Online
Oct 20 - Dec 22, 2020
15:00 - 17:00 IST
Instructors: Vasily Lobanov, Teklay Tesfazghi, Javad Hatami
Training Goal: To Introduce you the fundamental concepts of Earth remote sensing science and Python as a tool for the image analysis, including Big Data workflows
Practical Outcomes: During this highly-practical course you'll get your hands on:
~ Data from the NASA’s and ESA Earth observation Programmes (satellites: Landsat-8, Sentinel-1, 2 and 5P)
~ Python — a high-level general-purpose programing language widely used in ERS and other geospatial domains
~ Google Earth Engine — a cloud-based platform for big geodata processing and analysis, on top of which you will build your custom web application
Course Structure:
Part 1:
FUNDAMENTALS OF ERS: Learn the ways of collecting information about our planet with satellites, as well as modern applications of this information. You will get familiar with physical basics of Earth Remote Sensing (ERS), satellites and sensors, characteristics of images and see how it helps us face environmental, social and economic challenges.
Part 2:
PYHTON FOR IMAGE ANALYSIS: Get familiar with Python as a tool for geospatial image analysis. You will have a quick recap of Python basics followed by a specific workflows and libraries that are used for geospatial analysis. Practical part includes working in Jupyter / Colab notebooks.
Part 3:
EARTH ENGINE PYHTON API: Hone skills in working with a multi-petabyte catalog of big satellite image data and try yourself as a data analyst. You will develop your own algorithms to detect changes, map trends, and quantify differences on the Earth's surface, and then publish them as web-based applications.
P1: Remote Sensing -
The aim of the module is to provide the basis for understanding of the remote sensing of the Earth. Satellites are an important tool for the geosciences and innumerable practical applications. We have tried to homogenize the terminology the principles of interpretation and the general approach in this course. Basics of Remote Sensing (RS) Types of Satellite Sensors and platform RS Satellite Characteristics RS Data Processing Levels Digital image principles etc …
P2: Python for Image Analysis -
This module gives students an understanding of remote sensing image analysis using python programming language. Some of the contents that will be discussed in this module are: A quick introduction to Python Getting familiar with Python 3.x as a tool for geospatial image analysis Working on Libraries/environments that enable local and Cloud-based image analysis using python (working in Jupyter / Colab notebooks)
P3: Earth Engine Platform -
EARTH ENGINE PYHTON API: This module gives students an understanding of the techniques of processing large scale ERS data in the Earth Engine environment, with an emphasis on being able to apply those techniques to future analysis. Students will be able to carry out typical workflows in data processing and product generation. Covered topics: Introduction to GEE; Platform Components; Data Structures; Basic Algorithms; Importing and Exporting Data; Charting; Time-Series Analysis; Classification;
Where: This training will take place online. The instructors will provide you with the information you will need to connect to this meeting.
When: Oct 20 - Dec 22, 2020.
Requirements: You must have a laptop or a desktop computer with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).
