جست‌وجوی استاد و پژوهشگر

• Wireless Communication Networks
• Distributed Systems
• Sensor Networks
• +۸ مورد دیگر

Prof. Horst Hellbrück is an esteemed academic at the Lübeck University of Applied Sciences , where he holds a professorship in Communication Systems and Distributed Systems since 2008. He leads the Center of Excellence CoSA (Communications - Systems - Applications) and focuses on wireless mobile networks, sensor networks, and their applications in medical and underwater technologies. His work bridges theoretical research with practical implementations, including projects like 5G-TELK-NF (autonomous driving/flying), Baltic Future Port (5G drone-based data optimization), and DAVE (digital twin for inland vessel navigation). Education: Diplom (equiv. to Master’s) in Electrical Engineering (1994, University of Saarland); PhD in Computer Science (2004, TU Braunschweig). Research Interests: Modern network structures, sensor systems for healthcare, underwater object detection, and 5G/AI integration. Scientific Contributions: His recent publications focus on UWB-based localization, 5G industrial deployments, and multisensor platforms for underwater applications. He has developed tools like ANSim (Ad-Hoc Network Simulator) and JGPSMap (GPS visualization). Leadership Roles: Active in GI/ITG Fachgruppe KuVS , program committee memberships, and long-term support for internationalization as part of the university’s governance bodies.

Maninder Singh Dhillonمشاهده پروفایل

پژوهشگر

• Agricultural remote sensing
• Crop yield modeling
• Land-use diversity
• +۶ مورد دیگر

Dr. Maninder Singh Dhillon is a Postdoctoral Researcher at the Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg (Germany). Since March 2024, he leads the 'Biodiversity' work package in the EO4CAM project, continuing his academic career at the same institution where he completed his PhD (2019-2023) with the thesis 'Potential of Remote Sensing in Modeling Long-Term Crop Yields'. His previous roles include scientific employee (2023-2024), PhD student (2019-2023), and research assistant (2018-2019) at the University of Wuerzburg's Remote Sensing Department. Education: M.Sc. in Earth Observation and Geoanalysis (EAGLE) from University of Wuerzburg (2016-2019), with thesis on crop growth models using synthetic remote sensing data Professional Experience: Internship at German Aerospace Center (DLR) in Neustrelitz (2018) and research assistant role at Punjab Remote Sensing Center (2015-2016) Dr. Dhillon's research focuses on agricultural remote sensing applications, particularly: Crop yield modeling through multi-sensor data fusion (MODIS, Landsat, Sentinel-2) Biodiversity analysis in agricultural ecosystems Land-use diversity impacts on crop biomass Climate variability and soil quality interactions Sustainable agriculture through precision farming technologies Carbon emissions assessment in groundwater irrigation systems His publication record (2018-present) shows expertise in: STARFM data fusion algorithms for NDVI time-series Machine learning integration with crop models Environmental monitoring of agroecosystems Water-energy-agriculture nexus analysis Phenological pattern detection in small-scale farming Multiscale biodiversity assessment frameworks

• Remote Sensing
• SAR (Synthetic Aperture Radar)
• InSAR
• +۳ مورد دیگر

Dr. Sofia Antonova is a postdoctoral researcher in the Permafrost Research group at the Alfred Wegener Institute (AWI), specifically within the Polar Terrestrial Environmental Systems department. Her work focuses on remote sensing techniques, including SAR and InSAR, applied to permafrost studies. She is actively involved in the BMBF-funded project ThinIce , investigating permafrost dynamics and environmental impacts. Her research integrates satellite data with field observations to monitor thaw subsidence, subsidence detection, and permafrost-related environmental changes in Arctic and subarctic regions. Key research interests include permafrost degradation, cryosphere dynamics, and the application of advanced remote sensing methods for environmental monitoring. Projects span the Lena River Delta in Siberia and the Mackenzie Delta in Canada, addressing questions of methane emissions, soil temperature, and waterbody contributions to climate feedbacks. Publications emphasize interdisciplinary approaches combining geospatial analysis, field campaigns, and remote sensing data to advance understanding of permafrost vulnerability and its implications for global climate systems.

