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Master AI and Machine Learning for Real-World Geospatial Applications

A 10-day intensive GEOAI program designed to help learners apply Artificial Intelligence and Machine Learning to remote sensing, spatial analytics, environmental monitoring, urban studies, and disaster-related use cases. This course combines geospatial fundamentals, machine learning workflows, deep learning concepts, and time-series modeling using practical datasets and hands-on exercises.

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Live coding, and guided hands-on practice

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Online (Google Meet/Zoom)

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2 hours per day (Monday-Friday)

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Duration-30 Days

Who Should Attend

-GIS and Remote Sensing learners

-Geospatial analysts and professionals

-Environmental and climate researchers

-Urban and smart city planners

-Disaster management practitioners

-Students and professionals looking to apply AI/ML in geospatial workflows

Image by Hunters Race

Learning Outcomes

By the end of this course, participants will be able to:

  • Understand AI and ML concepts in geospatial problem-solving

  • Preprocess raster and vector datasets for analysis

  • Build regression and classification models for environmental and urban applications

  • Apply clustering and dimensionality reduction techniques

  • Understand CNN workflows for satellite image analysis

  • Perform time-series analysis for climate and environmental forecasting

  • Complete and present a domain-based GEOAI mini projec

Training Methodology

Each session is designed to be practical and balanced:

  • 30 minutes of conceptual learning

  • 60 minutes of live coding demonstration

  • 30 minutes of guided hands-on exercise and discussion

Training Modules

Day 1 – Python Introduction

Concepts

  • What is Python

  • Installation and environment setup

  • Jupyter Notebook basics

  • Syntax, comments

  • Print statements

  • Input / Output

Hands-on

  • Write first Python script for simple GIS-related calculations

Tools

  • Python, Jupyter

Day 2 – Variables, Data Types & Operators

Concepts

  • Variables

  • Data types: strings, integers, floats, booleans

  • Type conversion

  • Arithmetic operators

  • Comparison operator

Hands-on

  • Build unit converter and coordinate value calculators

Tools

  • Python

Day 3 – Data Structures

Concepts

  • Lists, Tuples, Sets, Dictionaries

  • Indexing and slicing

  • Updating values

  • Built-in method

Hands-on

  • Store parcel IDs, village names, and land details in structured collections

Tools

  • Python

Day 4 – Conditions & Loops

Concepts

  • if, elif, else

  • for loop

  • While loop, Nested Loops

  • break and continue

Hands-on

  • Filter records and iterate through GIS attribute data

Tools

  • Python

Day 5 – Functions & Error Handling

Concepts

  • Functions

  • Parameters and return values

  • Variable scope

  • Error handling (try-except)

  • Common programming error

Hands-on

  • Build reusable GIS utility functions with error handling

Tools

  • Python

Day 6 – Basic File Handling

Concepts

  • File paths and directories

  • File opening and closing

  • File modes

  • OS module basic

Hands-on

  • Create project folders and manage GIS file paths

Tools

  • Python, OS

Day 7 – Reading & Writing Files

  • Reading and writing TXT files

  • CSV file handling

  • JSON file handling

  • Append vs overwrite

  • Saving structured output

Hands-on

  • Read survey data and write cleaned outputs

Tools

  • Python, CSV, JSON

Day 8 – Pandas & GeoPandas

Concepts

  • DataFrames

  • Reading CSV/Excel files

  • Filtering rows and selecting columns

  • Creating GeoDataFrame from coordinate

Hands-on

  • Convert tabular location data into GeoDataFrame

Tools

  • Pandas, GeoPandas

Day 9 – Shapefile Reading & Writing

Concepts

  • Reading shapefiles

  • Understanding CRS

  • Attribute table inspection

  • Editing fields

  • Exporting to Shapefile / GeoJSO

Hands-on

  • Load shapefile, update attributes, save new output

Tools

  • GeoPandas, Fiona

Day 10 – Shapely for Geometry Operations

Concepts

  • Geometry types: Point, LineString, Polygon

  • Buffer operations

  • Intersection

  • Area and distance calculation

  • Geometry logic

Hands-on

  • Create geometries and perform buffer/intersection analysis

Tools

  • Shapely, Python

Day 11 – NumPy for Spatial Calculations

Concepts

  • Arrays

  • Indexing

  • Mathematical operations

  • Raster-like grid handling

Hands-on

  • Perform matrix-style spatial calculations

Tools

  • NumPy

Day 12 – Rasterio for Raster Data

  • Raster reading

  • Metadata handling

  • Bands

  • Raster writing

  • Band math

  • NDVI workflow

Hands-on

  • Read raster bands and compute NDVI

Tools

  • Rasterio, NumPy

Day 13 – Integrated GIS Python Workflow

Concepts

  • Combining:

