جست‌وجوی استاد و پژوهشگر

• Remote Sensing
• Civil Engineering
• Transportation Engineering
• +۵ مورد دیگر

Ayman Habib is the Thomas A. Page Professor of Civil Engineering at Purdue University's College of Engineering. He serves as Co-Director of the Civil Engineering Center for Applications of UAS for a Sustainable Environment (CE-CAUSE) and Associate Director of the Joint Transportation Research Program. His work focuses on integrating remote sensing technologies like LiDAR and UAV systems into infrastructure monitoring, environmental management, and transportation engineering. Key areas include sensor calibration, mobile mapping systems, and applications in forest inventory, pavement maintenance, and stockpile monitoring. Research interests span remote sensing, geomatics, and UAV-based solutions for civil engineering challenges. He actively develops methodologies for automated data processing, LiDAR intensity normalization, and machine learning-driven infrastructure assessment. His projects address sustainability through precise environmental and transportation systems analysis. Selected publications highlight advancements in LiDAR-based road cracking detection, forest reconstruction via neural networks, and UAV calibration for agricultural and environmental applications. His work emphasizes scalable solutions for infrastructure maintenance and environmental monitoring, leveraging interdisciplinary approaches in civil engineering and computer science.

• Geomatics
• Cultural Heritage Documentation
• 3D Modeling
• +۵ مورد دیگر

Antonia Teresa Spano is a Full Professor at the Department of Architecture and Design (DAD) of Politecnico di Torino, Italy. She serves as Vice Coordinator of the PhD Program in Architectural Heritage and is a member of the Future Urban Legacy Lab (FULL). Her research focuses on geomatics, 3D modeling, and digital documentation for cultural heritage preservation. University: Politecnico di Torino Department: Department of Architecture and Design (DAD) Spano's work integrates advanced geomatic techniques like LiDAR, UAV photogrammetry, and SLAM-based modeling to address challenges in architectural and archaeological heritage documentation. Her expertise spans GIS, digital photogrammetry, and machine learning applications in conservation processes. Her recent publications emphasize multi-sensor 3D surveys for Pier Luigi Nervi's structures, AI-driven decay detection, and semantic classification of LiDAR data. These works align with SDG goals for sustainable cities and quality education. Scientific Awards: Premio Giovani CNR (2005) Miglior Poster at GISTAM (2016) and GEORES (2019) Fellow of CIPA Heritage Documentation (2023-2033), CIRAAS ETS (2020-2030), and ISPRS (2017-2024) Spano supervises PhD students in architectural heritage programs and leads numerous research projects, including collaborations with institutions like the Pompeii Archaeological Park, CRAST, and international universities. She has contributed to editorial boards and program committees of journals and conferences such as Virtual Archaeology Review and ISPRS symposiums.

• Remote Sensing
• Environmental Monitoring
• Marine Ecology
• +۳ مورد دیگر

Karen Joyce is an Associate Professor at James Cook University (JCU) with expertise in remote sensing and environmental monitoring. She holds a PhD in Geographical Sciences from the University of Queensland (2005). Her work focuses on developing remote sensing tools for applications in marine, coastal, and savanna ecosystems. Notable contributions include advancing drone technology for coral reef mapping, mangrove phenology modeling, and disaster management integration. She co-founded She Maps, a social enterprise promoting women in STEM through drone education, and GeoNadir, emphasizing geospatial innovation. Education: PhD in Geographical Sciences (University of Queensland, 2005) Key Roles: Co-Founder of She Maps and GeoNadir Former Geomatic Engineering Officer in the Australian Army Her research interests center on optimizing remote sensing models to quantify Earth observation data, with applications in coral reef health, mangrove ecosystems, and invasive species management. Recent projects include She Flies Drone Camps to build STEM confidence in girls and hyperspectral drone technology for bathymetric mapping. Her publications emphasize drone-based data acquisition, spectral analysis for coral cover, and automated image processing using tools like Google Earth Engine. Despite no listed academic awards, her work has significant practical impact in conservation and disaster preparedness. Key grants include projects like 'Is satellite technology telling the truth? Perspectives from a coral reef' (2015–2017) and 'Developing hyperspectral drone technology' (2016–2017). She collaborates extensively with institutions like the Australian Army, New Zealand conservation agencies, and Kakadu National Park researchers.

