Spatial Omics, Ghent, Belgium, 13-14 June 2024.

Kontakt

Supervisors:

Jürgen Oberst (DLR)

Odej Kao (TU)

Contact

Supervisors:

David Berge (DESY)

Matthias Weidlich (HU)

The sensitivity of ground-based gamma-ray telescopes is ultimately limited by their ability to reconstruct the properties of gamma-rays from the particle shower produced when they interact with the atmosphere and reject the much more numerous background of showers from charged cosmic rays. 

At this time, array sensitivity can be considered as “software limited” and as such has continuously improved in the past 20 years by exploiting advanced image reconstruction and classification algorithms [1] and multivariate classification techniques such as boosted decision trees [2] and neural networks. It is clear that using modern machine learning techniques can improve this performance further. Yet, telescope arrays typically make observations under a huge range of context conditions. As such, it is of utmost importance to integrate contextual data on the operation of the telescope as well as on atmospheric conditions into any data analysis pipeline. This is challenging not only because of the heterogeneity of contextual data, but also from a computational point of view. 

This PhD project sets out to develop data processing pipelines that combine deep learning techniques for gamma-ray telescope data with diverse types of contextual data to dramatically improve the telescopes’ sensitivity. To this end, we will draw on initial results on employing state-of-the-art machine learning techniques to gamma-ray telescope data [3] as well as technical insights into integration of static datasets into stream processing pipelines [4].

Full-length publications

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Conference presentations

1. A. Mehta. Machine Learning for Imaging Atmospheric Cherenkov Telescope (IACT) Background Rejection. H.E.S.S. Collaboration Meeting, Bordeaux, France, 24-29 September 2023.

Kontakt

Supervisors:

Martin Kircher (Charité)

Dieter Beule (MDC)

Ulf Leser (HU)

Kontakt

Supervisors:

Volker Markl (TU)

Uwe Ohler (MDC)

Kontakt

Supervisors:

Janik Wolters (DLR)

Guillermo Gallego (TU)

Contact

Supervisors:

Christiane Becker (HZB)

Christof Schütte (FU)

Sven Burger (ZIB)

With the growing share of photovoltaic (PV) solar energy in the global energy generation capacity, building-integrated photovoltaics (BIPV) gains increasing importance. For BIPV, aesthetical factors, such as color, play a larger role than for large industrial PV fields. The structural color of PV modules and surface texturing for minimizing reflective losses and for self-cleaning have to be considered for accurately estimating the optical performance and, subsequently, the annual energy yield. This raises several issues which are hardly considered in state-of-the-art numerical methods, which are used for planning PV systems and for estimating their performance: Which specific module surface treatments ensure maximum energy yield? How does the module appear visually to an observer throughout the day and year? How do the spectral albedo of the surroundings, local weather conditions and local shadowing affect the result? How can the complex environments in building-integrated and bifacial solar modules be simulated as efficiently as possible?

This project aims to develop an all-encompassing optical modeling and optimization toolbox for individual PV modules considering the full optical circumstances from specifically textured module surfaces to local solar irradiance conditions. Together with urban planners and architects, desirable color effects will be identified. Metrics will be developed in order to quantify the aesthetical effect of color appearance of BIPV modules. The PhD student will model PV modules with different textured surfaces and coloring techniques, and account for shadowing and spectral reflection of the surroundings, e.g. from overgrown ground and trees.

Full-length publications

1. J. Schaible, H. Winarto, V. Škorjanc, D. Yoo, L. Zimmermann, K. Jäger, I. Sekulic, P.-I. Schneider, S. Burger, A. Wessels, B. Bläsi, and C. Becker (2024). Optimizing Aesthetic Appearance of Perovskite Solar Cells Using Color Filters. Solar RRL. https://doi.org/10.1002/solr.202400627

Conference presentations

1. J. Schaible, B. Nouri, T. Kotzab, M. Loevenich, N. Blum, A. Hammer, K. Jäger, C. Becker and S. Wilbert. Application of Nowcasting to Reduce the Impact of Irradiance Ramps on PV Power Plants. (Oral presentation), EU PVSEC, Lisbon, Portugal, 18-22 September 2023.
2. J. Schaible, H. Winarto, D. Yoo, L. Zimmermann, A. Wessels, K. Jäger, B. Bläsi, S. Burger, C. Becker. On aesthetical appearance of colored perovskite solar modules. (Oral presentation), SPIE Photonics Europe, Strasbourg, France, 8-12 April 2024.
3. J. Schaible, H. Winarto, D. Yoo, L. Zimmermann, A. Wessels, K. Jäger, B. Bläsi, S. Burger, C. Becker. On aesthetical appearance of colored perovskite solar modules. (Poster presentation),16th Annual Meeting Photonic Devices (AMPD2024), Berlin, Germany, 17-19 April 2024.

Kontakt

Supervisors:

Matthias Weidlich (HU)

Elisa Pueschel (DESY)

Kontakt

Supervisors:

Natalia Kliewer (FU)

Rutger Schlatmann (HZB)

Christian Meske (Ruhr-University of Bochum)

Kontakt

Supervisors:

Ulrike Herzschuh (AWI)

Begüm Demir (TU)

Kontakt

Supervisors:

Andrea Cominola (TU)

Heidi Kreibich (GFZ)

Kontakt

Supervisors:

Dagmar Kainmueller (MDC)

Andreas Hocke (Charité)

Marina Höhne (TU-Berlin)