ESGRS

How AI Enriches Remote Sensing Data Analysis in Urban Areas Insights on: Datasets, Strategies and Application
2025-07-30 11:00 - 12:00 (Asia/Dubai) | Virtual
Urban areas are under increasing pressure due to the economic, social, and environmental domains since the extraordinary rise in global urbanization, which is predicted to reach roughly 70% by 2050. The rapid pace of urbanization calls for intensive studies on mapping urban growth, assessing infrastructure, managing water resources, and monitoring natural land cover. The Urban environment, however, presents one of the most challenging research fields for remote sensing data analysis tasks due to the complexity of urban landscapes, the wide range of land cover materials, and the variety of land use classes. To address the challenges related metropolitan settings, recent Earth observation satellites provide a wide range of valuable spatial and quantitative information, large geographic coverage, and real-time monitoring. About one thousand Earth Observation (EO) satellites are in orbits, according to Union of Concerned Scientists (UCS), offering various multimodal remote sensing data including Multi/ Hyper-Spectral (MS/HS) images, Digital Surface Models (DSMs), and LiDAR point clouds. It is more challenging to accurately classify urban land cover and pinpoint particular features when multimodal data is interpreted using traditional methods. The integration of remote sensing techniques and Artificial Intelligence (AI) approaches provide affordable tools for gathering and analyzing large amounts of data in comparison to resource-intensive traditional scenarios like human based surveying and field observation. AI enhances data processing accuracy and efficiency, providing valuable information for urban development, infrastructure management, and environmental surveillance. AI tools interpret remote sensing data employing cutting-edge Neural Networks (NNs) to identify relevant land use and land cover characteristics and over-come human development challenges. The webinar explores the effectiveness of AI strategies especially neural networks (e.g., CNN, DNN, SNN, KAN) for remote sensing dataset analysis in urban areas and exploring their applications.

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Mohamed FawzyPh.D. Researcher/Teaching Assistant at Budapest University of Technology and EconomicsA Ph.D. researcher investigating AI, photogrammetry, remote sensing, and Earth observation. Recently, I am focusing on developing innovative solutions for Earth tracking activities leveraging recent AI strategies. I highly believe that the power of photogrammetry, remote sensing, image processing, and geospatial information systems are expected to be the next issue in of interest for the world. Their applications have received much interest in human development since their function is extremely related to the analysis of built-up areas, infrastructural networks, water resource monitoring, climate change effects, and disaster management.

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Remote Sensing & GIS for Smart Cities: Insights, Innovation, and Impact
2025-09-03 10:00 - 11:00 | Virtual
This webinar explores the critical role of geospatial technologies in building and managing smart cities. Participants will gain insights into how GIS, photogrammetry, remote sensing, and AI are leveraged to capture, assess, and analyze urban data in real-time. Through practical examples and use cases, the session will demonstrate how these technologies support data-driven urban planning, infrastructure monitoring, environmental assessment, and decision-making. Join us to discover how geospatial intelligence transforms raw data into actionable solutions for smarter, more sustainable cities.

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Eng. Hussein M. AbdulmuttalibGIS Principal Specialist at GIS Center Department of Dubai MunicipalityHussein M. Abdulmuttalib is an MSc Civil Engineer, graduated from the Technical University of Budapest, with over 20 years of experience in geospatial analytics, remote sensing, photogrammetry, and smart city planning. He currently serves as a GIS Specialist at Dubai Municipality, where he also contributes to the management of GeoHub—the Municipality’s GIS innovation incubator.

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Climate Change impact on the formation of the Cyclogenesis process over the Arid and semi-arid regions of South Asia
2025-09-10 10:00 - 11:00 | Virtual
Climate change is altering atmospheric and oceanic patterns, significantly impacting cyclogenesis—the formation and development of tropical and extratropical cyclones. In arid and semi-arid regions of South Asia, such as parts of Pakistan, northwest India, and Afghanistan, traditionally not known for frequent cyclonic activity, shifts in temperature, and sea surface warming in adjacent oceans (e.g., Arabian Sea and Bay of Bengal). Changes in wind shear are making conditions more conducive to cyclone formation.
Rising sea surface temperatures due to global warming enhance evaporation and provide more energy for storm systems. Increased moisture availability and weakened vertical wind shear can allow cyclones to intensify more rapidly, and in some cases, form in regions that were previously considered unlikely. These changes pose new threats to dryland communities that are poorly equipped to handle such extreme events, raising concerns about infrastructure, water security, and disaster preparedness.
In summary, climate change is expanding the geographic range and intensity of cyclogenesis, increasing the vulnerability of arid and semi-arid regions in South Asia to extreme weather events.

