This industry-oriented training program is designed to build strong practical expertise in Python programming, geospatial analysis, machine learning, computer vision, deep learning, and cloud-based geospatial AI workflows. The curriculum progresses from foundational Python programming to advanced AI-driven remote sensing applications using modern geospatial technologies.

• Duration: 32 Weeks
• Class Frequency: 1 Day per Week
• Session Duration: 2 Hours per Session
• Total Contact Hours: 64 Hours
• Capstone Mentoring: Additional Guided Support
• Learning Format: Hands-on Practical Training + Real-world Projects

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PHASE 1: Python Foundations (Weeks 1–4)

Total Hours: 8

Week 1: Introduction to Python Programming

Topics Covered

• Introduction to Python for Geospatial Applications
• Installing Python using Anaconda & Miniconda
• Jupyter Notebook Environment
• VS Code Setup and Configuration
• Python Syntax Fundamentals
• Variables and Data Types
• Operators and Expressions
• Conditional Statements
• Loops (For & While)
• User Inputs and Output Formatting

Practical Exercises

• Basic Python coding exercises
• Simple geospatial coordinate calculations
• Data type conversion practices

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Week 2: Functions and File Handling

Topics Covered

• Python Functions
• Function Parameters and Return Values
• Modules and Libraries
• Importing External Packages
• File Handling Operations
• Reading & Writing CSV Files
• Working with JSON Data
• Exception Handling and Debugging

Mini Project

Location Dataset Automation

• Reading geospatial coordinate datasets
• Automating CSV processing
• Exporting cleaned outputs

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Week 3: Numerical Computing with NumPy

Topics Covered

• Introduction to NumPy
• Creating NumPy Arrays
• Array Indexing & Slicing
• Vectorized Operations
• Broadcasting
• Statistical Computations
• Matrix Mathematics
• Multi-dimensional Arrays

Practical Exercises

• Raster-like matrix operations
• Numerical transformations for satellite data

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Week 4: Data Analysis using Pandas

Topics Covered

• Pandas DataFrames
• Reading and Writing Tabular Data
• Data Cleaning Techniques
• Handling Missing Values
• Filtering and Aggregation
• Time-Series Data Basics
• DateTime Operations
• Exploratory Data Analysis

Mini Project

Temporal Rainfall Analysis

• Rainfall trend analysis
• Monthly and yearly aggregation
• Visualization of rainfall patterns

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PHASE 2: Core Geospatial Python (Weeks 5–10)

Total Hours: 12

Week 5: Introduction to GeoPandas

Topics Covered

• Introduction to Geospatial Python
• GeoPandas Fundamentals
• Spatial Vector Data
• Shapefile Structure
• GeoJSON Format
• Geometry Types
  • Point
  • Line
  • Polygon
• Spatial Attribute Tables

Practical Exercises

• Loading vector datasets
• Mapping administrative boundaries

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Week 6: Spatial Analysis using GeoPandas

Topics Covered

• Spatial Joins
• Overlay Analysis
• Buffer Operations
• Proximity Analysis
• Intersection and Union
• Clipping Vector Layers

Mini Project

Urban Buffer Analysis

• Road buffer creation
• Urban accessibility assessment
• Service zone mapping

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Week 7: Coordinate Reference Systems (CRS)

Topics Covered

• Understanding Coordinate Systems
• Geographic vs Projected CRS
• EPSG Codes
• Datum Concepts
• Reprojection Techniques
• PyProj Library
• Coordinate Transformation

Practical Exercises

• Reprojecting datasets
• CRS correction workflows

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Week 8: Raster Data Processing with Rasterio

Topics Covered

• Raster Data Fundamentals
• GeoTIFF Structure
• Raster Metadata
• Reading Single & Multi-band Raster
• Raster Visualization
• Pixel Resolution Concepts

Practical Exercises

• Loading Sentinel and Landsat imagery
• Multi-band visualization

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Week 9: Raster Analysis & Vegetation Indices

Topics Covered

• Raster Clipping & Masking
• Raster Calculations
• Band Math
• NDVI Computation
• Vegetation Analysis
• Raster Exporting