• Hydroinformatics
• Urban Hydrology
• Flood Modeling
• +۳ مورد دیگر

Jonathan L. Goodall is a Professor of Civil and Environmental Engineering and Director of the Link Lab at the University of Virginia. His research focuses on hydroinformatics, urban hydrology, and flood modeling, leveraging data science and cyber-physical systems to enhance resilience in coastal urban environments. He leads efforts in reproducible environmental modeling through integration of platforms like HydroShare, emphasizing open science and computational workflows. His work includes advancing machine learning techniques for real-time flood forecasting, integrating IoT and sensor networks for smart city applications, and assessing climate change impacts on coastal communities. Notable contributions involve developing surrogate models for flood prediction in Norfolk, VA, and studying compound flooding effects using hydrodynamic modeling paired with crowdsourced data. Collaborations span universities, national labs, and agencies like CUAHSI, focusing on environmental data interoperability and infrastructure resilience. Goodall’s research often addresses socio-technical challenges in urban flood management, combining engineering solutions with community engagement strategies. His lab explores reinforcement learning for real-time stormwater control, IoT education initiatives, and geospatial tools for flood vulnerability analysis. Projects frequently involve interdisciplinary teams and emphasize reproducibility through containerized environments and metadata standards.

Hansjörg Kuttererمشاهده پروفایل

استاد

• Geodesy
• Geophysics
• Environmental Science
• +۳ مورد دیگر

Prof. Hansjörg Kutterer is a Professor and Dean at the KIT-Department of Civil Engineering, Geo and Environmental Sciences at Karlsruhe Institute of Technology (KIT). His primary affiliation is with KIT's Department of Civil Engineering, Geo and Environmental Sciences. He leads geodetic research initiatives focusing on Earth observation systems, atmospheric modeling, and geophysical data analysis. His research emphasizes advanced applications of GNSS, InSAR, and satellite gravimetry for monitoring climate-related phenomena such as water vapor dynamics, terrestrial water storage changes, and ground motion patterns. Key projects include developing machine learning-enhanced models for tropospheric delay corrections and integrated water vapor estimation in the Upper Rhine Graben region. Prof. Kutterer actively contributes to international geodetic frameworks like the Global Geodetic Observing System (GGOS), particularly through DA-CH regional collaborations. His work bridges geodetic methodologies with interdisciplinary challenges in climate science and environmental engineering. He oversees departmental operations as Dean, fostering innovation in geospatial education and infrastructure. His technical expertise spans geodetic deformation analysis, statistical robust estimation, and the integration of geophysical models with observational data.

• Cartography
• Geomatic Engineering
• Historical Map Analysis
• +۴ مورد دیگر

Dr. Yizi Chen is a Researcher affiliated with the Professorship for Cartography at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. Their work focuses on advancing cartographic techniques through AI-driven methods, historical map analysis, and geospatial technologies. Key contributions include automated map vectorization, semantic segmentation of historical maps, and integrating multimodal data for robotic systems. They have published extensively in top-tier journals and conferences, addressing challenges in deep learning applications for geomatic engineering. Education details are not explicitly provided in the text. Research interests include semantic segmentation, generative AI for cartography, and steganography in image translation. Notable publications span topics from eye-tracking segmentation to urban land use mapping, reflecting a strong interdisciplinary approach. Dr. Chen collaborates on projects involving historical map digitization and benchmarking datasets for computer vision tasks. No awards or grants are mentioned. Their work contributes to advancing geomatic engineering through innovative solutions in digital mapping and spatial data analysis.

Jim Chenمشاهده پروفایل

استاد

• Coastal Engineering
• Environmental Sciences
• Numerical Modeling
• +۵ مورد دیگر

Jim Chen is a Professor at the Department of Marine and Environmental Sciences and holds an affiliation with the College of Engineering's Civil and Environmental Engineering at Northeastern University. His research focuses on coastal engineering and science, emphasizing numerical modeling to address coastal resiliency and sustainability, particularly in the context of hurricanes and sea-level rise. Education details are not explicitly provided in the text, but his expertise includes advanced modeling techniques applied to coastal systems. His work integrates field observations with computational methods, such as deep learning and physics-informed neural networks, to analyze wave dynamics, sediment transport, and vegetation effects on coastal processes. Key research areas include hurricane impact analysis on wetlands and engineered infrastructure, living shoreline restoration effectiveness, and the morphological evolution of coastal systems. His studies often involve rapid deployment of sensors during storms (e.g., Hurricane Laura) to monitor wave, current, and sediment dynamics, contributing to disaster preparedness and mitigation strategies. Notable collaborations include projects with the Shinnecock Indian Nation, Gandys Beach (New Jersey), and Chesapeake Bay, focusing on sustainable coastal management. His work bridges engineering and environmental science to enhance coastal resilience in vulnerable regions.