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Dr. Ajoy DasCartographer at Gujarat UniversityDr. Ajoy Das has a keen interest in maps since he was an undergraduate student. He has a Bachelors in Geography with specialization in Pedology from The University of Burdwan, a Masters in Geography with specialization in Agricultural Geography from CSJM University, PGD in Geoinformatics with specialization in Water Resources Management from West Bengal University of Technology, MTech in GIS with specialization in Natural resources conservation and management through participatory approach from NIIT University, and a Ph.D. in Geography with specialization in Wetland biodiversity conservation and management from Gujarat University, India. He is currently working as a Cartographer at the Department of Earth Sciences, Gujarat University, and sharing best practices for mapping and analysis with modern GIS, Remote Sensing, and GNSS technologies. He publishes and presents worldwide on many aspects of mapping and GIS. His working areas are natural resources management, biodiversity conservation, Hydro-Geomorphology, terrain analysis, and spatio-temporal modeling, addressing critical and contemporary issues on climate change impact, Disaster risk resilience on Earth and its environment.

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Uncertainty-aware Urban Digital Twin based on Mobile Laser Scanning
2025-09-16 10:00 - 11:00 | Virtual
Mobile Laser Scanning (MLS) systems have become an indispensable solution that has reached a high level of popularization in various fields, for instance, digital twinning, smart cities, engineering geodesy, 3D modeling, autonomous driving, etc. Compared with the remarkable achievements of the MLS systems in efficient data acquisition and various scenario applications, the uncertainty evaluation and reduction has not followed the same trend, significantly lagging behind the development pace of current MLS systems. On the other hand, uncertainty-aware digital twinning remains an underexplored topic. This talk will present our latest work on modeling uncertainty in MLS point clouds and its application to building quality-assured urban digital twin.

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Mr. Ziyang XuResearch Associate and PhD Candidate - Chair of Engineering Geodesy, TUMMr. Ziyang Xu is currently a doctoral student at TUM Chair of Engineering Geodesy. Before, he graduated from Wuhan University in China, majored in Geodesy and Geomatics, and also worked in industry for 3 years, including IBM, Xiong’an New Area, and Zhejiang Academy of Surveying and Mapping. His main research interests are MLS quality control and improvement, uncertainty evaluation, and its application in digital twinning.

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Dust Storm Mapping and Monitoring in the Middle East using Remote Sensing Data and Google Earth Engine Platform
2025-09-23 10:00 - 11:00 | Virtual
This webinar will explore advanced techniques and tools for monitoring dust storms and aerosol distributions using remote sensing technologies. This session will cover a range of key topics aimed at enhancing your understanding of environmental monitoring through satellite data and machine learning.
Key topics include:

Remote Sensing products for dust storm monitoring
Aerosol mapping using Aerosol Optical Depth (AOD) products
Aerosol thickness mapping with PM2.5 concentration data
Dust storm occurrence mapping and monitoring
Analyzing dust storm trends using machine learning methods

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Dr. Amirhossein AhrariResearcher at University of OuluI have received bachelor’s and master’s degrees in Remote Sensing from the University of Tehran (Iran) between 2011 and 2018. As a remote sensing researcher, I worked at the Remote Sensing Research Center (RSRC) at Sharif University of Technology and FAO (Iran) between 2018 and 2020. Since then, I moved to Finland and started a PhD in Environmental Engineering, and received a PhD degree in 2025. My PhD thesis is entitled “Environmental Monitoring using Remote Sensing Data and Machine Learning”. I am skilled in remote sensing, artificial intelligence, and environmental engineering, and share my skills through my YouTube channel “Google Earth Engine with Amirhossein Ahrari”. Recently, I have received the Google Developer Expert title from the Google Earth Engine team at Google, which officially approved my skills. Currently working as a Researcher at the University of Oulu and collaborating on peer-reviewed papers that are already highlighted on my Google Scholar account.

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Satellite Image Object Detection with YOLOv11: From Labeling to Model Deployment
2025-09-24 09:30 - 2025-09-25 14:45

Geospatial Innovation in Action: Mobile Mapping, AI, and Digital Twins for Adaptive Decision Support
2025-10-08 10:00 - 11:00 | Virtual
Recent advances in mobile mapping systems have been driven by rapid improvements in sensor technology and platform integration, enabling the efficient capture of high-resolution, multi-modal geospatial data at unprecedented spatial and temporal scales. Coupled with developments in artificial intelligence and data analytics tools, these systems now facilitate automated feature extraction, pattern recognition, and predictive modeling from vast and complex geospatial datasets. As a result, there has been an explosion of potential applications across sectors seeking to leverage location-based intelligence, from natural resource management to infrastructure monitoring.
These technological trajectories converge in the creation of multi-resolution digital twins—virtual replicas of the physical environment that integrate heterogeneous geospatial data streams into coherent, scalable models. Digital twins provide a dynamic, data-rich foundation for decision support, enabling stakeholders to simulate scenarios, evaluate trade-offs, and optimize interventions. In digital forestry, they can enhance forest inventory precision, wildfire risk modeling, and ecosystem monitoring. For transportation asset management, digital twins offer continuous assessment of roadway and bridge conditions, improving maintenance planning and safety outcomes. In urban development, they guide sustainable planning, optimize land use, and evaluate the impacts of growth in real time. Along shorelines, digital twins enable fine-grained monitoring of erosion, flooding risks, and ecosystem health, supporting climate resilience strategies.
Together, the integration of mobile mapping innovations, AI-driven analytics, and digital twin frameworks heralds a new era of data-driven decision-making, where spatial intelligence informs more adaptive, efficient, and sustainable management of natural and built environments.

