🎓 Course 1: Machine Learning for GIS Applications

🎓 Course 1: Machine Learning for GIS Applications

Code: 4014Soft-GIS-CIV

🧩 Level: Intermediate – Advanced
⏱ Duration: 30 Hours
👥 Target Audience: GIS analysts, remote sensing professionals, data scientists, environmental researchers

📘 Introduction

This course provides a comprehensive exploration of how Machine Learning (ML) techniques can be integrated with Geographic Information Systems (GIS) to analyze, model, and extract insights from spatial data. From basic spatial data handling to advanced deep learning on satellite imagery, this course bridges the gap between geospatial science and artificial intelligence.

🧾 Course Description

As GIS data becomes increasingly complex and voluminous, traditional analysis methods fall short. This course empowers professionals with modern ML tools and techniques tailored for geospatial data. You’ll learn to build complete ML pipelines for spatial classification, prediction, and object detection using Python-based tools and libraries. Whether you’re planning urban zones, detecting land cover changes, or mapping environmental hazards, this course equips you to automate and enhance your GIS analysis through ML.

Course outlines:

📍 Module 1: Introduction to GIS and Machine Learning (3h)

• 🌍 What is GIS? Overview and key components

• 🗺️ Basics of spatial data: raster vs vector

• 🤖 Introduction to Machine Learning (ML)

• 📈 Applications of ML in GIS

🧹 Module 2: Spatial Data Preparation (4h)

• 📂 Working with shapefiles & GeoTIFFs

• 🌐 Coordinate Reference Systems (CRS)

• 🧼 Data cleaning, scaling, feature extraction

• 🏷️ Labeling for classification/regression

✅ Module 3: Supervised Learning for GIS (6h)

• 📊 Algorithms: SVM, Random Forest, k-NN

• 📉 Regression for spatial prediction

• 🛰️ Case Study: LULC classification via satellite imagery

• 🧪 Accuracy assessment, confusion matrix

🧭 Module 4: Unsupervised Learning & Clustering (4h)

• 🔍 K-Means & DBSCAN for region clustering

• 🔥 Hotspot detection

• 🏙️ Use cases: urban planning, crime mapping

🧠 Module 5: Deep Learning for Geospatial Analysis (6h)

• 🧬 CNNs for image classification

• 🧯 U-Net for semantic segmentation

• 🎯 Object detection: YOLO, Faster R-CNN

• 🏗️ Case Study: Building footprint extraction

🛠️ Module 6: GIS Tools and Libraries in Python (4h)

• 🐼 GeoPandas, Rasterio, Fiona, Scikit-learn

• 🧭 Integration with QGIS / ArcGIS

• 🤖 Automate spatial tasks using Python

🚀 Module 7: Project & Deployment (3h)

• 🧱 Build an end-to-end ML GIS pipeline

• 🌐 Web deployment using Folium / Kepler.gl

• 📝 Document and present project outcomes

• Tools You’ll Need:

  • Python 3.8+ with libraries: GeoPandas, Rasterio, scikit-learn, matplotlib, etc.

  • GIS software (preferably QGIS, optionally ArcGIS Pro)

  • Jupyter Notebook or Google Colab for exercises

  • Internet access to download datasets and documentation