Alper Yilmazمشاهده پروفایل

استاد

• Deep Learning
• Reinforcement Learning
• Computer Vision
• +۱۱ مورد دیگر

Alper Yilmaz is Professor with appointments in Civil Environmental and Geodetic Engineering and Computer Science and Engineering (courtesy) Departments at The Ohio State University. He serves as Director of PCVLab and is currently interim president for the ISPRS Technical Commission II. Dr. Yilmaz has been inducted to the U.S. National Academy of Inventors in 2020 and is a Fellow of the American Society for Photogrammetry and Remote Sensing (ASPRS) and senior member of IEEE. Dr. Yilmaz's research focuses on biomimetic navigation systems for unmanned systems, mining anomalies in multi-physics and multi-dimensional data for surveillance, and learning geospatial information for scene understanding. His expertise spans Deep Learning, Reinforcement Learning, Computer Vision, Photogrammetry, Data-centric Surveillance, Collaborative Swarm Navigation, and Indoor Positioning Systems. His recent work on biomimetic navigation systems has resulted in UbiHere Inc., an Ohio State University spin-off founded in 2018. Dr. Yilmaz has served as Editor-In-Chief for the Photogrammetric Engineering and Remote Sensing Journal (PE&RS) between 2016-2024, during which the journal's impact factor increased to its highest since 1934. He previously served as Associate Editor for Computer Vision and Image Understanding Journal (2014-2016) and Machine Vision and Applications Journal (2006-2011). Outstanding Service Award (2022, ASPRS) Innovator of Year (2020, OSU) Presidential citation (2019, ASPRS) Masao Horiba Award honorable mention (2016, Japan) Lumley Interdisciplinary Research Award (2015, OSU) Lumley Research Award (2012, OSU) Top 1% most cited researcher in Artificial Intelligence & Image Processing and Geological & Geomatics Engineering (2024) Dr. Yilmaz has advised 29 Ph.D. and 15 M.Sc. students to completion on topics ranging from photogrammetry, machine learning, and computer vision. His research has received over $13M in extramural funding from NASA, NSF, DOD, DOE, NIH, and industry partners including Ford Motor Company, Trimble Inc., and UbiHere Inc. PCVLab, located in Bolz Hall Suite 233, features a sensor calibration room, state-of-the-art workstations with GPUs for deep learning studies, and small robotic systems including ground and aerial units.

• Smart Cities
• Cyber-Physical Systems (CPS)
• Internet of Things (IoT)
• +۱۰ مورد دیگر

Dr. Hung Cao is an Assistant Professor of Computer Science at the University of New Brunswick, where he directs the Analytics Everywhere Lab. His work focuses on interdisciplinary research in Cyber-Physical Systems (CPS), IoT, Edge/Fog/Cloud Computing, and Explainable AI, addressing societal challenges through data-driven solutions. Prior roles include PostDoc Fellow and Data Scientist at the People in Motion Lab, UNB, and Lecturer/Researcher at Vietnam National University. He holds a Ph.D. in Geomatics Engineering (specializing in Data Science) from UNB (2020), an M.Sc. in Computer Science from University College Dublin (2015), and a B.Eng. from Vietnam National University (2011). Research interests span Smart Cities, Embedded AI, TinyML, Federated Learning, and Real-time Systems. He has led projects with Cisco, NB Power, and other industry partners to develop scalable analytics frameworks for IoT applications. Dr. Cao actively contributes to technical communities (IEEE Smart City, Edge Computing, etc.), serving as a reviewer for journals and conferences, and a Topic Editor for Electronics Journal . His innovations include the Analytics Everywhere framework for spatio-temporal data analysis, MACeIP platform for smart cities, and energy-efficient IoT systems for environmental monitoring. Current work emphasizes human-centered AI for healthcare diagnostics and industrial inspection systems.

Fraser Kingمشاهده پروفایل

استادیار

• Climate and Climate Change
• Radiation and Remote Sensing
• Synoptic Meteorology
• +۱۳ مورد دیگر