    • Python basics

    • File handling

    • Pandas

    • Shapefiles

    • Geometry

    • Raster workflows​

Hands-on

  • Build end-to-end GIS automation script

Tools

  • Python, Pandas, GeoPandas, Rasterio

Day 14 – Revision & Mini Project

  • Revision of all Python + GIS topics

  • Workflow structuring

Hands-on

  • Complete a small Python-for-GIS project (input → processing → output)

Tools

  • Full Python GIS stack, Jupyter

Day 15 – Introduction to AI & Geospatial Foundations

  • AI vs ML vs Deep Learning

  • Supervised vs Unsupervised learning

  • Raster vs Vector

  • Spatial, temporal, spectral resolution

  • GEOAI domains

    • Tools: NumPy, Pandas, Matplotlib

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Day 16 – Geospatial Data Handling

Concepts

  • CRS, spatial metadata

  • Raster/vector workflows

  • GeoTIFF & shapefile processing

    • Tools: Rasterio, GeoPandas, Shapely

Day 17 – Data Preprocessing & Feature Engineering

  • Data cleaning

  • Missing values

  • Scaling (StandardScaler, MinMaxScaler)

  • EDA

Day 18 – Regression Models

Concepts

  • Linear Regression

  • Random Forest

  • Metrics (MAE, RMSE, R²)

    • Applications: Air quality, wind, temperature

Day 19 – Classification Models

Concepts

  • Logistic Regression

  • Random Forest Classifier

  • Confusion Matrix

    • Applications: LULC, wetlands, burn scars

Day 20 – Unsupervised Learning

Concepts

  • K-Means

  • DBSCAN

  • PCA

    • Applications: Heat island, pollution hotspots

Day 21 – Deep Learning for Remote Sensing

  • Neural networks

  • CNN

  • Image classification

    • Applications: Roads, buildings, disaster mapping

    • Tools: TensorFlow / Keras

Day 22 – Time Series & Spatio-Temporal Modelling

  • Time-series analysis

  • Anomaly detection

  • LSTM

    • Applications: Climate, drought, NDVI

Day 23 – Integrated GEOAI Applications

Concepts

  • Flood risk, Landslides, Wind energy, Wildfire

  • Air quality

  • Infrastructure risk

  • Focus: Model comparison, tuning

Day 24 – Model Deployment & Ethical GEOAI

  • ML pipeline

  • Model saving (.pkl, .h5)

  • Explainable AI

  • Ethic

Day 25 – Integrated Multi-Domain GEOAI Applications (Extended Practice)

Concepts

  • Flood risk mapping

  • Landslide susceptibility modelling

  • Wind energy suitability analysis

  • Wildfire risk assessment

  • Air quality forecasting

  • Infrastructure vulnerability mapping

Focus

  • Model application on real datasets

  • Workflow integration​

Day 26 – Model Improvement & Evaluation (From Day 9 Content)

Concepts

  • Hyperparameter tuning (only as mentioned in your syllabus)

  • Cross-validation

  • Model comparison

Tools

  • Scikit-learn

  • XGBoost (optional

Day 27 – End-to-End ML Pipeline Practice (From Day 10 Content)

Concepts

  • ML workflow execution

  • Model saving (.pkl / .h5)

  • Loading trained model

Focus

  • Joblib / Pickle

Day 28 – Explainable & Ethical GEOAI

Concepts

  • Feature importance

  • Explainable AI concepts

  • Ethical considerations in GeoAI

Focus

  • Model application on real datasets

  • Workflow integration

Day 29 – Mini Project Development

Concepts

  • Problem definition

  • Data preprocessing

  • Model building

Day 30 – Final Project & Presentation

Concepts

  • Model evaluation

  • Result interpretation

  • Final presentation

Tools and Software's

Participants will work with:

  • Python

  • NumPy

  • Pandas

  • Matplotlib

  • Scikit-learn

  • TensorFlow / Keras

  • Google Earth Engine

  • QGIS

  • Rasterio / GDAL

Application Areas

The course is designed around real-world geospatial use cases across:

  • Urban and Smart Cities

  • Disaster Management

  • Climate and Environmental Monitoring

  • Hydrology

  • Renewable Energy

  • Biodiversity Monitoring

  • Coastal Studies

  • Infrastructure Mapping

Assessment and Certification

Graduation Cap Display

Participants will complete a domain-based mini project combining satellite data and machine learning methods. Evaluation will consider methodology, model performance, interpretation, and presentation. Certificates will be awarded upon successful completion of the program.

Join the GEOAI Program and build future-ready geospatial AI skills.
Learn how to apply machine learning, deep learning, and geospatial analysis to solve real-world problems across environment, urban systems, climate, and infrastructure.
Enroll now to strengthen your career in the emerging field of GEOAI.

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