Land Webinar | Land Innovations – Data and Machine Learning
the chair
Welcome to the webinar titled "Land Innovations - Data and Machine Learning ".
As we navigate through the burgeoning era of Big Data, machine learning and deep learning technologies are witnessing an upsurge of interest from researchers across diverse domains. The synthesis of machine learning, deep learning, big data analytics, and land-use and land-cover classification, along with remote sensing, provides researchers and practitioners with a strong foundation to delve deeper, make precise predictions, and create solutions for a broad spectrum of applications.
In this webinar, presentations will be delivered by four scholars to elaborate on how machine learning and deep learning techniques, as integral parts of geospatial artificial intelligence, can be leveraged to extract valuable insights and address issues related to land. Through this interactive session, we hope that participants will acquire a comprehensive understanding of state-of-the-art machine learning applications in land-related issues.
We look forward to a session filled with insightful presentations and productive discussions.
Thank you for joining us.
Date: 25 August 2023 at 3:30 pm CEST | 9:30 am EDT | 9:30 pm CST Asia
Webinar ID: 819 3147 8463
Webinar Secretariat: journal.webinar@mdpi.com
Meet Our Speakers
Prof. Dr. Chuanrong (Cindy) Zhang
Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, USA;
Prof. Dr. Chuanrong (Cindy) Zhang is currently a Professor at the Department of Geography & Center of Environmental Sciences and Engineering, University of Connecticut, Storrs. Zhang is a broadly trained geographer with substantive interests in geospatial technologies. Her research is interdisciplinary, and in particular concentrates on geographical information science (GIS), remote sensing, geo-spatial statistics, geocomputation, and their applications in land -use/cover studies, climate change, managing disasters and natural resources, landscape ecology, environmental planning, as well as transportation studies. She has published articles in leading journals in GIS, geography, remote sensing, soil, and environmental studies. So far, she has published almost 200 peer-reviewed journal articles, book chapters, and conference proceedings. Her work has been supported by different prestige agents such as the National Science Foundation and the Department of Energy. She has served as committee chair or member for several international or national organizations in the geospatial community.
Dr. Sesil Koutra
Faculty of Architecture and Urban Planning, University of Mons, Belgium;
Dr. Sesil Koutra has been an Assistant Professor at the Faculty of Architecture and Urban Planning of the University of Mons in Belgium since September 2022. As an Engineer of Urban Planning and Design, she has multidisciplinary research interests related to energy autarky at the district level, urban resilience and climate change challenges. Her research background comprises academic participation in two Erasmus Programs (Urban Climate Change Resilience Adaptation, https://www.uccrn.education/ and Smart Cities and Communities, https://www.smaccs.eu/), but also courses of architectural laboratories related to urban resilience and landscape architecture. She has published more than 50 peer-reviewed papers in prestigious scientific journals, such as Energy and ‘Sustainable Cities and Society.
Dr. Zhijie Zhang
School of Geography, Development & Environment, University of Arizona, USA;
Dr. Zhijie Zhang aims to fill the research gap in disaster remote sensing, specifically in the area of multiple disaster coupling and disaster chains under the background of global change, through data-driven artificial intelligence remote sensing technology combined with big data GIS. They are committed to advancing interdisciplinary research applications of remote sensing, earth science, and artificial intelligence. Their past research has advanced the fields of remote sensing, GIS, and deep learning technologies, as well as their application in disaster chain research, from three aspects: hydrological processes, early recognition and warning of flood and geological disasters, and monitoring the impact of human activities on the environment under the background of global change.
Dr. Moataz Kilany
Connecticut Transportation Institute, University of Connecticut, USA;
Dr. Moataz Kilany is currently a research associate in Connecticut Transportation Safety Research Center (CTSRC). His main interest lies in machine learning applications for remote sensing and geographic information systems (GIS) and GI-Science. Kilany has a strong back ground in both computer science and GIS as he earned both his bachelor and Master degree in computer science, as well as a recent Ph.D in Geography. His research applies novel optimization methods to the remote sensing process with the aim of generating high accuracy maps. He published many articles in classification optimization for a wide range of applications including remote sensing. In the past few years, he employed his experience in classification optimization to obtain high accuracy models for urban land cover mapping. Kilany also acquired a great GIS skill set during his work with CTSRC for the past four years, which involved the analysis, design and development of GIS systems that helped the Connecticut Department of Transportation (CTDOT) with many aspects of roadway asset data collection, storage, analysis, reporting and visualization.
On Friday, 25 August 2023, MDPI and the Journal Land organized a webinar entitled "Land Innovations—Data and Machine Learning".
This was successful. First, Dr. Chuanrong Zhang introduced general land use and land cover mapping using multiple data sources, including remotely sensed imagery and geospatial big data. She discussed the challenges and future research directions in using machine learning and cloud computing for land use and land cover mapping in the era of big data. Next, Dr. Moataz Kilany presented the process of obtaining a high-quality urban land cover classification map using optimization techniques such as Swarm algorithms. Finally, Dr. Zhijie Zhang explained how to harness various big remote sensing data products and deep learning methods for disaster applications.
A Q&A session and discussion moderated by the Chair was conducted at the end. The webinar was hosted via Zoom and required registration to attend. The full recording can be found below. In order to learn about future webinars, be sure to sign up to our newsletter by clicking on “Subscribe” at the top of the page.
Feature Papers for Land Innovations – Data and Machine Learning
Guest Editor: Prof. Dr. Chuanrong (Cindy) Zhang
Deadline for Manuscript Submissions: 27 October 2023