Darius Burschkaمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Prof. Dr.-Ing. Darius Burschka is a Professor of Robotics, Artificial Intelligence, and Embedded Systems at Technische Universität München (TUM), specifically within the TUM School of Computation, Information and Technology. He leads the Professorship of Robotics, Artificial Intelligence and Embedded Systems and collaborates closely with the German Aerospace Center (DLR). His academic career includes postdoctoral research at Yale University (1998), associate research scientist and assistant research professor roles at Johns Hopkins University (1999-2005), and has been at TUM since 2005. His research focuses on sensor systems in robotics, human-machine interfaces, video-based navigation, and 3D reconstruction. Notable contributions include advancements in laser-based map generation, monocular navigation algorithms, and endoscopic image registration for medical applications. Prof. Burschka has received awards such as the Airtec 2010 Silver Award and best paper awards from prominent conferences like IROS and MICCAI. His work spans robotics, autonomous systems, computer vision, and medical imaging, with a strong emphasis on real-world applications in automotive, healthcare, and industrial automation. Education: Bachelor/Master in Electrical Engineering at TUM Doctorate (Dr.-Ing.) in Electrical Engineering, TUM (1998) His research interests include: Autonomous navigation and control Multi-sensor fusion and 3D reconstruction Human-robot interaction and haptic systems Robotic perception in dynamic environments Recent articles highlight advancements in graph neural networks for action segmentation, hybrid tracking systems, and latency modeling in industrial robotics, reflecting his ongoing contributions to cutting-edge robotics and AI.

• Machine Learning Applications in Agriculture
• Sensor Technology for Animal Health Monitoring
• Data Science in Bioeconomy
• +۳ مورد دیگر

Lukas Minogue is a researcher at the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Potsdam , affiliated with their Sensor Technology and Modeling department. He holds a Master's degree in Mathematics (University of Potsdam, 2019-2023) with focus on machine learning and statistical data analysis, and a prior Bachelor's degree in Mathematics (University of Potsdam, 2013-2019) concentrating on statistical modeling. His research specializes in applying machine learning models to agricultural sensor data , particularly for mastitis detection in dairy cows through automated milking systems (AMS). Key projects include: MEDICow (German-Irish collaboration): Developing AI-driven mastitis risk assessment using multisensor data AMRAfrica : Strengthening antibiotic resistance monitoring through data management systems in DR Congo INNOVAFRIKA (DFG-funded): Digital innovations in crop-livestock systems Recent publications demonstrate his work on: Time series analysis of AMS data with ML classifiers (logistic regression, random forest, neural networks) Comparative performance of SMOTE resampling techniques Feature engineering for dairy health metrics (e.g., fat-protein ratio) His technical expertise spans Python (scikit-learn, NumPy), data visualization (Matplotlib), and statistical modeling frameworks.

Michael Försterمشاهده پروفایل

پژوهشگر

• Remote Sensing
• Ecosystem Dynamics
• Environmental Monitoring
• +۳ مورد دیگر

Dr. Michael Förster is the Head of the Vegetation Remote Sensing Research Department and a Postdoctoral researcher at the Technical University of Berlin since 2009. His work focuses on developing remote sensing methods to analyze ecosystem dynamics, vegetation structure interactions, and environmental indicators using satellite and drone data. He holds expertise in integrating optical and SAR time series to study forest health, drought impacts, and soil moisture dynamics. Education & Positions: PhD in Geoecology (Potsdam, 2003) Visiting Scientist positions at JRC (2018), Utrecht University (2012), and EURAC (2010) Research roles at TU Berlin since 2003 Research Interests: Analyzing ecosystem degradation via remote sensing time series Linking spectral/plant trait data to biophysical variables Deriving actionable environmental indicators for policy frameworks (e.g., NATURA 2000) Integrating LiDAR/SAR with optical data for forest evaluation Drone-satellite synergy for ecohydrological studies Key Contributions: Pioneered drought impact analysis using XAI and Sentinel-2 data Developed methods for bark beetle infestation detection Advanced soil moisture modeling via cosmic-ray neutron sensing and remote sensing fusion Published extensively in journals like Ecological Indicators, Remote Sensing of Environment, and Forest Ecology and Management Labs & Teams: Leads the Vegetation Remote Sensing Research Department, coordinating interdisciplinary projects on environmental monitoring and geoinformatics.

