J.E. Goncalves is a researcher at the Delft University of Technology , affiliated with the Faculty of Architecture and the Built Environment and the Department of Spatial Planning and Strategy . Their work focuses on spatial justice , urban sustainability transitions , and digital engagement in climate action. Research Interests: Goncalves explores the intersection of urban planning, climate policy, and equity. Key areas include spatial justice frameworks , decolonizing data science , and interface design for citizen participation . Their recent work evaluates digital tools for inclusive urban governance and energy transitions. Recent Activities: They have presented at workshops and conferences on topics like urban climate action pedagogies , public value spheres , and BIG SHIFTS in spatial planning . Their projects emphasize participatory approaches to address socio-spatial inequalities in energy poverty and climate resilience.
S. van Cranenburgh is a researcher at the Delft University of Technology in the College of Technology, Policy and Management , specializing in the Transport and Logistics department. Their work bridges transportation engineering, data science, and urban analytics through innovative methodologies in machine learning and spatial-temporal modeling. Research Focus Transportation behavior modeling (e.g., residential location choice). Advanced collision risk prediction using naturalistic driving data. Integration of computer vision and discrete choice models. Psychological impacts of urban noise pollution on health. Proactive risk learning from large-scale driving datasets. Collective perception in historic urban landscapes. Recent Work Trends Their publications from 2025 emphasize deep learning applications in transportation safety (neural networks, contrastive learning), spatial-temporal data analysis , and social media-driven urban dynamics studies. Key themes include data-driven policy modeling and interdisciplinary approaches combining engineering, psychology, and social sciences. Collaborative Engagement Active collaboration with institutions like TU Delft's 4TU.ResearchData. Contributions to open-access datasets and software repositories. Participation in academic workshops and seminars (e.g., 2025 collective perception workshop). Media engagement on urban mobility topics (2016 coverage in Dutch outlets).
Rosa Maria Aguilar Bolivar is an Assistant Professor at the Department of Geo-Information Processing, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente. Her work bridges geospatial technology, machine learning, and participatory planning, with a focus on urban sustainability and food security challenges.
Andy Nelson is a Full Professor in Spatial Agriculture and Food Security at the Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, Netherlands. He leads the Department of Natural Resources and focuses on Earth observation and spatial analysis for global food security and sustainable resource management. PhD in Geography, University of Leeds MSc in Geographical Information Systems, University of Leicester BSc in Civil Engineering, University of Nottingham His research addresses critical questions in food production, risk mitigation, and food transport network resilience. He has developed open-access datasets like global travel time maps and urban-rural continuum models , with publications in Nature , Science , and policy reports for the World Bank and FAO. Recent work includes machine learning for pest monitoring, multi-sensor crop mapping, and climate stress modeling in Ethiopia and the Philippines. He co-supervises MSc and PhD students, emphasizing international collaboration. 2023 GEO Team Impact Award 2024/2025 ITC PhD Publication Awards As Senior Editor at Food Security and coordinator of projects like GEOGLAM and 4TU Centre for Resilience Engineering , Nelson bridges academia, policy, and technology to address global food system challenges.
Claudio Persello is an Adjunct Professor at the University of Twente , affiliated with the Faculty of Geo-Information Science and Earth Observation (ITC) and the Department of Earth Observation Science (EOS). Prior to this, he held a Marie Curie research fellowship at the Max Planck Institute for Intelligent Systems and the Remote Sensing Laboratory at the University of Trento. His research focuses on Deep Learning for Earth Observation , developing AI methodologies tailored to remote sensing data (RGB, multispectral, SAR, LiDAR) and geospatial applications. Key themes include urban deprivation mapping, agricultural monitoring, glacier dynamics, and 3D urban modeling. He emphasizes user engagement, open science, and the creation of interpretable AI solutions for societal challenges. Recent publications highlight trends in deep learning for remote sensing , with applications spanning climate change impact assessment, urban planning, and sustainable development. His work often integrates multi-source data (e.g., satellite imagery, socio-economic layers) to address real-world problems. 2018 : Top five teacher at ITC 2012 : Best PhD thesis in Pattern Recognition (GIRPR) 2011 : Marie Curie fellowship for MaleRS project He actively contributes to editorial boards (e.g., Remote Sensing journal) and collaborative networks like the Digital Society Institute. His datasets (e.g., CadastreVision, Svalbard glacier mapping) are publicly available via Zenodo and DANS.
Mahdi Khodadadzadeh is an Assistant Professor in the Department of Geo-information Processing, specializing in machine learning, data mining, and geospatial analysis. His work bridges traditional and deep learning methods with applications in mineral exploration, environmental modeling, and circular economy initiatives. Research outputs include novel cross-validation techniques for geospatial machine learning (Spatial+, dissimilarity-adaptive methods), hyperspectral mineral mapping for ore characterization, and hybrid models combining artificial neural networks with optimization algorithms. He actively contributes to open-access datasets and methodologies, particularly in drill-core analysis and multi-source data fusion. Collaborations with researchers like R. Zurita-Milla and R. Gloaguen are evident through co-authored publications and shared datasets. His work impacts domains such as mineral exploration, geospatial modeling, and sustainable resource management, with a focus on robust statistical evaluation frameworks and spectral-spatial analysis.