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Prof. Ayman F. HabibProfessor of Civil Engineering at Purdue UniversityAyman Habib is the Thomas A. Page Professor at the Lyles School of Civil Engineering at Purdue University. He is the Co-Director of the Civil Engineering Center for Applications of UAS for a Sustainable Environment (CE-CAUSE). He is also the Associate Director of Purdue University’s Joint Transportation Research Program (JTRP) and Institute of Digital Forestry (iDiF). He received the M.Sc. and Ph.D. degrees in photogrammetry from The Ohio State University, Columbus, OH, USA, in 1993 and 1994, respectively. His research interests include the fields of terrestrial and aerial mobile mapping systems using photogrammetric and LiDAR remote sensing modalities, UAV-based 3D mapping, and integration of multi-modal, multi-platform, and multi-temporal remote sensing data for applications in transportation, infrastructure monitoring, environmental protection, precision agriculture, digital forestry, resource management, and archeology.

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Hydro-geodesy of the Arabian Peninsula
2025-10-28 10:00 - 11:00 | Virtual
Hydro-geodesy is an interdisciplinary scientific discipline that employs geodetic methodologies, primarily satellite-based techniques, to investigate the dynamics of water movement and the resultant land surface oscillations. The Arabian Peninsula is classified as a water-scarce region, characterized by limited precipitation, elevated evaporation rates, and excessive groundwater extraction. Additionally, it is surrounded by oceanic bodies that exhibit both seasonal fluctuations and long-term secular trends. These hydrological motions induce variations in stress on the Earth’s crust, potentially leading to both short-term seasonal movements and long-term subsidence. Our recent research findings indicate that winter atmospheric loading also contributes significantly to the seasonal crustal movements. Such crustal deformations can pose significant challenges to the sustainability of human settlements and infrastructure. In the context of climate change, increasing water demand, and sea level rise scenarios, a comprehensive understanding of hydro-geodesy within the Arabian Peninsula becomes crucial. Such knowledge is instrumental in addressing regional hydrological and infra-structural issues and in fostering sustainable development trajectories. In my talk, I will present our research results based on Gravity Recovery and Climate Experiment (GRACE) and its Follow-On, Global Navigation Satellite System (GNSS), Non-Tidal Atmospheric Loading (NTAL), and satellite altimetry datasets, and discuss the groundwater decline rates, sea level variability, and the associated crustal movement in the Arabian Peninsula Region.

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Dr. Muhammad UsmanAssistant Professor at Zayed UniversityDr. Muhammad Usman completed his MS and PhD at Hokkaido University (Japan) with the Japanese Government’s MEXT Scholarship. He also has two years of postdoc research experience at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. He won the Outstanding Student Presentation Award (OSPA) at the Japan Geoscience Union-American Geophysical Union (JpGU-AUG) joint meeting of 2017 in Makuhari Messe, Chiba, Japan. He won first prize in the “Student Paper Contest” at the Annual Technical Conference (ATC) 2011, which was organized by the Pakistan Association of Petroleum Geoscientists (PAPG). He studies the water cycle, ocean dynamics, geodetic hazards, geomorphology, climate change, and different physical parameters of the environment using remote sensing data and field surveys. He mainly uses Synthetic Aperture Radar (SAR), Global Navigation Satellite System (GNSS), Gravity Recovery and Climate Experiment (GRACE) Satellite, Satellite Altimetry, and various climatic data; Python, MATLAB, and Generic Mapping Tool (GMT) products.

