Satellite Image Processing With Google Earth Engine & Python Training Course

Introduction

The Satellite Image Processing with Google Earth Engine & Python training course offers an in-depth understanding of satellite image processing techniques, using powerful tools like Google Earth Engine and Python programming. This course provides professionals with the skills needed to analyze, process, and interpret satellite imagery for various environmental and geospatial applications. Participants will learn how to harness the capabilities of cloud computing and Python-based data analysis to handle large-scale satellite data for applications such as land cover change detection, vegetation analysis, disaster monitoring, and climate studies.

Google Earth Engine, a cloud-based platform for planetary-scale environmental data analysis, will be combined with Python, one of the most versatile and widely-used programming languages, to enable participants to perform efficient satellite image analysis and automate geospatial data processing workflows.

Target Audience

This training course is designed for professionals and practitioners in fields related to geography, environmental science, remote sensing, GIS (Geographical Information Systems), and data analysis. It is ideal for:

• Geospatial analysts and GIS professionals
• Environmental scientists and ecologists
• Remote sensing specialists
• Researchers in climate change, land use, and urban planning
• Data scientists and engineers working in the geospatial industry
• Academics and students specializing in remote sensing, geospatial technologies, or environmental sciences

Course Objectives

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

• Understand Satellite Image Processing Concepts
  Gain a solid foundation in the principles and methods used in processing satellite imagery and extracting valuable data.
• Leverage Google Earth Engine for Data Analysis
  Learn how to use Google Earth Engine’s cloud-based platform to access, process, and analyze satellite images on a global scale.
• Perform Image Preprocessing
  Master techniques for correcting, enhancing, and preparing satellite images for analysis, including atmospheric correction, geometric correction, and resampling.
• Work with Python for Geospatial Analysis
  Develop Python programming skills for processing, analyzing, and visualizing satellite imagery, with an emphasis on using libraries like NumPy, Matplotlib, and Rasterio.
• Analyze Land Use and Land Cover Changes
  Use satellite data to monitor and analyze land use changes over time, including urban expansion, deforestation, and agriculture.
• Utilize Remote Sensing for Environmental Monitoring
  Apply satellite imagery to monitor environmental changes, such as vegetation health, water bodies, and forest cover.
• Automate Data Processing Workflows
  Learn how to automate satellite image processing tasks using Python scripts, significantly improving workflow efficiency and handling large datasets.
• Perform Time-Series Analysis
  Develop skills to analyze temporal changes in satellite imagery, enabling the detection of trends, patterns, and anomalies over time.
• Understand Advanced Image Processing Techniques
  Explore advanced satellite image processing methods such as classification, object detection, and change detection.
• Apply Satellite Image Data to Real-World Applications
  Gain practical experience in applying satellite image processing skills to real-world projects, including environmental monitoring, disaster management, and urban planning.

Through hands-on training, practical assignments, and case studies, this course equips participants with the tools necessary to process and analyze satellite data for a wide range of applications in research and industry. Whether you're looking to enhance your career in remote sensing, GIS, or environmental monitoring, this course offers the expertise you need to succeed in the field of satellite image processing.

Duration

10 Days

Course content

Introduction to Remote Sensing and Satellite Image Processing

• Overview of remote sensing and its applications in geospatial analysis.
• Introduction to various types of satellite imagery and sensors.
• Understanding spatial and temporal resolution in satellite data.

Introduction to Google Earth Engine (GEE)

• Overview of Google Earth Engine (GEE) platform and its capabilities.
• Navigating the GEE user interface.
• Accessing and importing satellite data from the Earth Engine database.

Satellite Image Data Formats and Preprocessing

• Understanding image data formats: Raster, GeoTIFF, HDF5, etc.
• Image preprocessing techniques: calibration, georeferencing, and resampling.
• Handling missing data and cloud cover in satellite imagery.

Basics of Python for Remote Sensing and Geospatial Analysis

• Introduction to Python programming for geospatial analysis.
• Working with essential Python libraries: NumPy, Pandas, Matplotlib, and Rasterio.
• Automating image processing tasks using Python.

Exploring Google Earth Engine API with Python

• Setting up Google Earth Engine API in Python.
• Basic functions for querying and filtering satellite image datasets.
• Connecting Python with Google Earth Engine for efficient data processing.

Geometric and Radiometric Image Corrections

• Techniques for geometric corrections to align satellite images.
• Radiometric corrections to standardize brightness values and atmospheric conditions.
• Converting raw satellite images into meaningful geospatial data.

Time-Series Analysis and Change Detection

• Working with temporal data to detect land cover changes.
• Implementing time-series analysis to assess long-term environmental trends.
• Techniques for change detection in satellite images.

Image Classification Techniques

• Introduction to supervised and unsupervised classification methods.
• Classifying satellite images using machine learning algorithms.
• Creating land use/land cover classification maps.

Vegetation Analysis and NDVI Calculation

• Understanding vegetation indices such as NDVI (Normalized Difference Vegetation Index).
• Analyzing vegetation health and monitoring agricultural areas.
• Extracting vegetation data from satellite imagery.

Water Bodies and Hydrological Analysis

• Identifying and mapping water bodies in satellite images.
• Monitoring changes in water bodies and wetlands.
• Assessing water resources using satellite-based remote sensing data.

Urbanization and Land Use Change Analysis

• Mapping urban expansion using high-resolution satellite images.
• Analyzing the impact of land use changes on the environment.
• Monitoring deforestation, construction, and agricultural conversion.

Advanced Image Processing Techniques

• Exploring advanced techniques such as object-based image analysis.
• Working with remote sensing data fusion techniques.
• Applying advanced filtering and enhancement methods to improve image quality.

Automation of Satellite Image Processing with Python Scripts

• Writing Python scripts to automate the processing of large volumes of satellite data.
• Using batch processing techniques for repetitive tasks.
• Optimizing processing workflows for efficiency.

Integrating Satellite Data with Other Geospatial Data

• Combining satellite imagery with vector data (e.g., shapefiles, GeoJSON).
• Analyzing satellite data alongside GIS layers (e.g., roads, population density).
• Creating interactive maps with Python and geospatial libraries.

Practical Applications and Real-World Case Studies

• Applying satellite image processing to environmental monitoring projects.
• Working on case studies, including disaster management, urban planning, and climate change monitoring.
• Developing a final project utilizing Google Earth Engine and Python for satellite image analysis.

By the end of this course, participants will have hands-on experience with satellite image processing, data analysis, and automation using Google Earth Engine and Python, enabling them to work on complex remote sensing projects with real-world applications in various industries.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.