Sensory Analysis for UAS Use Case II

Beginner

Upon completing this course, students will be able to: - Demonstrate Knowledge of Advanced Drone Sensors: Explain advanced sensor technologies used in drones, including multispectral, hyperspectral, thermal, LiDAR, and radar sensors, and describe their principles, capabilities, and limitations. - Apply Complex Data Analysis Techniques: Perform advanced data processing and analysis on sensor data, including image classification, pattern recognition, and change detection, using tools such as machine learning and AI for enhanced insights. - Master data Fusion and Integration Techniques: Fuse data from multiple sensors to create comprehensive datasets that provide richer insights than could be obtained from any single sensor and use the algorithms and software tools that facilitate this process. - Deploy Application-Specific Sensors: Select and configure drone sensor payloads optimized for specific applications, such as precision agriculture, environmental monitoring, infrastructure inspection, and disaster management. - Translate Data into Actionable Insights: Convert complex datasets into clear, actionable insights for decision-makers and present findings in a manner accessible to non-expert audiences.

None

- Sensor Selection for Applications: Criteria for selecting appropriate sensors based on specific use cases, such as agriculture, surveying, search and rescue, or environmental monitoring. - Data Acquisition and Processing: Techniques for collecting data using drone-mounted sensors, including considerations for flight planning to optimize data quality. - Optical and Thermal Imaging Analysis: Basics of processing and analyzing images from optical and thermal cameras, including applications for inspection, surveillance, and environmental monitoring. - LiDAR and 3D Mapping: Introduction to Light Detection and Ranging (LiDAR) technology for creating high-resolution maps and 3D models with applications in forestry management and urban planning. - Radar and Sonar Sensors: Exploration of radar and sonar sensors for obstacle detection, terrain following, and altitude measurement in various flying conditions. - Multispectral and Hyperspectral Imaging: Applications of multispectral and hyperspectral imaging in precision agriculture, vegetation health assessment, and environmental research. - Integration and Fusion of Sensor Data: Techniques for combining data from multiple sensors to enhance analysis, improve accuracy, and support decision-making. - Machine Learning and AI for Sensor Data Analysis: Introduction to using machine learning algorithms and artificial intelligence to interpret sensor data, identify patterns, and automate decision processes.

- Kerle, N., Nex, F., Gerke, M., Duarte, D., & Vetrivel, A. (2020). UAV-based structural damage mapping: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 104-119. - Nex, F., & Remondino, F. (2014). UAV for 3D mapping applications: A review. Applied Geomatics, 6(1), 1-15. - Schirrmann, M. (2022). UAV Imagery for Precision Agriculture. Remote Sensing, MDPI. ISSN 2072-4292

Exam

English

5

15

2.5

Presentation, group work, discussion, exercises

3

3_4

integrated lecture

Compulsory