Fraser King is an incoming Assistant Professor in the Department of Atmospheric and Oceanic Sciences (AOS) at the University of Wisconsin–Madison, starting in Winter 2026. He holds a PhD in Machine Learning and Remote Sensing of Precipitation from the University of Waterloo (2022) and is currently a postdoctoral research associate at NASA Goddard Space Flight Center. His research integrates machine learning with atmospheric physics to advance precipitation and snowfall retrieval, cloud microphysics, and climate modeling. He has held research positions at the University of Michigan and NASA Jet Propulsion Laboratory. His research interests include: Climate and Climate Change Radiation and Remote Sensing Synoptic Meteorology Atmospheric and Cloud Physics Large Scale Dynamics Machine Learning and Model Interpretability Arctic Snowfall Prediction His recent publications reflect a strong trend in applying deep learning (e.g., U-Net, CNNs) and unsupervised methods (PCA, t-SNE, UMAP) to radar and satellite data for precipitation and snow microphysics. Key themes include radar gap inpainting, melting layer detection, and dimensionality reduction for physical interpretation. His work bridges geoscience and AI, aiming for interpretable models that enhance physical understanding. Scientific awards and professional service include: Finalist for the 2023 Governor General's Gold Medal, University of Waterloo Associate Editor, Journal of Atmospheric and Oceanic Technology (AMS) Member, AMS Committee on Artificial Intelligence Applications to Environmental Science Executive Council Member, AGU Precipitation Technical Committee Executive Member, Eastern Snow Conference Research Board Fraser King has mentored students through research projects and led educational initiatives such as a 12-week course on machine learning for land cover classification. He has secured research experience through internships at Aquanty Inc. and multiple NASA-affiliated institutions. He founded MapsByFraser, a company combining cartography and satellite data, and has collaborated with Google's Quantum AI team. His technical skills span Python, deep learning frameworks, and high-performance computing platforms. He leads several major research projects: Towards Interpretable Physical Models : Using sparse autoencoders and nonlinear dimensionality reduction to interpret geoscience models. Microphysical Dimensionality Reduction : Applying PCA, t-SNE, and UMAP to identify physical modes in precipitation data. BlindPaint : A U-Net for radar gap inpainting in spaceborne systems. DeepPrecip : A deep learning model for surface precipitation retrieval. iPhone LiDAR : Using consumer smartphones for snow depth measurement via drones. NRCan Machine Learning Land Cover Classifier : Training ML models on Sentinel-2 data. Climate Model Calibration : Using ML to correct biases in snow-related climate variables. CloudSat Snowfall Validation : Validating high-latitude snowfall estimates. Snow Modelling : A Rust-based physical/temperature-index snow model.

Gunho Sohnمشاهده پروفایل

دانشیار

• 3D Urban Space Modelling
• Photogrammetric Computer Vision
• Geometric Remote Sensing
• +۷ مورد دیگر

Gunho Sohn is an Associate Professor and Department Chair in the Earth and Space Science and Engineering (ESSE) Department at York University's Lassonde School of Engineering. His research focuses on advanced geomatics engineering applications, including 3D urban modeling, photogrammetric computer vision, and geospatial data integration. He specializes in developing innovative solutions for navigation systems, energy optimization, and autonomous robotics through interdisciplinary approaches. Dr. Sohn's work emphasizes practical implementations of remote sensing technologies, with notable contributions to LiDAR data processing, SLAM systems, and BIM-GIS integration. His research has addressed real-world challenges such as improving air quality models using industrial plume observations and creating inclusive pedestrian navigation tools using open geospatial datasets. Recent trends in his publications highlight advancements in deep learning for geospatial tasks, including semantic segmentation of aerial LiDAR data, noise reduction in sensor networks, and UAV positioning systems. His work also explores digital twin applications for simulating urban environments and optimizing building energy consumption through BIM data analysis. While no specific awards or grants are listed, his extensive publication record reflects significant contributions to the fields of geomatics and computer vision. His research group collaborates on large-scale datasets like YUTO MMS and Yuto Semantic, advancing mobile mapping and semantic understanding of urban infrastructure.

Dongmei Chenمشاهده پروفایل

استاد

• Geomatics
• Environmental Science
• GIS Applications
• +۴ مورد دیگر

Dr. Dongmei Chen is a Professor in the Department of Geography and Planning at Queen's University, affiliated with the Faculty of Arts and Science. She holds cross appointments in the School of Environmental Studies and the Beaty Water Research Centre. Her research focuses on understanding human-environment interactions through GIS and remote sensing, with specialties in land use modeling, climate change impacts, and spatial data analysis. Education: PhD in Geography (2001, San Diego State University/UCSB); M.Sc. in GIS/Remote Sensing (Chinese Academy of Sciences); B.Sc. in Geography (Peking University). Pre-Queen's career included work as a GIS product specialist at ESRI. Research interests include geomatics, environmental science, and applying machine learning to remote sensing challenges. Recent work addresses flood susceptibility modeling, urban sprawl analysis, and spatial-temporal disease risk. Her interdisciplinary approach bridges geospatial technologies with environmental and social science. Active in research centers like LaGISA, she explores topics like air pollution monitoring and spatial data privacy. Grants and advising are integral to her role, though specific details are not detailed here. Collaborations span global contexts, including Brazil, Thailand, and China.

