ESGRS

Application of Remote Sensing and GIS to Flood and Rainwater Harvesting
2021-09-21 10:00 - 11:00 (Asia/Dubai)
The construction of the Grand Ethiopian Renaissance Dam (GERD) will have positive and negative impacts on the people living downstream. Among possible impacts is the probability of flooding of human settlements along the Blue Nile. Reduction of water level is also a possible adverse effect on the agricultural schemes along the Blue Nile; the Gezira scheme, one of the largest irrigation projects in the world. The objective of this study is two-fold. The first objective is to identify cities and villages near the Sennar Dam (in Sudan) that may be affected by potential fluvial (river) floods. The second objective is to select rainwater-harvesting (RWH) sites that can be used for water-supply backup while minimizing possible water-level reduction. RWH is in line with UN Global Sustainable Development Goals (UN-SDG). The use of remote-sensing techniques has enabled access to unique sources of information (satellite images and DEM) to better characterize hazards and risks, and a GIS was used as an effective modeling and visualization tool.Keywords: Fluvial flood, Rainwater harvesting, Sennar Dam, Sudan, Remote sensing, GIS

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DR. MOHAMED YAGOUBDEPARTMENT OF GEOGRAPHY AND URBAN SUSTAINABILITY, UAEUProf. Yagoub led Graduate Studies at the Department (20016-2020), chaired the Department between 2005 and 2007, and directed the Master Program of Remote Sensing and GIS at the UAE University between 2002 and 2005. His scholarship is indexed in Google Scholar, Scopus, Web of Science, and Research Gate. Dr. Yagoub teaches various courses related to Geoinformatics such Remote Sensing, Geographic Information System (GIS), and Global Navigation Satellite Systems (GNSS). Dr. Yagoub is a referee to many organizations/journals. He participated in organization of national and international conferences and co-chaired the 5th International Conference on Earth Observation for Global Changes (EOGC), 2015. He received Abu Dhabi -Spatial Data Infrastructure (AD-SDI) Distinguished Award for contribution to GIS/Remote Sensing academic programs (2018). He also received the Teaching Faculty Recognition Award on August 28, 2007 bestowed by H.H. Sheik Nahayan Mabarak Al Nahayan, former Minister of Higher Education and Scientific Research.

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Remote Sensing of Soil Salinity in the United Arab Emirates
2021-09-07 10:00 - 11:00
Soil salinity, whether natural or human induced, is a major geo-hazard in arid and semi-arid landscapes. In agricultural lands, it negatively affects plant growth, crop yields, whereas in semi-arid and arid non-agricultural areas it affects urban structures due to subsidence, corrosion and ground water quality, leading to further soil erosion and land degradation. Accurately mapping soil salinity through remote sensing techniques has been an active area of research in the past few decades particularly for agricultural lands. Most of this research has focused on the utilization and development of salinity indices for properly mapping and identifying saline agricultural soils. This research develops a soil salinity index and model using Landsat 8 OLI image data from the near infra-red and shortwave infra-red spectral information with emphasis on soil salinity mapping and assessment in non-agricultural desert arid and semi-arid surfaces. The developed index when integrated into a semi-empirical model outperformed in its soil salinity mapping overall accuracy (60%) in comparison to other salinity indices (~50%). The newly developed index further outperformed other indices in its accuracy in mapping and identifying high saline soils (67%) and excessively high saline soils (90%).

DR. NAEEMA AL HOSANIChair, Department of Geography and Urban Sustainability, UAEUDr. Naeema Alhosani, obtained her Ph. D. (2009), and her M.A. (2005) in Geography “Cartography” from the University of Kansas, USA. Currently, she is department chair since Aug. 2017, as well as she was an assistant dean for student affairs in CHSS from 2013-2017. She has many awards. Her research interests are: Cartography, Maps & Mapping, Map Conception & Development, Map scale, Cartographic Design, Information Design, Practicum in Cartography, Problems in Production map, Cartography and Environment, GIS, Remote Sensing, and Education. Dr. Naeema has participated in many conferences (both regional and international) and research projects as well as maps publications, besides being a member of many professional committees. Also she is affiliated to many professional associations. She has various contributions at the departmental, College, University and Community levels.

DR. ABDELGADIR ABUELGASIMAssociate Professor, Department of Geography and Urban Sustainability, UAEUDr. Abuelgasim received both his masters and PhD degrees from Boston University in 1996. He later joined the NASA Goddard Space Flight Center in Maryland, USA in 1998 as a visiting scientist where he worked in a research program exploiting multi-angle remote sensing observations. In 2000 he relocated to Canada where he has been with the Canada Centre for Remote Sensing and the Canadian Space Agency as a research scientist. Dr Abuelgasim also served as the international geospatial data consultant during 2012 for the Nile Basin Initiative. Dr Abuelgasim research and teaching interests are focused on the analysis of geospatial data for information products generation, physical and human geography and on the effects of human induced environmental and ecological changes. He is currently an associate professor of geography at UAEU.