Karl-Heinz Frommoltمشاهده پروفایل

پژوهشگر

• Bioacoustics
• Acoustic Ecology
• Vocal Communication in Vertebrates
• +۳ مورد دیگر

Karl-Heinz Frommolt is a Researcher at the Museum of Natural History, Leibniz Institute for Evolution and Biodiversity Research, where he serves as the Scientific Director of the Animal Sounds Archive and Head of the Bioacoustics Laboratory . His work focuses on bioacoustic monitoring of vertebrates, particularly breeding bird communities, using automated acoustic detection and pattern recognition technologies. Key Projects: Automated Multisensor Station for Monitoring Species Diversity (AMMOD) Development of Intelligent Acoustic Sensor Technology (DeViSe) Integrative Analysis of Pesticides and Land Use Effects on Biodiversity (INPEDIV) His research integrates bioacoustics with environmental monitoring, including work on bat call reference libraries and acoustic communication in vertebrates. Recent publications highlight applications of multisensor stations, microphone array signal processing, and long-term acoustic studies in wetlands and Mediterranean ecosystems. His team develops tools for automated detection, localization, and tracking of vocal animal species, contributing to biodiversity conservation and ecological data management. Dr. Frommolt's work spans taxonomy, ecological informatics, and conservation technology, with field applications in Germany, Greece, and international collaborations.

Eckhard Hennigمشاهده پروفایل

استاد

• Digital Circuit Design
• Low-Power CMOS Technology
• Integrated Sensors
• +۴ مورد دیگر

Eckhard Hennig is a Professor in the Department of Electronics & Drives at Reutlingen University, specializing in digital circuit design, low-power systems, and power electronics. His work bridges academic research and industrial application, with a focus on robust multisensor systems and technical origami for engineering. Education: Dipl.-Ing. in Electrical Engineering (1994, TU Braunschweig), Dr.-Ing. (2000, TU Kaiserslautern) Professional History: Researcher at University of Kaiserslautern (1995), Fraunhofer ITWM (1996–2000), Infineon Technologies AG (2000–2008), IMMS Institute (2008–2015) His research interests include low-power CMOS technology , delta-sigma modulation , and heterogeneous system modeling , with applications in energy-efficient systems and sensor networks. Recent publications emphasize adaptive modulation techniques , GaN-based power devices , and temporal decoupling for circuit stability. He leads projects like RoMulus (robust multisensor systems) and ZEBRA (technical origami) and contributes to interdisciplinary initiatives in hearing diagnostics and energy systems. His teaching includes courses on digital electronics, mixed-signal circuit design , and system modeling , with hands-on labs in FPGA/ASIC design and GaN device optimization. He works in the Digital Circuit Technology Lab, focusing on verification, heterogeneous simulation, and industry 4.0 sensor systems.

Martin Pahlمشاهده پروفایل

پژوهشگر

• Remote Sensing Data Evaluation
• Visualization of Remote Sensing Data
• Geospatial Data Quality Control
• +۳ مورد دیگر

Dr.-Ing. Martin Pahl is a faculty member at the Institute for Information Processing at Leibniz University Hannover , where he has served as Head of Administration since 2003 and leads the Remote Sensing Data workgroup since 2007. He also holds a part-time lecturing position at the University of Applied Science and Arts in Hannover , teaching Basics of Information Technology . Education: Diploma in Electrical Engineering, University of Hannover (1995) Dr.-Ing. in Information Processing, University of Hannover (2003) Research Interests: His research focuses on remote sensing data evaluation , visualization techniques , and quality control of geospatial datasets . He has contributed significantly to the development of systems for automatic quality assessment and semantic interpretation of multisensor remote sensing data . Publications Overview: His work spans from 2001 to 2012, with a strong emphasis on GIS data quality , photogrammetric methods , and knowledge-based systems for remote sensing applications. He has authored or co-authored over 15 peer-reviewed publications in top-tier venues like ISPRS and Photogrammetric Engineering & Remote Sensing . Projects & Collaborations: Project WIPKA-QS (Quality Control of Topographic Data) GeoAIDA system development for GIS data fusion and quality surveillance Technical Contributions: Beyond academia, he has developed open-source tools, including a Psion printer driver and UPS monitoring daemon for Raspberry Pi, demonstrating his expertise in system-level programming and hardware integration.

