Courses
We place great importance on educating and training students in remote sensing and data science for environmental studies. Currently, we offer three courses, each carrying 3-4 academic credits
Course name: Introduction to Remote Sensing
Lecturers: Prof. Tarin Paz Kagan
Teaching Assistants: Iaroslav Grozdov
Course number: 001-1-9203
Semester: 1 Semester
Credits: 4
Introduction to Remote Sensing
Remote sensing is a primary tool for acquiring spatial information about the Earth’s surface and is widely used in scientific research, environmental monitoring, and natural resource management. This course provides students with the theoretical and practical foundations needed to understand remote sensing, from the physical principles of electromagnetic radiation and its interaction with matter to sensing systems, satellite platforms, and geospatial data analysis. During practical sessions, students will work with Sentinel satellite imagery and gain hands-on experience in basic remote sensing analysis using Google Earth Engine (GEE).
Course name: Image Processing and Computer Vision
Lecturers: Prof. Tarin Paz Kagan
Teaching Assistants: Iaroslav Grozdov
Course number: 001-2-9031
Semester: 1 Semester
Credits: 3
Image Processing and Computer Vision
This course focuses on understanding digital image processing and computer vision tools required to process, analyze, and interpret images. Combining theoretical and experimental approaches, it emphasizes image analysis using Python. The course covers a range of image processing and computer vision applications, including:
Image Processing and Computer Vision with OpenCV, Morphological operations, Face detection, Image histogram matching, Segmentation, Image classification, Object detection, Face and digit recognition, and 3D image starter. During class, students will engage in hands-on exercises to implement the concepts covered using Python and libraries such as OpenCV, NumPy, and TensorFlow. Assignments will reinforce these skills by applying the learned methods to process images for scientific and industrial applications.
Course name: Advanced Remote Sensing Application
Lecturers: Prof. Tarin Paz Kagan
Teaching Assistants: Dr. Moshe Vladislav Dubinin
Course number: 001-1-9207
Semester: 2nd Semester
Credits: 3
Advanced Remote Sensing Applications
This advanced course in remote sensing is designed to enhance students' understanding of the physical principles of remote sensing, with a particular focus on optical remote sensing. It prepares students for further advanced studies by providing hands-on training in coding and analysis using Python, Google Earth Engine (GEE), and SNAP. The course emphasizes computational approaches to solving mathematical and physical problems in remote sensing data processing and interpretation. Topics include earth observation systems, pre-and post-processing applications, and advanced analytical techniques for mapping and analysis. Practical Python programming exercises are integrated throughout the course to reinforce these concepts and skills.
Course name: UAVs for Environmental Sciences
Lecturers: Prof. Tarin Paz Kagan
Teaching Assistants: Dr. Tamir Caras
Course number:
Semester: 2nd Semester
Credits: 3
UAVs for Environmental Sciences
UAVs for Monitoring Soil, Vegetation, and Riverine Environments. This course explores how Unmanned Aerial Vehicles (UAVs) have transformed monitoring capabilities for river systems, soil properties, vegetation, and water cycle processes, providing unprecedented spatiotemporal resolutions. Students will gain hands-on experience working with field survey datasets and applying the techniques discussed in real-world case studies. The course also covers the integration of geographic information systems (GIS), GPS devices, and data mining applications to improve the precision and efficiency of agricultural and environmental operations. Designed to equip practitioners and scientists with essential guidelines, technical expertise, and practical skills, the course emphasizes enhancing monitoring efficiency using UAVs. It culminates in a final project in which students apply their acquired knowledge to address real-world challenges in agriculture and environmental management using UAV technologies.