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Crop Yield Prediction as an Early Warning Tool for Drought and Food Security Disasters
2026-01-13 10:00 - 11:00 | Virtual
Timely and reliable crop yield prediction is a critical component of early warning systems for drought and food security disasters, particularly in climate-vulnerable regions such as Afghanistan. The country is highly exposed to extreme weather events, and winter wheat plays a central role in national food security. Accurately capturing the influence of irrigation on crop productivity is therefore essential for anticipatory food security planning.
In this study, we evaluate Earth Observation (EO) based yield prediction models for winter wheat in Afghanistan by distinguishing between irrigation-sensitive and irrigation-insensitive predictors. EO datasets were grouped accordingly, and an irrigated area mask was applied to isolate signals from irrigated croplands. To enhance model robustness and reduce noise from inter annual variability, a first-difference approach was applied to both yield and predictor time series. The irrigation-sensitive model incorporates vegetation indices and biophysical parameters (NDVI, LAI, and FAPAR) along with surface and root-zone soil moisture from GLEAM (Global Land Evaporation Amsterdam Model), while the irrigation-insensitive model relies on precipitation, reference evapotranspiration, aridity index, and soil moisture from FLDAS ( FEWS NET Land Data Assimilation System).
Winter wheat yields were predicted from January through May, revealing that forecasts generated in February and March, approximately four months before harvest, were the most accurate. The combined vegetation-and-precipitation model achieved the lowest prediction error (RMSE ≈ 0.30 mt/ha), outperforming models that relied solely on irrigation-sensitive or irrigation-insensitive predictors. Results demonstrate the potential of EO-driven yield forecasting as an effective early warning tool for drought and food security monitoring. By providing reliable seasonal yield estimates well ahead of harvest, such models can support proactive decision-making and targeted interventions in regions where irrigation plays a critical role in buffering climatic shocks.

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Dr. Barnali DasAssistant Professor at Kansas State UniversityBarnali Das is Assistant Professor of Geography and Director of Natural Resources and Environmental Sciences Secondary Major at Kansas State University. She received her bachelor’s degree in Geography from University of Calcutta in 2009. She earned her master’s degree in 2011 and doctorate in 2022, both in geography, from University of Pune. She has also Diploma in Remote Sensing and GIS from Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO). Her PhD work won the Best Scientific Story Award in 2022, a national award across all scientific and technical fields organized by the Augmenting Writing Skills for Articulating Research (AWSAR) program of the Department of Science and Technology (DST). Dr. Das worked as Postdoctoral research scholar at Smithsonian Tropical Research Institute (STRI) in Panama and at University of California Santa Barbara (UCSB) before joining K-State in 2025. Her work focused on how climate change and shoreline dynamics affect mangroves in Central America as a postdoctoral researcher at the STRI. At UCSB her work utilizes earth observation products, remote sensing, satellite-derived biophysical parameters, and machine learning methods to understand how climate extremes affect crops in food insecure countries. Earlier in her career, she worked with the India Meteorological Department on the Forecasting Agricultural Output using Space, Agro-meteorology, and Land-based Observations (FASAL) project, where she applied geospatial and agro-meteorological approaches to operational crop yield forecasting.

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The Contribution of Satellite EO to Better Understanding Hazards and Disasters
2026-01-20 11:00 - 12:00 | Virtual
Satellite Earth Observation (EO) can be used to detect, identify and measure parameters relevant to disaster risk management. This can help in different phases including disaster response and recovery and in other phases such as risk prevention and mitigation. Modern EO missions providing high resolution and wide area mapping with systematic and global revisit, new information technologies have been applied to increase access to EO data and information. Regarding the immediate disaster response phase the International Charter Space & Major Disasters is providing rapid access at no cost to imagery from 240+ satellites and is reserved to a pre-defined list of users, the Authorized Users. Regarding risk assessment and prevention different initiatives have taken place to provide geoscience users and risk management experts with advanced systems and tools able to retrieve geoinformation concerning hazard and exposure including historical and up-to-date data from dedicated EO missions. The proposed lecture will explain and illustrate how these data and tools allow to better understand hazards and disasters.

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Eng. Philippe BallyEarth Observation Science & Application Engineer at European Space AgencyPhilippe Bally is a remote sensing expert working with the European Space Agency (ESA) in the Climate Action, Sustainability and Science Department of the Earth Observation Directorate. Graduated from an aerospace engineering Grande Ecole, Philippe worked with the French Space Agency (CNES) and joined Spot Image (France) in 1996 working on Earth Observation projects in Latin America and Asia in the fields of cartography and disaster risk management. He joined the ESRIN centre of ESA in 2000 and participates to the satellite Earth Observation (EO) programmes with focus on disaster risk management, humanitarian aid and international development. Philippe implemented large scale projects on advanced remote sensing such as the Geohazard Exploitation Platform for geoscience and a rapid mapping platform for disaster response using satellite imagery. He collaborates with the United Nations and Multilateral Development Banks. In 2022, he was appointed Board member of the International Charter ‘Space and Major Disasters’, an international collaboration for EO based disaster response.

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Advanced Oil Spill Trajectory Modelling for Environmental Protection
2026-02-04 10:00 - 11:00 | Virtual
Current oil spill emergency response protocols employ standardized, universal strategies that fail to account for critical environmental variations, particularly water temperature effects on oil weathering behaviour. This research addresses this operational gap through comparative analysis of two contrasting marine environments using integrated satellite remote sensing and GNOME (General NOAA Operational Modelling Environment) trajectory modelling.
The study examines the 1989 Exxon Valdez spill in Alaska’s cold waters (8°C, 257,000 barrels) and the 2017 Al-Khafji spill in the Arabian Gulf’s warm waters (40°C, 35,000 barrels). Satellite data from Landsat 5 TM and Sentinel-2 achieved 84-86% detection accuracy, validated through NOAA aerial surveys and multi-stage verification protocols.
Key findings reveal counterintuitive results: despite 32°C higher temperatures, the Arabian Gulf exhibited 24% lower evaporation (26.3%) compared to Alaska (34.8%) due to rapid emulsification processes that trapped volatile components. Alaska showed 7% shoreline beaching versus 0.5% in the Gulf, with dramatically different surface persistence patterns (57.9% vs 72.8%).

