Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Eyes in the Sky AI explores how computer vision applied to aerial and satellite imagery turns a view from above into actionable insight. You learn to work with drone and satellite images and build models that detect objects, map land and infrastructure, and monitor change from the sky. The course spans applications across agriculture, environment, security and urban monitoring, and the practicalities of handling large aerial imagery. You finish able to reason about an aerial-vision AI system. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI-powered aerial and satellite vision — applying computer vision to drone and satellite imagery for monitoring, mapping and detection from the sky.
📋 Course Objectives
1. Work with drone and satellite imagery.
2. Detect objects and features from above.
3. Map land, infrastructure and change.
4. Handle large-scale aerial image data.
5. Apply aerial vision across domains.
👥 Who Should Enroll?
• Remote-sensing and drone professionals
• Computer-vision developers
• GIS and monitoring analysts
• Students of aerial imaging
🚀 Key Learning Outcomes
• An understanding of aerial-vision AI.
• An aerial-imagery analysis project.
• A cross-domain monitoring perspective.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Eyes In The Sky Foundations
Apply linear algebra and calculus concepts to solve problems in computer vision and machine learning • Develop a comprehensive understanding of AI and machine learning fundamentals, including supervised and unsupervised learning • Configure and implement Python libraries such as NumPy, pandas, and Matplotlib for data analysis and visualization
Data Engineering, Preprocessing, and Feature Pipelines
Design and implement data pipelines using Apache Beam and Google Cloud Dataflow for efficient data processing • Analyze and preprocess large datasets using techniques such as data normalization, feature scaling, and data augmentation • Evaluate the performance of different data preprocessing techniques using metrics such as accuracy, precision, and recall
Model Architecture, Algorithm Design, and Eyes In The Sky Methods
Implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs) using TensorFlow and Keras for image and signal processing • Develop and train machine learning models using techniques such as transfer learning, fine-tuning, and hyperparameter tuning • Configure and optimize model architectures using techniques such as batch normalization, dropout, and early stopping
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate machine learning models using techniques such as cross-validation, grid search, and random search • Analyze and visualize model performance using metrics such as accuracy, precision, recall, and F1-score • Optimize hyperparameters using techniques such as Bayesian optimization, gradient-based optimization, and evolutionary algorithms
Deployment, MLOps, and Production Workflows
Deploy machine learning models using cloud platforms such as Google Cloud AI Platform, AWS SageMaker, and Azure Machine Learning • Design and implement MLOps pipelines using tools such as TensorFlow Extended, Kubeflow, and MLflow • Configure and manage model serving and monitoring using tools such as TensorFlow Serving, AWS SageMaker Hosting, and Azure Machine Learning Model Management
Ethics, Bias Mitigation, and Responsible AI Practices
Evaluate and mitigate bias in machine learning models using techniques such as data preprocessing, feature engineering, and model regularization • Develop and implement responsible AI practices using techniques such as transparency, explainability, and accountability • Analyze and address ethical concerns in AI development and deployment using frameworks such as fairness, privacy, and security
Industry Integration, Business Applications, and Case Studies
Apply Eyes in the Sky AI concepts to real-world industry applications such as agriculture, transportation, and healthcare • Develop and implement business cases for Eyes in the Sky AI solutions using techniques such as cost-benefit analysis and return on investment (ROI) analysis • Evaluate and analyze case studies of successful Eyes in the Sky AI deployments using metrics such as accuracy, efficiency, and profitability
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | OpenCV |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | pandas |
| Covered Tool / Platform | Matplotlib |
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