Ville Valtteri Lehtola is an Assistant Professor in the Department of Earth Observation Science, affiliated with the Digital Society Institute. His research bridges geosciences and artificial intelligence, focusing on sensor technologies and autonomous systems. Academic Rank: Assistant Professor Department: Earth Observation Science Key Affiliations: Digital Society Institute Lehtola's work spans several interconnected domains: Artificial Intelligence : Edge AI, deep learning, graph neural networks Geospatial Research : Point cloud analysis, 3D mapping, indoor navigation Autonomous Systems : Sensor fusion, real-time computing, robotic perception Urban Sustainability : Digital twin applications for city planning His recent publications highlight trends in AI-enhanced geospatial analysis and autonomous navigation technologies. Notably, he has contributed to indoor environment mapping using advanced machine learning techniques and explored digital twin implementations for urban sustainability. Lehtola actively participates in academic collaboration, organizing the ISPRS Workshop Indoor 3D in 2019. His research outputs demonstrate consistent engagement with geospatial AI, sensor technologies, and their applications in real-world environments.
Dr. Farzaneh Dadrass Javan is an Assistant Professor at the Department of Earth Observation Science, Faculty of Geo-Information and Earth Observation (ITC), University of Twente, Netherlands. Her academic journey began at the University of Tehran, Iran, where she earned her PhD and MSc in Photogrammetry and Remote Sensing. She contributes to interdisciplinary research bridging UAV technology, Artificial Intelligence, and data fusion for environmental and societal challenges, particularly in the Global South. Research Interests: Her work focuses on multi-scale geospatial data integration, leveraging drones to bridge ground and satellite observations. Key areas include Environmental monitoring through UAV-aided remote sensing Explainable AI for ecological and agricultural applications 3D modeling of urban and heritage sites Climate resilience and drought analysis in Iran Deep learning for infrastructure and vegetation health assessment Scientific Contributions: With over 59 research outputs, she specializes in UAV photogrammetry, multi-sensor fusion, and AI-driven environmental solutions. Her projects address water and health security in marginalized communities, precision agriculture, and smart cities. She has received recognition for journal-featured papers, APC waivers, and invitations to speak at global conferences. Awards and Leadership: Guest Editor for Remote Sensing Journal and Drones Journal panel member Associate Editor for GIScience Journal Scientific Committee roles in ISPRS, IEEE, and SEFI conferences External examiner/supervisor for universities in South Africa, USA, Belgium, and Netherlands Education: She holds a PhD and MSc from the University of Tehran, supervised by Prof. Farhad Samadzadegan.
Francesco Nex is an Associate Professor at the University of Twente in the Department of Earth Observation Science , where he holds the chair of real-time analytics for ubiquitous geo-sensors. He earned a Master's in Environmental Engineering (2006) and a PhD (2010) from TU Turin. His career spans roles at Italy's FBK institute (2011-2015) and the University of Twente (2015-present). His research integrates photogrammetry , deep learning , and robotics to enable automated UAV-based solutions for applications like disaster management , infrastructure monitoring , and precision farming . Key projects include EU-funded initiatives (Ingenious, Panoptis, RECONASS) and leadership roles in the ISPRS (Chairman of ICWG II/Ia). He has supervised 12 PhD students directly and co-supervised others at institutions like Politecnico Milano and Politecnico Torino. Recent publications highlight advancements in glacier monitoring using low-cost UAV systems, real-time 3D reconstruction , and autonomous drone navigation . Awards include the ISPRS President’s Honorary Citation (2021) and the E.H. Thomson award (2020). His work aligns with UN Sustainable Development Goals for Smart Industry , Climate Action , and Robotic Mobility .