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Mr. Osman HamedFaculty of Engineering and Sciences at University of WolverhamptonOsman Hamed is a final-year PhD candidate in Environmental and Analytical Sciences at the University of Wolverhampton, UK, supervised by Professor Craig Williams, Dr. Catherine Duke, and Professor Abdallah Shanbleh. His research focuses on integrating satellite remote sensing (Landsat, Sentinel-2) with NOAA’s GNOME trajectory model for advanced oil spill monitoring and prediction. Osman’s doctoral work provides a comparative analysis of oil spill behaviour across contrasting marine environments—the 1989 Exxon Valdez spill in Alaska’s cold waters versus the 2017 Al-Khafji spill in the Arabian Gulf’s warm waters. His research demonstrates how environmental parameters, particularly water temperature, critically influence oil weathering processes and emergency response effectiveness.

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The Contribution of Satellite Data and Artificial Intelligence for Earthquake Response with damage mapping at building level
2026-02-17 10:00 - 11:00 | Virtual
The presentation will touch down on how earth observation data is currently used to detect building damages following an earthquake event. It will focus on the use of damage detection deep learning algorithms with very high resolution optical data, through the lens community challenge recently held by ESA phi-lab with the support of the International Charter: Space And Major Disasters.

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Eng. Mounia El BazEO Digital Innovation Engineer at ESAMounia El Baz received the Engineering degree in Applied Mathematics from École Centrale Paris (now CentraleSupélec) and the Master of Science in Mathematics, Computer Vision and Machine Learning from ENS Paris-Saclay, both part of Université Paris-Saclay. She was formerly a Senior Data Scientist at Descartes Underwriting, specializing in wildfire modeling, and is currently an EO Digital Innovation Innovation Engineer at the Φ-lab, ESA/ESRIN.

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Operational Satellite Radar Interferometry (InSAR) Applications for Earthquake and Ground Deformation Monitoring
2026-03-12 10:00 - 11:00 | Virtual
Earthquake-induced ground deformation and slow-moving geohazards such as subsidence and landslides pose significant risks across tectonically active and rapidly urbanizing regions. Through real case studies, the session will demonstrate how multi-temporal SAR datasets can be transformed into actionable deformation maps supporting disaster response, risk assessment, and resilient urban planning. The webinar highlights practical workflows, time-series deformation analysis for disaster management authorities. Participants will gain insight into InSAR processing strategies, time-series analysis approaches, and GIS-based integration workflows for disaster risk mapping and decision-support applications at local and provincial levels.

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Dr. Fatma Canaslan ÇomutDivision Manager – Earthquake and Disaster Risk Reduction at Governorship of Denizli Provincial Directorate of Disaster and Emergency (AFAD)Dr. Fatma Canaslan Çomut is a Cartography/Geomatics Engineer specializing in disaster risk reduction studies based on geodesy, remote sensing, radar interferometry, and Geographic Information Systems (GIS). She completed her graduate studies at the Department of Geodesy, Institute of Natural Sciences, Selçuk University in 2016. She has been working at the Denizli Provincial Disaster and Emergency Management Directorate since 2012. Since 2014, she has served as the Head of the Earthquake and Risk Reduction Branch, where she coordinates provincial disaster response and risk reduction planning processes. Her work integrates multi-temporal SAR interferometry and GIS-based spatial analysis for monitoring earthquakes, land subsidence, landslides, groundwater depletion, and infrastructure-related ground instability. Dr. Çomut actively participates in international collaborations and scientific events, contributing to the advancement of satellite-based disaster response methodologies.

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The Contribution of Satellite SAR Observations for Geohazards and Disaster Assessment Mapping
2026-03-31 12:00 - 13:00 | Virtual
Overview on how SAR data contributes to Geohazards mapping and Damage Assessment Mapping, indicating some initiatives, software and platforms that help its exploitation by final users and decision makers

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Dr. José Manuel Delgado BlascoGeosaptial System Engineer at European Space AgencyJosé Manuel Delgado Blasco, holds a double Ph.D. diploma in Geoscience and Remote Sensing from Delft University of Technology (The Netherlands) and Dr of Science in Geography from KU Leuven (Belgium). He has extensive experience working in the Earth Observation domain, specialized in algorithm development/parallelisation, processing platforms, AI, advanced InSAR applications, data fusion and algorithm optimization. Currently works as Geospatial System Engineer in the ESA Φ-lab Invest Office within the EO Commercialisation programme helping companies developing sustainable EO solutions.