Accessibility:
MS Teams is our general environment for the online course
~ video-lectures, tests & assignments, class materials
~ use your @rudn.ru account
Colab is our lab environment
~ Labs and Homework
~ use your @gmail.com account
Earth Engine is our Big Data environment
~ Labs and Homework
~ use your @gmail.com account
~ REQUEST ACCESS TODAY: Sign up for EarthEngine
| Week | Date | Time (GMT+5:30) | Module | Topics | Classwork/Homework | Course Materials | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Tue-Oct,20 | 15:00 - 17:00 IST | M0L1 Meeting Lecturers and Course Introduction | Training Goal,About RUDN University and Lecturers,Course Structure- Part 1: Remote Sensing,Part 2: Python for image analysis,Part 3: Earth Engine Python API,Online Classroom Environment,Grading | Presentation | ||||||
| 15:00 - 17:00 IST | M1L1 Basics of remote sensing | Introduction, Remote sensing for Earth observation, Remote Sensing Workflow, Electromagnetic Energy, Viewing Parameters, Sensor Parameters, Spatial Resolution, Spectral Resolution, Radiometric resolution, Temporal Resolution, Imaging Sensors, Create Google and Earth Engine accounts | Presentation | ||||||||
| 2 | Tue-Oct,27 | 15:00 - 17:00 IST | M2L1 Data Acquisition and Applications | Platforms and missions, Types of Multispectral Imaging Sensor Systems, Satellite Characteristics, Aerial Photography, Satellite Imagery (Cameras, Scanners), Active sensors, Passive sensors, Various examples of sensors and applications | Presentation | ||||||
| 3 | Tue-Nov,03 | 15:00 - 17:00 IST | M3L1 Image principles | Properties of digital image, Data formats Types of Digital Images, Tri-stimuli model Color spaces, Image display (FCC,Origins and nature of digital image), components of image processing system, Visual perception, Basics of pixels, bands, and sampling" | Presentation | ||||||
| 15:00 - 17:00 IST | M3L2 Intro to Python | Introduction to python programming language | Presentation | ||||||||
| 4 | Tue-Nov,10 | 15:00 - 17:00 IST | M4L1 Raster processing | 5Raster processing using Python Tools. Python computing environments: Conda, Jupyter notebooks, Colab and other Python tools for satellite data acquisition and data storage | Presentation | ||||||
| 5 | Tue-Nov,17 | 15:00 - 17:00 IST | M5L1 Intro to Earth Engine platform | Big Data Platforms for Remote Sensing,Earth Engine Platform, EE Primitives,Earth Engine API | Presentation | ||||||
| 6 | Tue-Nov,24 | 15:00 - 17:00 IST | M6L1 Image Interpretation. | Quantitative Analysis of ERS data, Types of Pictorial Data Products, Image interpretation strategy: Levels of Interpretation Keys. Process of Image Interpretation, Interpretation of Aerial Photo, General procedure for photo interpretation | Presentation | ||||||
| 15:00 - 17:00 IST | M6L2 Spectral indices | Vegetation indices, Soil Indices, Water indices | Presentation | ||||||||
| 7 | Tue-Dec,01 | 15:00 - 17:00 IST | M7L1 Image corrections (preprocessing) | Image corrections through geometric / radiometric transforms | Presentation | ||||||
| 15:00 - 17:00 IST | M7L2 Image enhancement | Gray level transform, histogram processing, arithmetic/logic operations, spatial filtering Exploratory Image analysis Using Python | Presentation Lab | ||||||||
| 8 | Tue-Dec,08 | 15:00 - 17:00 IST | M8L1 Supervised Classification | Minimum Distance to Mean, Maximum Likelihood, Hypercube, Mahanolobis distance | Presentation | ||||||
| 15:00 - 17:00 IST | M8L2 Unsupervised Classification | K- Mean Clustering, ISODATA clustering, Hierarchical Clustering | Lab | ||||||||
| 9 | Tue-Dec,15 | 15:00 - 17:00 IST | M9L1 Soft Classification Techniques | Hybrid classification, Knowledge Engineer, Fuzzy Classification, Object Oriented Classification,Segmentation | Presentation | ||||||
| 15:00 - 17:00 IST | M9L2 Validation of Image Classification | Kappa Accuracy, Overall Accuracy, Regression | Presentation Lab | ||||||||
| 10 | Tue-Dec,22 | 15:00 - 17:00 IST | Final Exam Discussion | ||||||||
| Additional External Materials | |||||||||||
| Youtube Links | |||||||||||
| Website Links | |||||||||||
If you haven't used MsTeams before, go to the official website to download and install the MsTeams client for your computer.
In this course, most of the time, you will be learning by "coding along" with the Instructors. To do this, you will need to have both the window for the tool you will be learning about (a terminal, your web browser, etc..) and the window for the MsTeams video conference client open. In order to see both at once, we recommend using one of the following set up options: