Remote Sensing

This course provides an extensive overview of remote sensing technologies, which are crucial for obtaining data about the Earth’s surface without direct physical contact.

• Introduction to Remote Sensing: Definition, principles, and historical development,
• Types of Remote Sensing Systems: Active vs. passive, aerial vs. satellite, and terrestrial systems,
• Sensors in Remote Sensing: Optical, radar, thermal, LiDAR, and multispectral sensors, Electromagnetic Spectrum and its Application in Remote Sensing,
• Remote Sensing Platforms: Satellite-based, UAVs (drones), airborne sensors, and ground-based systems,
• Image Acquisition and Processing: Preprocessing, calibration, and image enhancement techniques,
• Remote Sensing Data Analysis: Classification, change detection, and feature extraction methods, Applications of Remote Sensing in land cover mapping, agriculture monitoring, environmental monitoring, and disaster management, Integration of Remote Sensing Data with Geographical Information Systems (GIS),
• Advanced Remote Sensing Techniques: Hyperspectral imaging, radar remote sensing (SAR), and LiDAR

• Geospatial and remote sensing engineers working on satellite imagery, mapping, and spatial analytics

• Environmental scientists and climate researchers analyzing land, ocean, and atmospheric data

• Urban planners, civil engineers, and surveyors using aerial and satellite data for infrastructure and land-use planning

• Aerospace and drone/UAV developers integrating optical, thermal, radar, or LiDAR sensors into airborne platforms

• Researchers and graduate students in geomatics, electrical/electronics engineering, environmental engineering, and applied physics

• GIS analysts and data scientists performing image classification, change detection, and feature extraction

• Disaster management and agricultural technology professionals leveraging Earth-observation data for monitoring and decision support