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ESA’s P-band SAR mission status one year after launch
2026-04-09 10:00 - 11:00 | Virtual
The webinar will provide an update on ESA’s P‑band SAR mission one year after launch, highlighting its key characteristics and in‑orbit performance. It will showcase the first scientific results, including early insights into biomass mapping and subsurface imaging. The session will also explore emerging science applications, with a focus on geology and desert environments.

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Dr. Klaus ScipalBiomass Mission Manager at European Space AgencyDr. Klaus has twenty-five years of experience in earth science, satellite remote sensing, and technical innovation. He earned a Master’s in geodesy and a Doctorate in technical sciences from Vienna University of Technology. His professional journey includes positions as Assistant Professor at the Vienna University of Technology, Research Scientist at the European Centre for Medium-Range Weather Forecasts in the UK, and Mission Scientist at the European Space Agency’s Space Research and Technology Centre in the Netherlands. Since 2020, Dr. Klaus has served as Mission Manager for the SMOS and BIOMASS satellite missions at ESA’s Center for Earth Observation in Italy.

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Open-Source GeoAI for Earth Observation: Practical Tools for AI-Driven Analysis and Visualization
2026-04-23 17:00 - 18:00 | Virtual
The rapid expansion of Earth observation data from satellites, aerial platforms, and cloud-based archives has created unprecedented opportunities for large-scale environmental monitoring and analysis. At the same time, the volume, complexity, and velocity of these data demand new approaches that are both scalable and accessible. This presentation focuses on open-source GeoAI tools designed specifically for Earth observation applications, highlighting how artificial intelligence can be integrated with geospatial data science to enable efficient, reproducible, and transparent workflows. The talk introduces the GeoAI Python package and its ecosystem, which support a wide range of Earth observation tasks, including land cover classification, object detection, semantic segmentation, and change detection. Through real-world examples, participants will see how open-source tools can streamline data access, model development, and interactive visualization. The integration with QGIS and web-based mapping frameworks further enables seamless analysis across desktop, cloud, and browser environments.

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Dr. Qiusheng WuAssociate Professor at University of TennesseeDr. Qiusheng Wu is an Associate Professor in the Department of Geography & Sustainability at the University of Tennessee, Knoxville, and an Amazon Scholar. His research focuses on open-source geospatial data science, cloud computing, and GeoAI for Earth observation. He is the creator and maintainer of widely used open-source Python packages, including Geemap, Leafmap, SamGeo, and GeoAI, which enable scalable analysis and visualization of satellite and geospatial data. His work emphasizes building accessible, reproducible tools that bridge artificial intelligence and Earth observation.

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GeoAI for Environmental Management
2026-05-05 10:00 - 11:00 | Virtual
Applications of various AI-based models for environmental management, including deforestation monitoring, urban tree cover change and canopy height prediction, forest health assessment, nature resources management, and a brief about advanced Foundation models and LLMs.

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Dr. Pulakesh DasTechnical Lead in GIS Division at Madhya Pradesh State Electronics Development CorporationDr. Pulakesh Das received his PhD degree in 2019 from the Indian Institute of Technology Kharagpur, India. He did postdoctoral research in the School of Forest Resources at the University of Maine, USA. Pulakesh holds a master’s degree in Remote Sensing and GIS from Vidyasagar University, India. He has more than 13 years of experience in applying geoinformatics in environmental studies. He is currently working as a Technical Lead, Agriculture in the GIS division at Madhya Pradesh State Electronics Development Corporation (MPSEDC), India. Previously, he worked at the World Resources Institute India from December 2019 to January 2023. He taught for two years (from September 2017 to December 2019) in the Department of Remote Sensing & GIS at Vidyasagar University, West Bengal, India. He has authored 60 peer-reviewed research articles and book chapters. His work focuses on land use and land cover modelling, deforestation monitoring, forest health monitoring, ecosystem resilience, landscape restoration, hydrological modelling, agriculture practices monitoring, crop damage assessment, natural resources management, and climate change impact studies.

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Empowering the NexGen of Geospatial Intelligence Through Public-Private Partnerships
2026-05-14 09:00 - 10:30 | Virtual
Join us for an introductory webinar highlighting the latest updates and capabilities of the GIQ platform and related UAE space ecosystem initiatives supporting academic, startup, and R&D entities. The session will showcase how researchers and innovation partners can leverage Earth Observation data, AI-driven analytics, and collaborative geospatial tools to support research, innovation, and operational applications across the UAE.
The webinar will also provide a brief overview of the UAE Space Agency programs focused on innovation, capacity building, and applied research.
This awareness-focused session is organized in response to growing interest from UAE universities, startups, and research teams seeking to better understand and utilize GIQ services and capabilities within their research and development activities.