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Spatial Enhancement of Hyperspectral Satellite Imagery
2021-07-13 10:00 - 11:00
Remote sensing technology has undeniable importance in various industrial applications. Some of which include mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image types that can facilitate the aforementioned applications. One such type is Hyperspectral Images (HSIs). Unlike multispectral and natural images, HSIs consist of hundreds of bands, which makes it easier to identify objects through their spectral reflectance signals. Despite its high spectral resolution, HSIs suffer from low spatial resolution. This is a natural tradeoff that occurs due to cameras attempting to capture 3D images using 2D sensors. In order to implement remote sensing applications effectively with satisfying accuracy, HSIs need to have high spectral resolution as well as high spatial resolution. This webinar discusses the several advances in the field of HSI spatial enhancement, highlights and assesses the most recent state-of-the-art approaches, and draws the future direction of this research field.

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ENG. NOUR ABURAEDRESEARCH ASSISTANT @MBRSC LAB - UNIVERSITY OF DUBAINour Aburaed is a holder of BSc degree in Electrical and Computer Engineering and MSc degree in Electrical and Computer Engineering from Khalifa University of Science and Technology in Abu Dhabi – UAE. Her MSc is specialized in High-ISO Image De-noising and Quantum Image Processing. Nour was a Teaching Assistant for various math, physics, and programming courses at Khalifa University for two years. She has been working as a Research Assistant at Mohammed Bin Rashid Space Centre Laboratory based at the University of Dubai since 2018, where she specializes in applications of Image Processing and Artificial Intelligence within the context of remote sensing imagery. Nour is also currently a PhD student at the University of Strathclyde, where she studies the enhancement of Hyperspectral Satellite Imagery. Her research and study activities resulted in various publications in notable conferences and journals. Her current interests include object detection, semantic segmentation, single image super resolution, hyperspectral images, convolutional/complex/quantum neural networks, and data analytics and visualization.

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Automated Surveying Using AI & Google Maps Satellite Imagery
2021-06-29 10:00 - 11:00
In this session, InfraLytiks will present some very interesting capabilities and case studies for automation and Artificial Intelligence (AI) case studies for Satellite Data based automated surveying. The focus will be on a case study have been successfully developed and completed by InfraLytiks for engineering surveying & planning. AI has been used to automatically identify and generate shapefiles for objects of interest for surveying such as road attribute detection, tress cover, water body & structure types identification from Google Maps API. A live demo will also be performed to showcase the AI algorithm in actions along with additional software features. InfraLytiks is a complex data analytics and automation company based in the USA & UAE that specializes in engineering-based data analytics and development of custom software products.

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AKASH VIDYADHARANCHIEF TECHNOLOGY OFFICERFounder & CTO of InfraLytiks · 4+ years of successful company operations in USA · Experience in leading ML, AI, Automation & Software Development · Experience in Robotics & Drone Development & Automation · BS Aerospace Engineering · MS Aerospace Engineering · M.Eng. Systems Engineering · Iowa State University, USA

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Earth Observation in the New Space Age
2021-06-15 10:00 - 11:00
The “NewSpace” age has been a great disruptor of the earth observation industry ecosystem over the last few years, more so in the last three years with several new startups announcing plans to develop constellations of microsatellites with the ability to provide very high spatial resolution data. This competition is significantly accelerating innovation in both hardware and software, thereby contributing to the enhancement of the quality of data. The amount of high-resolution data is significantly increasing both from optical and radar satellites. Along with the spatial resolution, the concept of constellations has greatly improved the temporal resolution of the data and this is expected to get better with the launch of new constellations and adding more satellites to the existing constellations such as Planet, ICEYE, Satellogic, etc. In parallel to the data explosion, the field of machine learning has gone through a significant disruption due to the improving hardware capabilities and the exponential growth of deep learning architectures. This is greatly supporting the processing of the large amount of data becoming available from the satellites. In line with these developments, this talk explores how data and the corresponding processing architectures are expected to be handled in the future with an outlook on opportunities that can be created.