BGCM Krol is a Lecturer in Natural Hazard Studies at the Department of Applied Earth Sciences, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente. He specializes in integrating technical expertise with practical risk planning and decision-making. Education : MSc in Earth and Environment from Wageningen University, with a specialization in Remote Sensing and Earth Surface Dynamics. Research interests focus on: Effective knowledge transfer from technical experts to communities at risk Earth surface dynamics and remote sensing applications Digital soil mapping and geospatial data quality Interdisciplinary approaches to natural hazard management
Suhyb Salama is Associate Professor of Remote Sensing of Water Quality and Water Resources Management at the Department of Water Resources, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, the Netherlands. His research integrates radiative-transfer physics, machine-learning analytics and multi-sensor satellite data to unravel hydrological and water-quality processes from micro-scale spectroscopy to meso-scale land–atmosphere interactions, directly supporting UN Sustainable Development Goals 6 and 14. Education: PhD in Civil Engineering, Katholieke Universiteit Leuven, Belgium (2003) MSc in Hydraulic Engineering (magna cum laude), KU Leuven (1999) BSc in Civil Engineering, Damascus University, Syria (1993) Research interests: Salama’s scholarly work spans remote sensing of biogeophysical variables, spatiotemporal modelling of suspended sediments and algal blooms, radiative-transfer theory across visible to microwave domains, development of smartphone-based water-quality apps, detection of floating plastic litter, and assessment of groundwater–vegetation feedbacks. He combines optical, thermal and microwave observations with physical and data-driven models to deliver actionable information on water scarcity, pollution extremes and climate-change impacts. Recent publication trends (2023-2025): His latest articles focus on machine-learning quantification of estuarine sediment variability, advanced detection of floating plastics, smartphone calibration for citizen-based water-quality monitoring, spectral-textural mining mapping, and analytical derivation of water-clarity time series from Sentinel-2 imagery. Collectively these works push toward operational, open-source solutions for high-frequency, basin-scale water-quality monitoring and UN SDG reporting. Editorial & professional service: Salama serves as topical editor for the journal Remote Sensing (2020-2024) and has chaired sessions on long-term earth-observation of suspended particulate matter. He actively collaborates across Europe, Africa and Asia on projects that merge remote sensing, hydrodynamic modelling and capacity development. Teaching & capacity building: He teaches MSc courses on optical and microwave remote sensing for water resources, supervises MSc and PhD research, and champions problem-based learning that equips students with enterprising skills to tackle complex water and climate challenges.
National Research Institute for Mathematics and Computer ScienceNetherlands
Dr. Gang Mei is an Associate Professor in Scientific Computing within the School of Engineering and Technology at China University of Geosciences (Beijing), where he has held academic positions since 2014. His career progression includes Postdoctoral Researcher (2014-2016), Lecturer (Oct-Dec 2016), and current Associate Professor (since Jan 2017). His research bridges computational science and engineering applications with significant editorial contributions to computer science literature. Education: Ph.D. in Computer Science, University of Freiburg, Germany (2014) Research Interests: Dr. Mei specializes in Numerical Simulation and Computational Modeling, GPU Computing, Machine Learning, and Data Mining, with strong applications in Network Science and Spatial Information Systems. His work integrates Distributed and Parallel Computing techniques for large-scale scientific simulations, particularly in geospatial modeling and network analysis. The research demonstrates consistent focus on computational efficiency through hardware acceleration and algorithmic optimization across diverse domains including satellite imagery processing, financial event detection, and medical image classification. Publication Trends: His editorial portfolio reveals strong interdisciplinary patterns connecting computer science fundamentals with domain-specific applications. Recent works emphasize GPU-accelerated methods for data-intensive problems (2020-2022), spatial-temporal modeling (2019-2020), and network science applications (2021). The publications consistently address computational scalability challenges while maintaining practical relevance across geospatial, financial, medical, and engineering contexts. Professional Recognition: As an IEEE Member, Dr. Mei serves on editorial boards for IEEE Access and PeerJ Computer Science, reflecting peer recognition in computational fields. His editorial contributions span 15+ publications demonstrating expertise in evaluating cutting-edge computer science research. Academic Service: Beyond editorial work, Dr. Mei's service includes advising on computational methodology across multiple disciplines. His role as Academic Editor demonstrates commitment to scholarly communication, particularly in bridging theoretical computer science with practical engineering applications. No grant funding details were specified in available materials.
Dr. Amiri Simkooei is a Professor in the Faculty of Aerospace Engineering at Delft University of Technology, specializing in Operations & Environment. His research spans the application of aerospace engineering principles to environmental monitoring and geospatial analysis, with strong connections to both theoretical and practical aspects of remote sensing and GNSS technologies. Dr. Simkooei's research interests focus on the intersection of aerospace engineering and environmental science, particularly in developing advanced methods for data analysis. His work in least squares variance component estimation has provided significant contributions to GNSS time series processing, while his environmental applications include aircraft noise monitoring systems and marine habitat characterization using remote sensing technologies. His recent publications (2025-2026) demonstrate a strong research trajectory with applications spanning from airport environmental management to marine ecosystem monitoring. The research shows a consistent theme of applying rigorous mathematical and statistical methods to solve practical environmental monitoring challenges using aerospace-derived technologies. Dr. Simkooei actively supervises graduate students, as evidenced by thesis-related datasets, and collaborates extensively within TU Delft and with external partners on environmental monitoring projects. His work bridges theoretical geodetic methods with practical environmental applications, making significant contributions to both aerospace engineering and environmental science communities. His research group focuses on GNSS and remote sensing applications, with particular expertise in data processing algorithms, environmental monitoring systems, and marine habitat mapping. The group maintains strong connections with Dutch environmental agencies and aerospace industry partners, facilitating the translation of research into practical applications.