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Eng. Naser Al RashdiManager of the National Space Academy at UAE Space AgencyEng. Naser Al Rashdi is an Emirati executive with over 21 years of public sector experience in space, AI, cybersecurity, and ICT governance and policymaking. He leads the National Space Academy, serves on boards of trustees for government authorities, sits on the advisory board of MBZUAI, and is an adjunct instructor at MBRSG and PwC Academy. He has shaped more than 50 national policies and regulations, managed major strategic projects, and led multiple talent development programs, as well as international collaborations and regulatory initiatives for the UAE and the wider region.

Eng. Sultan Mohammed Al ZeidiGIQ Sponsorship Manager / UAE Space Data Center Oversight & Governance Manager at UAE Space AgencyEng. Sultan Al Zeidi is part of the Space Projects Management Section at the UAE Space Agency, where he serves as Oversight & Governance Manager for two space projects: Arab Satellite 813 and GIQ.AE. Since January 2023, he has served as the UAE representative to the International Disasters Charter Executive Secretariat, bringing over a decade of experience in government-funded aerospace programs. He contributes to strategic national space initiatives with a focus on Earth Observation and space data analytics. His work spans space projects development & oversight, and cross-sector collaboration to support the UAE’s growing space ecosystem. He has contributed to national initiatives, including Arab Satellite 813, the UAE-led first Arab cooperative earth observation satellite, GIQ.AE, an AI-powered satellite imagery platform recognized with the UAE Government Future Fit Seal, and the Space Analytics and Solutions Program, which drives innovation through funding, mentorship, and public-private partnerships. With a strong foundation in systems engineering and extensive experience in project sponsorship, R&D governance, and public–private partnerships, he is dedicated to translating advanced technologies into sustainable national impact.

Dr. Ani ShtyllaSenior Product Owner at Space42Product leader with 14+ years of experience delivering geospatial, AI, and Earth Observation products across private sector, humanitarian, and international development contexts. Currently Senior Product Owner at Space42, leading AI-driven geospatial product strategy, roadmap development, and alignment between engineering, data science, and business teams. Skilled in translating complex technical and business needs into user-focused products, with expertise in GeoAI, satellite data, product strategy, ML Ops, and data-driven platform development.

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Artificial Intelligence and Machine Learning for Agriculture Analytics
2026-06-03 10:00 - 11:00 | Virtual
AI/ML plays a crucial role in agriculture analytics, especially in geospatial crop monitoring and mapping. In your research-based talk, machine learning models will be covered for classifying crops using multi-sensor temporal datasets such as optical and SAR data. Talk will cover various types of issues while mapping specific crops as well as monitoring at sowing/harvesting and crop-burning fields. Issues can be within crop heterogeneity, spectral overlap, mixed pixel problem, minimum training data use, etc. AI techniques improve the detection of crop phenology and growth stages using time-series satellite imagery. Transfer learning can be applied to improve crop mapping accuracy with limited ground truth data. AI supports the detection and monitoring of stubble burning using satellite-based indicators. Integration of geospatial datasets with ML enables efficient acreage estimation and crop type discrimination. Advanced analytics help in understanding spatial variability for better resource and water management. Overall, AI/ML strengthens precision agriculture, food security assessment, and policy-level decision support.

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Dr. Anil KumarGroup Head, Geo-spatial Technology & Program Outreach at The Indian Space Research Organisation (ISRO)Anil Kumar is a scientist/engineer “G” and the Group Head Geo-spatial Technology and Program Outreach of Indian Institute of Remote Sensing (IIRS), ISRO, Dehradun, India. He received his BTech degree in civil engineering from IET affiliated to the University of Lucknow, India, and his M.E. degree as well as his PhD in soft computing from the Indian Institute of Technology, Roorkee, India. So far, he has guided 13 PhD theses, and six more are in progress. He has also guided several dissertations of MTech, MSc, BTech, and postgraduate diploma courses. He always loves to work with PhD scholars, Master’s, and Graduate students for their research work, and motivates them to adopt a research-oriented professional career. He received the Pisharoth Rama Pisharoty award for contributing state of the art fuzzy based algorithms for earth observation data. His current research interests are in the area of soft computing-based machine learning, deep learning for single date and temporal multi-sensor remote sensing data for specific class identification and mapping through in-house development of the SMIC tool. He also works in the area of digital photogrammetry, GPS/GNSS, and LiDAR. He has written two books on ‘Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification’ & Multi-Sensor and Multi-Temporal Remote Sensing-Specific Single Class Mapping with CRC Press. Won a challenge like Kritagya DA & FW Hackathon conducted by the Ministry of Agriculture, New Delhi, India, and Finalist of ‘ISRO immersion Challenge AI for Space and Geospatial conducted by NRSC-ISRO, Hyderabad, and IIIT Hyderabad.

