The 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDG) were adopted at the United Nations Sustainable Development Summit in 2015, to provide a global framework for action for the next two decades. In support of the measurement and monitoring of progress toward the SDGs, the UN has established a Global Indicator Framework, designed around SDG targets and indicators. In order to progress towards the achievement of the SDG goals and targets, this framework needs to be assessed using evidence, drawn from accurate and robust data, on a continuous basis. Earth observation (EO) is used to monitor and assess the status of, and changes in the natural and manmade environment. The aim of this course is to provide both theoretical understanding and practical introduction to the use of EO for monitoring and analysis of various environmental and societal processes, contributing to achieving Sustainable Development Goals. Introduction to satellite imagery analysis will provide students with initial understanding of the remote sensing process as one of the EO techniques. The course also provides an overview of alternative Earth observation and geospatial data collection systems, mainly space-based (satellite imagery), but also unmanned aerial vehicles (UAVs), and on-ground GPS systems. The course exposes students to the basic techniques and practical skills of digital image acquisition and processing; extracting relevant information from satellite imagery and combining it with on-ground observations. Examples of the Earth observation data use in practical decision- and policy-making processes will be given by practitioners in various SDG domains, e.g. disaster management, food security, sustainability, biodiversity conservation. The list of invited speakers features UN officers and geospatial industry representatives.
A particular focus of the course is on the application of the Google Earth Engine (GEE) - a cloud-based platform, which provides access to multiple repositories of satellite imagery and geospatial datasets allowing quick and accurate analysis and visualization of large datasets available in the cloud. Special attention is paid to basic analysis techniques of land use and and cover dynamics (for disaster management, food security, urbanization, climate change, etc). The course includes a theoretical overview of GEE’s scientific principles, available datasets, methods and tools followed by hands-on in-class exercises, individual consultations with instructors, and work on the final individual project.
The course is based on the “learn-by-doing” approach: instructors-led theoretical sessions will be followed by students’ individual work. During the practical sessions students will learn how to search for geospatial data, including raw satellite imagery and thematic data (such as land cover, climate datasets, etc.); fulfill analysis using presented methods, visualize and interpret the results. Successful participation and completion of the course depend on the student's ability for individual work and self-education. The course is a preparatory activity for the OSUN GeoHub’s Summer University on Geospatial Technologies for Building Resilience, to take place in Budapest in July 2024 (https://summeruniversity.ceu.edu/courses/2024/geospatial-technologies-m…).
The number of students is limited to 15.
- Knowledge of basic principles of Remote Sensing and its applications.
- Familiarity with online satellite imagery and earth observation products.
- Practical skills in remote sensing cloud packages (Google Earth Engine).
- Understanding of earth observations use in various domains and possible career paths.
- Ability to develop and conduct a practical project using geospatial methods.
The evaluation is based upon student’s performance using the following categories:
- practical sessions and homeworks (20%);
- course project on earth observations data collection, processing and interpretation (80%).
The final project consists of identifying a problem to be analysed using remote sensing, collecting available data, applying the studied methods and presenting the obtained results, including problem formulation, data search and manipulation, and results interpretation. The project can be a technical part of thesis research.
Basic knowledge of mapping and visualization of geospatial data.