Practical Exercises

• Vegetation health monitoring
• NDVI-based analysis

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Week 10: Time-Series Raster Analysis

Topics Covered

• Multi-temporal Raster Processing
• Raster Stacking
• Change Detection Techniques
• Temporal Trend Analysis
• Seasonal Vegetation Monitoring

Mini Project

NDVI Trend Monitoring

• Time-series NDVI generation
• Vegetation trend assessment
• Spatial change visualization

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PHASE 3: Machine Learning for Geospatial Applications (Weeks 11–15)

Total Hours: 10

Week 11: Introduction to Machine Learning

Topics Covered

• Fundamentals of Machine Learning
• Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
• Training & Testing Data
• Feature Engineering
• Data Normalization
• Model Workflow

Practical Exercises

• Preparing geospatial training datasets

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Week 12: Machine Learning Algorithms

Topics Covered

• Random Forest Classifier
• Support Vector Machine (SVM)
• Feature Extraction from Raster Data
• Model Training Workflow
• Hyperparameter Concepts

Practical Exercises

• Land cover classification using ML

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Week 13: Model Validation & Accuracy Assessment

Topics Covered

• Accuracy Assessment
• Confusion Matrix
• Precision, Recall & F1-Score
• Kappa Coefficient
• Cross-validation Techniques
• Overfitting vs Underfitting

Practical Exercises

• Classification accuracy evaluation

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Week 14: Advanced Feature Engineering

Topics Covered

• Multi-sensor Data Fusion
• Sentinel-1 & Sentinel-2 Integration
• Texture Analysis
• GLCM Texture Features
• Feature Stacking
• Dimensionality Reduction

Practical Exercises

• Multi-source feature generation

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Week 15: Machine Learning Mini Project

Mini Project

Land Cover Classification using Multi-Sensor Data

• Data preprocessing
• Feature extraction
• ML model training
• Classification map generation
• Accuracy validation

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PHASE 4: Computer Vision for Remote Sensing (Weeks 16–20)

Total Hours: 10

Week 16: OpenCV Fundamentals

Topics Covered

• Introduction to Computer Vision
• OpenCV Basics
• Image Reading & Writing
• Image Filtering
• Edge Detection
• Image Enhancement Techniques

Practical Exercises

• Satellite image enhancement

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Week 17: Image Segmentation Techniques

Topics Covered

• Image Segmentation Concepts
• Thresholding Methods
• Morphological Operations
• Binary Image Processing
• Connected Components

Practical Exercises

• Water body segmentation

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Week 18: Object Detection Concepts

Topics Covered

• Object Detection Fundamentals
• Sliding Window Techniques
• Feature Descriptors
• ORB Features
• SIFT Concepts
• Bounding Box Concepts

Practical Exercises

• Feature extraction from aerial imagery

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Week 19: Deep Learning Foundations for Vision

Topics Covered

• Neural Network Basics
• Convolutional Neural Networks (CNN)
• Convolution Operations
• Activation Functions
• Pooling Layers
• Feature Maps

Practical Exercises

• Simple image classifier development

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Week 20: Computer Vision Mini Project

Mini Project Options

• Rooftop Extraction
• Flood Extent Segmentation
• Building Footprint Mapping

Workflow

• Data preprocessing
• Image segmentation
• Object extraction
• Accuracy evaluation

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PHASE 5: PyTorch & Deep Learning for Geospatial AI (Weeks 21–27)

Total Hours: 14

Week 21: Introduction to PyTorch

Topics Covered

• PyTorch Installation
• Tensor Operations
• Automatic Differentiation (Autograd)
• Building Simple Neural Networks
• Training Loops

Practical Exercises

• Basic neural network implementation

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Week 22: CNN Implementation in PyTorch

Topics Covered

• CNN Architecture Implementation
• Custom Dataset Creation
• DataLoader for Raster Patches
• Batch Processing
• Model Training

Practical Exercises

• Satellite patch classification

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Week 23: Transfer Learning

Topics Covered

• Transfer Learning Concepts
• Pre-trained Models
• ResNet Architecture
• Fine-tuning Techniques
• Model Optimization