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Beyond the Fringe: Pushing the Boundaries of Precision in Earth Observation
2026-06-29 10:00 - 11:00 | Virtual
This webinar explores the frontiers of Synthetic Aperture Radar (SAR) Earth observation, moving beyond conventional InSAR to advanced techniques that redefine measurement precision. We know that multi-stack PSInSAR reveals millimeter-scale urban subsidence and structural instability, but it is less well known that SAR coherence analysis enables rapid change detection, for example in conflict and disaster zones. The session further introduces SAR geodesy for centimeter-to-decimeter absolute positioning without ground control points, showcasing the path for  enabling GCP-free mapping. This talk bridges interferometric phase analysis with precise geodetic positioning and reveals how modern SAR is transforming from a displacement tool into a comprehensive geodetic observation system.

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Prof. Timo BalzVice-Director of the International Academy of GeoInformation at Wuhan UniversityTimo Balz was born in Stuttgart, Germany. He received the Diploma degree (Dipl.-Geogr.) in geography and the Doctoral degree (Dr.-Ing.) in aerospace engineering and geodesy from the University Stuttgart, Stuttgart, in 2001 and 2007, respectively. From fall 2001 to the end of 2007, he was a Research Assistant with the Institute for Photogrammetry, University Stuttgart. Between 2004 and 2005, he was a Visiting Scholar with Wuhan University, Wuhan, China. From 2008-2010, he was a Postdoctoral Research Fellow with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University. From 2010-2015, he was an Associate Professor for Radar Remote Sensing with LIESMARS. Since 2015, he has been a Full Professor with LIESMARS. Since 2021, he has been Vice-Director of the International Academy of GeoInformation, Wuhan University. He serves as Associate Editor for the IEEE Geoscience and Remote Sensing Magazine and MDPI’s Remote Sensing. He is a member of the editorial board of Geo-Spatial Information Science and the Journal of Digital Earth. He is Chair of an ISPRS Commission I Working Group on SAR from 2016-2022 and again from 2022-2026. He has authored and co-authored more than 150 scientific articles in journals, books, and conference proceedings. Timo Balz’s research interests include surface motion estimation with SAR, data visualization, SAR geodesy, and SAR-optical fusion.

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Climate Change and Dust Monitoring from Space: Advancing Earth Observation for Resilience in Arid and Semi-Arid Regions
2026-07-07 10:00 - 11:00 | Virtual
Arid and semi-arid regions are among the most vulnerable environments to climate change, land degradation, water stress, heat extremes, and atmospheric dust. This webinar will explore how satellite Earth observations, remote sensing, GIS, climate modeling, and data science can be used to monitor climate-related hazards and support evidence-based decision-making. Special attention will be given to dust dynamics, air quality, desertification, land surface changes, coastal and marine vulnerability, and the broader impacts of climate change across the MENA region.
Drawing on regional and international examples, including applications from Egypt, the Arabian Peninsula, and the Mediterranean region, the presentation will highlight how space-based observations can improve our understanding of interconnected Earth system processes and support climate resilience, sustainable development, and adaptation planning. The webinar will also discuss the growing role of artificial intelligence, geospatial analytics, and integrated risk assessment in transforming satellite data into actionable knowledge for governments, researchers, and environmental decision-makers.

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Prof. Hesham El-AskaryDirector Earth Systems Science and Data Solutions Lab at Chapman UniversityProf. Hesham M. El-Askary is a Professor of Earth Systems Science and Remote Sensing at Chapman University and Director of the Earth Systems Science and Data Solutions Lab. He is also the former Vice President for Special Projects and Space Missions at the Egyptian Space Agency. Currently, he serves as an Advisor to the Director General of ICESCO, a consultant and science lead for the UN International Organization for Migration, and a consultant/advisor with UNESCO on open science and capacity-building initiatives. Prof. El-Askary is an internationally recognized Earth systems scientist with more than two decades of experience in remote sensing, Earth Observation, GIS, climate change impacts, environmental modeling, natural hazards, and data-driven solutions for sustainable development. He currently contributes to the Intergovernmental Panel on Climate Change Seventh Assessment Report cycle, with a focus on climate change impacts, adaptation, vulnerability, risk, exposure, and Mediterranean-region climate challenges. This builds on his earlier IPCC contribution to the Desertification chapter of the IPCC Special Report on Climate Change and Land. His research focuses on the use of satellite Earth observations, geospatial analytics, artificial intelligence, and environmental modeling to understand complex interactions among the atmosphere, land, water, coastal, marine, and human systems. His work has addressed sea-level rise, land degradation, desertification, drought, flash floods, air quality, dust storms, coral reefs, mangroves, renewable energy, water resources, biodiversity, and climate resilience across Egypt, the MENA region, and other vulnerable regions. Prof. El-Askary has led and contributed to major projects funded by NASA, NSF, USDA, the European Union, Horizon 2020, and regional institutions.

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