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DR. PRASHANTH MARPUTECH LEAD OF SPACE PROGRAM AT GROUP 42Dr. Prashanth Marpu is the Tech Lead of Space Program at Group 42, Abu Dhabi, United Arab Emirates. Prior to joining G42, he was an Associate Professor in the Department of Electrical Engineering and Computer Science and Manager of YahSat Space Lab at Khalifa University of Science and Technology, Abu Dhabi, UAE. His specialization is in small satellites, image processing, machine learning, remote sensing and internet of things. Before coming to the UAE, Dr. Marpu worked as a researcher at the University of Iceland and at the University of Pavia, Italy, where he was a Marie Curie Fellow working on developing advanced methods for earth observation data analysis. He holds a PhD in Remote Sensing from TU Freiberg, Germany, and MSc in Wireless Engineering from the Technical University of Denmark. He was involved in designing and building small satellites as a project manager of four satellite programs MYSat-1, DhabiSat, MeznSat and Light-1 while participating as an advisor and reviewer for several other satellite programs. He has also actively contributed to the development of novel approaches for processing remote sensing data for a wide range of applications. Dr. Marpu has been a Council member of the International Society of Digital Earth since 2020 and has been part of several international committees related to Earth Observation technologies. He has contributed to more than 150 publications in various journals and conferences and has been actively involved in the space and remote sensing educational activities.

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Contribution of GIS and remote sensing technologies to an integrated coastal and marine environmental management framework
2021-06-01 10:00 - 11:00
The coasts and islands that flank the United Arab Emirates (UAE), host an important number of benthic habitats; seagrass beds and coral reefs that should be the focus of conservation actions. Well-designed conservation and management plans require efficient monitoring systems that include mapping the extent and location of the major habitats and also understanding their status and trends over time. The coastal waters of the UAE are optically complex and heterogeneous, thus providing a challenging environment for mapping and monitoring seagrass beds and coral reefs. This is due to the similarity in reflectance characteristics of benthic features such as seagrass, corals and macroalgae. The mapping limitations are mainly faced by broadband multispectral sensors usually advantageous for their affordability. These challenges are taken on by providing more accurate knowledge on the UAE benthic habitats’ spatial distribution and more insights into their temporal variations. Advances in GIS, remote sensing instrumentation, data availability, and processing methods are presented and discussed. Additional tools to better evaluate and monitor the conditions of the UAE’s marine environment are presented. An integrated framework for future investigations that are at the forefront of contemporary marine environment science and management issues is suggested.

DR. HAIFA BEN-ROMDHANEVISITING ASSISTANT PROFESSORHaïfa Ben-Romdhane is presently a visiting assistant professor to the department of Geography and Planning, and the Environment: Dynamics of Territories and Societies programme at the Paris Sorbonne University Abu Dhabi. Dr. Ben-Romdhane received her PhD in Interdisciplinary Engineering (2016) from the Masdar Institute of Science and Technology in collaboration with Massachusetts Institute of Technology. She conducted her master’s studies at the Atmosphere and Ocean Research Institute of the University of Tokyo, Japan, and received her MSc degree in Aquatic Biosciences – Ecology, Behaviour and Observation Systems (2009). Before that, she received her BEng degree in Marine Environmental Engineering (2005) from the National Agronomic Institute of Tunisia. Before joining Sorbonne University, she was a researcher at the Research Centre for Renewable Energy Mapping and Assessment (ReCREMA), Khalifa University, Masdar Campus. Before joining Khalifa University, she worked as a project manager, data processing and data quality specialist at the Military Survey Department – Bayanat for Mapping and Surveying Services, Abu Dhabi, UAE. Her expertise falls in the fields of statistical methods and image processing with applications in the general fields of environmental data analysis, land use, land cover, change detection and analysis, time series analysis of environmental and climatic variables, water resources monitoring, etc. Her aspirations are to help protect the ocean and marine environments through the establishment of adaptive management plans, highlighting the role of science to provide a basis for robust decision-making against urgent problems and long-term management outcomes.

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Protecting Satellite Images Against Attacks using AI and Image Processing Techniques
2021-05-20 10:00 - 11:00
Satellite imagery is a pivotal source of valuable information for monitoring our planet along with natural resources. However, these images have an extreme acquisition cost. Nowadays, with the widespread of the advanced technology, unauthorized ordinary people can access these data, modify their content and utilize them illegally. Therefore, there is a massive demand for providing secure storage and transmission of such data. Hence, digital watermarking has been introduced for overcoming this illegitimate practice.

DR. ALAVIKUNHU PANTHAKKANASSISTANT PROFESSORDr. Alavikunhu Panthakkan is a dynamic research scientist in electronics engineering and image signal processing. His research interests are in the areas of engineering education, copyright protection, authentication, medical image processing, video signal processing and artificial neural network. He received bachelor degree (B.Tech) in electronics and communication engineering, master degree (M.Tech) in electronics engineering and Ph.D. in electronics engineering. He has 4 years industrial experience and 7 years of teaching/research experience. He has published 20 papers in international conferences and journals. He is a member of Institute of Electrical and Electronic Engineers (IEEE), member of Institution of Engineers (India) (MIE), member of International Association of Engineers (IAENG), member of International Association of Computer and Information Technology (IACSIT) and member of Institute of Research Engineers and Doctors (IRED).

