This cohort is now full. Sign-up on the Waitlist for our next cohort in November 2026.
This is an intermediate-level course that covers tools and techniques for working with climate and earth observation datasets using a modern cloud-native approach. With the growing ecosystem of cloud native data formats, open data catalogs and powerful open-source packages – remote sensing practitioners can build open and vendor-agnostic cloud-based data processing workflows.
This class provides a structured introduction to Python-based tooling (XArray, DuckDB, STAC, and Dask) for cloud-native workflows with best practices and hands-on examples.
Prerequisites
- Familiarity with remote sensing concepts
- Good working knowledge of Python (Python Foundation for Spatial Analysis or equivalent)
Course Outline
- Course Pre-work (Self-study)
- Introduction to Remote Sensing
- Module 1: Cloud Native Geospatial Fundamentals
- XArray Basics
- STAC and Dask Basics
- DuckDB Basics
- Creating a Median Composite
- Module 2: Remote Sensing Fundamentals
- Calculating Spectral Indices
- Masking Clouds
- Extracting and Processing Time-Series
- Module 3: Computation and Data Processing
- Working with Landcover Data
- Analyzing Landcover Change
- Computing Zonal Statistics
- Interoperability with Google Earth Engine
- Module 4: Machine Learning and AI
- Preparing Data for Machine Learning
- Unsupervised Classification
- Collecting Training Samples
- Supervised Classification
- Supervised Classification with Embeddings
- Module 5: Computation Environments
- Running Computation on Your Hardware
- Using Google Cloud Runtime
- Scaling Analysis in Cloud with Coiled
- Assignments
- Assignment 1: Create a Landsat Composite
- Assignment 2: Extract a Temperature Time-Series
Learn more about the course content on the Course Homepage.
Cost
The course fees are USD $175 / INR ₹12500+GST.
Student discounts available [Learn more]
Schedule
The course shall be held as a live online interactive class offered in 6 sessions of 3 hours each over two weeks. The classes will be conducted over Zoom.
Below is the schedule for live sessions. Please verify the local times before registering.
- Tuesday August 4, 2026: 6pm-9pm IST (3 hours) – Check Local time
- Wednesday August 5, 2026: 6pm-9pm IST (3 hours) – Check Local time
- Thursday August 6, 2026: 6pm-9pm IST (3 hours) – Check Local time
- Tuesday August 11, 2026: 6pm-9pm IST (3 hours) – Check Local time
- Wednesday August 12, 2026: 6pm-9pm IST (3 hours) – Check Local time
- Thursday August 13, 2026: 6pm-9pm IST (3 hours) – Check Local time
It is recommended to attend the sessions live for the optimal experience. We also record the live sessions and make them available to registered participants immediately after each day. If you do miss a session – you will be able to catch up using the recorded videos.
Register
Please use the appropriate forms below to book a seat. You must complete the online payment to confirm your spot. We accept all major credit/debit cards from over 150 countries. Indian residents have an option to pay via UPI also.
In case your plans change, you can cancel and obtain a full refund. See our cancellation policy
This is an intermediate-level course and assumes good working knowledge of Python. If you are new to programming – please join our Python Foundation for Spatial Analysis course instead.
Registration for Non-Indian Residents
Registration for Indian Residents
A GST charge of 18% will be applied to the course fees upon checkout.
Course Reviews
Once again, Ujaval’s instruction has increased my confidence to leverage the world’s cutting edge technologies and methodologies. This time is special, in that the workflows learned are independent of any platform and are scalable across environments! I am excited to continue down this path and now see how AI can open the doors to many more opportunities than I considered attainable to me before taking this course.
Zach Torres, Sr. Manager, Strategic Operations & Analytics, World Vision USA.
The Cloud Native Remote Sensing with Python course was practical, well structured, and highly relevant to modern geospatial workflows. It significantly strengthened my geocomputing skills by introducing scalable approaches for processing large Earth observation datasets using cloud-native technologies such as STAC, Dask, Xarray, and Cloud Optimized GeoTIFFs. The hands-on exercises made the concepts easy to apply, the instructor explained complex topics clearly, and the TA support was excellent throughout. I highly recommend this course to anyone looking to advance their Python, remote sensing, and geocomputing skills.
Abdillahi Osman Omar, PhD Researcher in GeoAI, University of West London (UWL).