Sensory Analysis for UAS Use Case I

Beginner

Upon completing this course, students will be able to: - Identify UAS sensor technologies: Describe the types of sensors commonly used in drones, including optical, thermal, multispectral, LiDAR, and radar sensors, and explain their use cases. - Plan and Conduct Sensor Data Collection: Develop and execute plans for UAS flights to efficiently collect data from onboard sensors, evaluating the impact of flight parameters on data quality. - Process Raw Sensor Data: Apply basic techniques for processing raw sensor data, such as image stitching, filtering, and preliminary analysis, to prepare data for further interpretation. - Analyze Sensor Data for Applications: Implement simple analysis methods to sensor data to extract useful information for applications in agriculture, environmental monitoring, or infrastructure inspection. - Integrate Sensor Data with GIS: Integrate processed sensor data with Geographic Information Systems (GIS) to enhance spatial analysis and visualization. - Ensure Sensor Data Quality and Accuracy: Evaluate data quality and accuracy in sensory analysis, including calibrating sensors and validating data against ground truth.

Data & Analysis

- Introduction to UAS Sensors: Overview of common types of sensors used in UAS, including optical, thermal, LiDAR, radar, and multispectral sensors, and their operational principles. - 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.

- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: with Applications in R (2nd ed.). Springer. ISBN: 978-1071614174. - Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. ISBN: 978-0387310732. - Oppenheim, A. V., & Schafer, R. W. (2021). Discrete-Time Signal Processing (3rd ed.). Pearson. ISBN: 978-0137549771. - Shumway, R. H., & Stoffer, D. S. (2018). Time Series Analysis and Its Applications: With R Examples (4th ed.). Springer. ISBN: 978-3319524511. DOI:10.1002/9781119528227

Portfolio tests

English

5

15

2.5

Presentation, group work, discussion, exercises

2

2_4

integrated lecture

Compulsory