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Semantic Segmentation of Roads in Satellite Imagery
2021-05-04 10:00 - 11:00
Remote sensing is a field that is constantly growing due to its importance in applications related to urban planning, risk assessment, resource exploration, and disaster management. Some of the most highly researched topics in the area of remote sensing include Land Cover Land Use (LCLU), object detection, scene classification, and semantic segmentation. Nowadays, researchers are looking into automating these tasks, as performing them manually is time and cost inefficient. Therefore, Deep Learning (DL) algorithms have been utilized for remote sensing and image processing tasks in the recent years. Many studies proved efficiency of DL algorithms, however, several challenges remain unsolved. In particular, semantic segmentation is one of the challenging tasks that is under heavy demand in the field of remote sensing. For this research study, semantic segmentation is applied to extract roads from satellite images. Road mapping is an essential first step in several applications that include transportation, traffic management, and city planning. Extracting roads efficiently and accurately will in turn boost the overall outcome of the aforementioned applications. U-Net is the state-of-the-art Convolutional Neural Networks (CNN) that perform semantic segmentation. This network will be trained using publically available Massachusetts dataset. The accuracy and loss of all experiments will be reported and sample results from the dataset will be demonstrated.

ENG. MINA AL-SAADRESEARCH ASSISTANT @MBRSC LAB - UNIVERSITY OF DUBAIMina Al-Saad is a Research Assistant at Mohammed Bin Rashid Space Center (MBRSC) Lab at university of Dubai. She received her BSc in Laser and Optoelectronics Engineering from Al-Nahrain University, Iraq in 2009 and received her MSc in Laser and Optoelectronics Engineering from Al-Nahrain University, Iraq (2012). Her research interests include: Geographic information system (GIS) and remote sensing, Autonomous Object Detection, Semantic Segmentation, and Satellite Calibration and Validation.

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Satellite Remote Sensing for Maritime, Urban Planning and Agricultural Applications
2021-04-20 10:00 - 11:00
Satellite remote sensing has emerged as a transformative technology with diverse applications, notably in maritime monitoring, urban planning, and agriculture. In the maritime domain, satellites with advanced sensors can track vessel movements, detect oil spills, and monitor ocean health, bolstering maritime security and environmental conservation efforts. In urban planning, satellite data aids in mapping urban growth, infrastructure development, and land-use patterns, enabling informed decision-making for sustainable city planning. Furthermore, satellites provide invaluable insights into crop health, yield prediction, and soil moisture levels in agriculture, empowering farmers to optimize their practices and enhance food security. With its far-reaching capabilities, satellite remote sensing continues revolutionizing these critical sectors, offering a bird’s-eye view of our ever-changing world.

DR. ALI AL HAMMADICO-FOUNDER OF FARMINDr. Ali Al Hammadi received his BSc in Chemical Engineering (CE) from the Petroleum Institute, Abu Dhabi (honors with distinction, 2011) and received his PhD from the Department of Chemical and Biomolecular Engineering (CHBE) at Rice University, Houston, TX, USA (2016). His research is focused mainly on the challenges facing the oil industry as more heavy crude oils are produced; in particular, the clogging of wellbores, chocks, pumps, and reservoirs caused by the presence of asphaltene. The effect of asphaltene is an example of how both thermodynamics and kinetics can contribute to a problem. On the thermodynamic part, it mainly concerns with how asphaltene presence in a mixture or crude oil can affect the phase behavior and PVT properties. On the kinetics part, it mainly emphasizes the precipitation, aggregation, and deposition interactions and the prediction of the extent of such deposit. Another main research of Dr. Al Hammadi are nanotubes and fiber-reinforced composites. Experiments are being established to purify nanotubes by removing byproducts that hinder the full capabilities of nanotubes and study their scalability. Such nanotubes composites can be used for better electrical efficiency, lower densities, and extreme temperature applications.

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Use Cases of GIS and Remote Sensing Projects: from Concepts to Applications
2021-04-06 10:00 - 11:00
GIS (Geographic Information Systems) and remote sensing technology use various applications, from conceptualization to practical implementation. These powerful tools have revolutionized the way we interact with spatial data. Starting with concepts, GIS aids in spatial data collection, analysis, and visualization, facilitating decision-making processes in fields as diverse as urban planning, natural resource management, and disaster response. Remote sensing complements GIS by providing real-time and historical Earth observation data, enabling applications like environmental monitoring, crop yield prediction, and disaster risk assessment. Together, GIS and remote sensing create a dynamic synergy, transforming abstract ideas into tangible solutions that address complex challenges in our ever-evolving world.