Claus-Eberhard Liedtkeمشاهده پروفایل

استاد

• Image Analysis
• Remote Sensing
• Industrial Automation
• +۳ مورد دیگر

Claus-Eberhard Liedtke is a Professor of Digital Signal Processing at the Department of Electrical Engineering, Leibniz University Hannover. He has held academic positions at the University of Minnesota (Assistant Professor, 1973–1976) and lectured as a Visiting Professor at Zhejiang University, China (1985). His work focuses on knowledge-based image analysis, 3D scene modeling, and remote sensing applications in industrial automation. Education: Diplom (1968) and Dr.-Ing. (1972) in Electrical Engineering from TU Berlin. Research Interests include automated knowledge acquisition for image systems, semantic networks in remote sensing interpretation, and multisensor fusion for 3D geometry reconstruction. His publications span hierarchical pose estimation, GIS-data integration, and Bayesian reasoning in vision systems. Publications Trends highlight expertise in remote sensing (7/15), 3D modeling (5/15), knowledge-based systems (6/15), and sensor fusion (3/15). Key subfields include topographic dataset verification, multitemporal analysis, and lifelike 3D human modeling. Professional Affiliations include EURASIP, EUROGRAPHICS, GI, ITG, and DAGM. He chairs the ITG working group on Pattern Recognition and serves on the Signal Processing journal’s editorial board, with over 90 publications and 30 grants from DFG, EU, and industry.

Zbynek Malenovskyمشاهده پروفایل

استاد

• Remote Sensing
• Optical Remote Sensing
• Imaging Spectroscopy
• +۹ مورد دیگر

Prof. Dr. Zbynek Malenovsky is a full Professor at the Institute of Geography, University of Bonn , where he leads the Remote Sensing Research Group (AG Malenovsky) . His work integrates advanced optical remote sensing, imaging spectroscopy, and 3D radiative transfer modeling to study plant eco-physiology and ecosystem responses to climate change. His research interests include: Quantitative remote sensing of vegetation biochemical and structural traits Solar-induced chlorophyll fluorescence (SIF) detection 3D radiative transfer modeling (e.g., DART model development) Climate change impacts on terrestrial ecosystems Machine learning applications in hyperspectral data analysis Land surface modeling and data assimilation His recent publications span high-impact journals in remote sensing and environmental science, with a strong focus on vegetation stress detection, canopy-level photosynthesis modeling, and deep learning-based biodiversity estimation using multisensor satellite data (Sentinel-1/2, Landsat, hyperspectral UAS). His work often involves collaborations with institutions like CESBIO (France) and the Global Change Research Institute (Czech Republic). Scientific awards and honors are not mentioned in the provided text. He actively supervises graduate students, including PhD candidates Thang Quang Nguyen and Lucas Casuccio , and offers thesis topics in areas such as bark beetle infestation mapping, pollinator habitat monitoring, and urban imperviousness analysis. He teaches advanced courses in physical geography, remote sensing methods, and land surface modeling, emphasizing hands-on use of software like ENVI, QGIS, SNAP, and Google Earth Engine. His research group is embedded in key initiatives such as PhenoRob (DFG Cluster of Excellence) and the Center for Remote Sensing of Land Surfaces (ZFL) , highlighting strong institutional support and interdisciplinary collaboration.

• Remote Sensing
• Geoinformatics
• Landscape Planning
• +۵ مورد دیگر

Professor Damian Bargiel serves as Head of Institute and holds the Professorship for Applied Geoinformatics in Landscape Planning within the Department of Landscape Planning & Nature Conservation at Geisenheim University. His work bridges advanced geospatial technologies with practical applications in agricultural monitoring and landscape planning. Professor Bargiel's research focuses on applied geoinformatics with particular expertise in remote sensing technologies , especially Synthetic Aperture Radar (SAR) satellite data. His work spans crop classification, agricultural land monitoring, habitat structure detection, and the integration of time series analysis with crop phenology information. He has developed innovative classification methods that significantly improve accuracy over conventional approaches by leveraging the temporal dimension of radar imagery. His publication record demonstrates consistent contributions to the field since 2011, with recent work expanding into satellite-based disease detection (2025) and small satellite Earth observation systems (2024). The research shows a clear progression from foundational work on TerraSAR-X applications to more integrated approaches combining multiple data sources and advanced machine learning techniques. Professor Bargiel is currently leading significant research initiatives including the AGRECO4CAST project (2025-2028), which aims to develop a transdisciplinary assessment framework for agroecological transitions in European agroforestry landscapes. This EU and German government-funded project reflects his expanding research scope into sustainable agricultural systems and policy-relevant applications of geospatial technologies. His work provides valuable methodological advances for agricultural monitoring systems and contributes to practical applications in precision agriculture, landscape planning, and environmental conservation. The integration of technical remote sensing expertise with real-world agricultural and landscape planning challenges represents the distinctive contribution of Professor Bargiel's research program.

