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Artificial Intelligence & Data Science, VIII-Semester
Open Elective-AD-803 (A) AI for Remote Sensing
Syllabus
Unit I: Introduction to Remote Sensing and AI Definition of Remote sensing, Principles of Remote Sensing, Introduction to Artificial Intelligence, Integration of AI in Remote Sensing
Unit II: Types of Remote Sensing Platforms and Sensors Sensors: Types and classification of sensors, Platforms: Types of platforms, ground, airborne, and space born platforms Remote Sensing Data Acquisition and Pre-processing Data acquisition techniques and image resolution, Data pre-processing and feature extraction techniques, Radiometric and geometric corrections, Data fusion and enhancement methods
Unit III: AI for Remote Sensing Supervised Learning for Remote Sensing Analysis, Unsupervised Learning for Remote Sensing Analysis, Deep Learning for Remote Sensing Analysis, Image Image Classification Techniques in Remote Sensing.
Unit IV: AI-Assisted Image Interpretation and Feature Extraction Object detection and tracking in remote sensing imagery, Change detection and time-series analysis, Hyperspectral and LiDAR data analysis, Fusion of multi-modal remote sensing data.
Unit V: Applications of AI in Remote Sensing Land cover and land use mapping, Environmental monitoring and assessment, Disaster management and response, Precision agriculture and crop monitoring.
Reference Books:
1. Rémi Cresson, “Deep Learning for Remote Sensing Images with Open Source Software”, CRC Press, 1st edition,2020.
2. Moulay A Akhloufi, MozhdehShahbazi,“Deep Learning Methods for Remote Sensing”,MDPI AG, 2022.
3. Alka Rani, Nirmal Kumar, S. K. Singh, N. K. Sinha, R. K. Jena, HimeshPatra, “Remote Sensing Data Analysis Using R”,CRC Press, Taylor & Francis Group,2021.
4. Tammy E. Parece, John A. McGee, James B. Campbell, “Remote Sensing with ArcGIS Pro”,2019.
Notes
Assignment
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