Laurie Gaskins Baiseمشاهده پروفایل

استاد

• Geotechnical Earthquake Engineering
• Seismic Hazard Mapping
• Natural Hazards
• +۳ مورد دیگر

Laurie Gaskins Baise is a Professor and Chair of the Department of Civil and Environmental Engineering at Tufts School of Engineering, Tufts University. She leads the Geohazards Research Lab and directs the BSCE degree program. Her expertise lies in geotechnical earthquake engineering, seismic hazard mapping, and natural hazards mitigation. Education: Ph.D., University of California, Berkeley (2000) M.S., University of California, Berkeley (2000 and 1997) B.S.E., Princeton University (1995) Research Interests: Dr. Baise focuses on integrating predictive models with observational data to address geohazards like earthquakes and extreme wind events. Her work emphasizes regional seismic hazard mapping, liquefaction risk assessment, and rapid damage detection via remote sensing. She develops machine learning frameworks for post-disaster damage mapping and geospatial models for site response analysis. Labs/Teams: She directs the Geohazards Lab at Tufts, which collaborates on projects involving satellite imagery, geostatistical methods, and deep learning for hazard response. Recent work includes liquefaction modeling in Türkiye and Ukraine, wildfire spread forecasting, and ensemble-based seismic hazard assessments. Grants/Advising: Her research is supported by national and international grants. She mentors students in developing predictive models and remote sensing techniques for disaster resilience. Notable collaborations include work with the U.S. Geological Survey and global seismic networks.

Xin Zhangمشاهده پروفایل

استادیار

• Smart/Digital Agriculture
• Artificial Intelligence (AI) in Agriculture
• Computer Vision
• +۵ مورد دیگر

Dr. Xin Zhang is an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University. His research focuses on smart agriculture, AI applications, agricultural robotics, and unmanned aerial systems. He leads the Sensing & Automation in Agrisystems (SAAS) Lab, which develops technologies for crop monitoring, robotic harvesting, and machine vision systems. Dr. Zhang holds a Ph.D. from Washington State University and has held postdoctoral positions at UC Davis. He is a FAA-certified UAS pilot and actively collaborates on projects involving machine learning, computer vision, and precision agriculture. Research interests include digital agriculture frameworks, crop prediction models, and the integration of AI into farming systems. Key achievements include pioneering work in robotic cotton-picking simulations and UAV-based yield estimation. His lab emphasizes interdisciplinary approaches combining robotics, data science, and environmental engineering. Awards include the 2020 Giuseppe Pellizzi Prize for agricultural engineering research and multiple ASABE accolades. Current projects involve scalable field extraction frameworks using satellite imagery and deep learning models for plant phenotyping. He has published over 50 peer-reviewed articles, with a focus on advancing automation in crop handling and environmental sensing technologies. Education: Ph.D., Biological & Agricultural Engineering (WSU), M.S., Agriculture (Northwest A&F University), B.S., Agronomy (Gansu Agriculture University).

Barbara Wohlmuthمشاهده پروفایل

استاد

• Numerical Simulation
• Partial Differential Equations
• Discretization Techniques
• +۴ مورد دیگر

Prof. Barbara Wohlmuth is a full professor in Numerical Mathematics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. She leads the International Graduate School of Science and Engineering at TUM and has held professorships at Stuttgart, Darmstadt, and Berlin universities. Her research focuses on numerical simulation of partial differential equations, multiscale solvers, and coupled multi-field problems with applications in engineering. Education: Studied mathematics at TUM and Université Joseph Fourier in Grenoble, received her doctorate from TUM in 1995, and completed habilitation in Augsburg. Visiting professorships in USA, France, and Hong Kong. Research interests include discretization techniques, predictive modeling, and interdisciplinary collaboration with engineering disciplines. Notable achievements: 2012 Gottfried Wilhelm Leibniz Prize (Germany’s highest academic honor in sciences), 2005 Sacchi-Landriani Prize. Publications emphasize advanced numerical methods in fluid dynamics, geophysics, and biomedical engineering. Active in editorial roles for international journals and scientific committees across Europe and USA. Elected member of Bavarian and European Academies of Sciences. Key contributions include: Development of robust numerical algorithms for exascale simulations Pioneering work in coupled multi-physics modeling Innovative methods for computational contact mechanics Leadership in graduate education initiatives