Simone Maria Stünziمشاهده پروفایل

پژوهشگر ارشد

• Permafrost Dynamics
• Boreal Ecosystems
• Numerical Modeling
• +۳ مورد دیگر

Simone Maria Stünzi is a Research Fellow at Humboldt-Universität zu Berlin, specializing in Arctic permafrost-vegetation interactions. She leads the BorealFrost project investigating boreal forests' role in permafrost protection. Her research integrates numerical modeling, field observations, and remote sensing to study climate feedbacks in Siberia and Canada. Education: PhD in Geography (2018-2022), Humboldt-Universität & Alfred Wegener Institute MSc Environmental Science (2016-2018), University of Zurich BSc Geography (2013-2016), University of Zurich Research Focus: She examines how boreal forests modulate permafrost stability through thermal insulation mechanisms. Key areas include permafrost-vegetation-climate couplings, thermohydrological processes, and the impacts of forest disturbances. Her work emphasizes modeling tools like CryoGrid and integrates satellite data for landscape-scale analysis. Publications Trends: Recent work highlights boreal forests' delaying effect on thermokarst formation, permafrost thaw under climate change, and modeling advancements in Siberian and Arctic ecosystems. Cross-disciplinary collaborations enhance understanding of global carbon feedbacks. Awards: Helmholtz representative for Lindau Nobel Laureates Meeting 2024 Geo.X Young Academy Research Fellow Best Presentation Award at AWI Science Days 2020 Grants & Leadership: Principal Investigator: BorealFrost (DFG Walter-Benjamin Grant, 2024-2026) MOMENT Project (AWI, 2023-2024): Led field expeditions in Greenland PermaRisk Project (AWI, 2021-2023): Analyzed thermokarst dynamics Labs & Teams: Collaborates with the Alfred Wegener Institute, CryoGrid community model developers, and international field teams in Siberia, Canada, and Mongolia.

Birgit Kleinschmitمشاهده پروفایل

استاد

• Remote Sensing
• Climate Change
• Environmental Science
• +۴ مورد دیگر

Prof. Birgit Kleinschmit is a Professor and Head of the Department of Geoinformation in Environmental Planning at Technische Universität Berlin. Her research focuses on remote sensing applications for analyzing human-environment systems, particularly in the context of climate and land-use changes. She leads projects on spatio-temporal modeling of landscapes, drought impacts, and wildfire dynamics using AI and satellite data. Member of the Scientific Advisory Board for Forest Policy (Bundesministerium) Co-Speaker of the DFG Urban Water Interfaces Research Group Recipient of the 2024 '100 Most Influential Minds in Berlin Science' award Her work integrates multisensor data (e.g., Sentinel-2, SAR) with machine learning to assess forest health, wildfire susceptibility, and urban evapotranspiration. Recent studies include drought responses in Central Europe, wildfire driver analysis, and sustainable land-use strategies. Publications emphasize environmental monitoring innovations, with over 20 peer-reviewed articles since 2022. Active in policy advising through Geo.X Research Network and federal committees.

Tobias Ullmannمشاهده پروفایل

استاد

• Remote Sensing
• Earth Observation
• Climate Change Impact Analysis
• +۶ مورد دیگر

Prof. Dr. Tobias Ullmann has served as Full University Professor (W2) for Remote Sensing in Geography at the Department of Remote Sensing, University of Würzburg since October 2022. His research integrates Earth observation archives with physical geographic investigations, focusing on polar, semiarid, and arid regions using SAR data, multispectral optical satellite imagery, and digital terrain models. Key projects include climate change impact assessment, interdisciplinary cooperation with biology/ecology fields, and UAV-based environmental data collection. PhD from University of Würzburg (2011-2015) Habilitation at University of Würzburg (2021) Research interests span: Multi-sensor approaches for tropical climate-landscape interactions (Pol)(In)SAR classification/change detection techniques Periglacial dynamics in alpine/polar regions Machine learning and cloud processing applications Recent publications highlight: 2025 work on landscape-scale SAR phenology analysis 2024 NDVI time series for East African crop systems 2023 Arctic snowmelt and wildfire modeling Current affiliations: Chair of Remote Sensing, University of Würzburg Earth Observation Research Cluster Faculty of Philosophy, Institute of Geography and Geology