Practical Exercises

• Satellite image classification using ResNet

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Week 24: Semantic Segmentation

Topics Covered

• Semantic Segmentation Concepts
• U-Net Architecture
• Encoder-Decoder Networks
• Loss Functions
  • CrossEntropy Loss
  • Dice Loss
• Segmentation Metrics

Practical Exercises

• Flood or building segmentation

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Week 25: SAR & Optical Data Fusion

Topics Covered

• SAR Fundamentals
• Sentinel-1 Preprocessing
• Speckle Noise Concepts
• SAR & Optical Fusion Strategies
• Multi-source AI Integration

Practical Exercises

• SAR-based flood mapping

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Week 26: Time-Series Deep Learning

Topics Covered

• Sequential Data Concepts
• LSTM Networks
• NDVI Forecasting
• Time-Series Prediction
• Temporal Pattern Learning

Practical Exercises

• Vegetation forecasting

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Week 27: Deep Learning Mini Project

Mini Project Options

• Deep Learning Land Cover Mapping
• Flood Mapping using U-Net
• Building Detection using CNN
• SAR-based Segmentation

Workflow

• Dataset preparation
• Model training
• Performance evaluation
• Prediction mapping

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PHASE 6: Web GIS, Cloud Computing & Deployment (Weeks 28–30)

Total Hours: 6

Week 28: Flask API Development

Topics Covered

• Introduction to Flask
• Building ML APIs
• GeoJSON Services
• API Endpoints
• Model Deployment Concepts

Practical Exercises

• Serving geospatial ML predictions

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Week 29: Web GIS Dashboard Development

Topics Covered

• Leaflet Fundamentals
• Interactive Web Mapping
• Dashboard Development using Dash
• Frontend Integration
• Geospatial Visualization

Practical Exercises

• Building interactive GIS dashboards

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Week 30: Cloud-Based Geospatial Processing

Topics Covered

• Google Earth Engine Python API
• Cloud Computing Concepts
• Large-scale Raster Processing
• Scalable Geospatial Analysis
• Automated Remote Sensing Pipelines

Practical Exercises

• Cloud-based geospatial workflow automation

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PHASE 7: Capstone Project & Industry Simulation (Weeks 31–32)

Total Hours: 4 + Guided Mentoring

Week 31: Capstone Planning

Topics Covered

• Project Topic Selection
• Problem Definition
• Data Pipeline Design
• Workflow Architecture
• Research Methodology

Deliverables

• Project proposal
• Dataset preparation
• Workflow planning

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Week 32: Final Project Presentation

Topics Covered

• Final Model Demonstration
• Visualization & Reporting
• Industry Feedback Simulation
• Presentation Skills
• Project Documentation

Deliverables

• Final report
• Presentation slides
• Working AI workflow

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Capstone Project Options

Students will select one industry-level project:

1. Deep Learning Flood Mapping using SAR + Optical Data
2. Crop Yield Prediction using LSTM Networks
3. Building Detection using U-Net
4. Urban Heat Island Forecasting
5. Mineral Prospectivity Mapping using ML + Texture Features
6. Deforestation Monitoring using Deep Learning
7. Landslide Susceptibility Mapping
8. Water Quality Prediction using Remote Sensing AI

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Software & Libraries Covered

Python Libraries

• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• GeoPandas
• Rasterio
• OpenCV
• PyTorch
• TensorFlow (Conceptual Overview)
• Folium
• Leaflet
• Flask
• Dash

Platforms & Tools

• Jupyter Notebook
• VS Code
• Google Earth Engine Python API
• GitHub
• Google Colab

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Learning Outcomes

By the end of the course, students will be able to:

• Build Python workflows for geospatial analysis
• Process vector and raster geospatial datasets
• Perform remote sensing analysis using Python
• Develop machine learning models for spatial prediction
• Build computer vision workflows for satellite imagery
• Implement deep learning architectures for geospatial AI
• Integrate SAR and optical remote sensing data
• Develop interactive Web GIS applications
• Deploy geospatial AI workflows using cloud technologies
• Execute industry-standard end-to-end geospatial AI projects