Cloud Native Remote Sensing with Python (June 2026)

About this course
When
Jun 23–Jul 2, 2026
Duration
18 hours 6 sessions of 3 hours
Format
Online Live, instructor-led
Fee
US$175
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. 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.

Schedule

All times are India Standard Time (IST, GMT+5:30). Use the local time link on each session to see it in your time zone.

Week 1
  1. Session 1 Tue, Jun 23 6 pm–9 pm IST Check local timeLocal time
  2. Session 2 Wed, Jun 24 6 pm–9 pm IST Check local timeLocal time
  3. Session 3 Thu, Jun 25 6 pm–9 pm IST Check local timeLocal time
Week 2
  1. Session 4 Tue, Jun 30 6 pm–9 pm IST Check local timeLocal time
  2. Session 5 Wed, Jul 1 6 pm–9 pm IST Check local timeLocal time
  3. Session 6 Thu, Jul 2 6 pm–9 pm IST Check local timeLocal time

Course outline

  1. Module 1

    Cloud Native Geospatial Fundamentals

    • Introduction and Course Overview
    • Introduction to Colab
    • Introduction to GeoLibre
    • Cloud Native Geospatial Tools
    • (XArray , STAC, Dask, DuckDB)
    • Creating a Median Composite
  2. Assignment 1
    Create a Landsat Composite
  3. Module 2

    Remote Sensing Fundamentals

    • Remote Sensing Fundamentals
    • Calculating Spectral Indices
    • Masking Clouds
    • Extracting and Processing Time-Series
  4. Assignment 2
    Extract a Temperature Time-Series
  5. Module 3

    Computation and Data Processing

    • Computation and Data Processing
    • Working with Landcover Data
    • Analyzing Landcover Change
    • Computing Zonal Statistics
    • Interoperability with Google Earth Engine
  6. Module 4

    Machine Learning and AI

    • Machine Learning and AI
    • Preparing Data for Machine Learning
    • Unsupervised Clustering
    • Supervised Classification
    • Supervised Classification with Embeddings
  7. Module 5

    Computation Environments

    • Running Computation on Your Hardware
    • Using Google Cloud Runtime
    • Scaling Analysis in Cloud with Coiled

This class has finished. See upcoming classes.

What participants say

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I am very happy to have taken the Cloud Native Remote Sensing with Python course with Ujaval. This is the sixth course I have taken with him, and I will definitely continue learning from him. In my opinion, he is the best educator in the geospatial technology field. He is also an excellent person: kind, approachable, and always willing to help and answer questions, even after the courses have ended. I highly recommend this course and Ujaval as an instructor.
Joaquín Urruti
GIS & Data Consultant
Argentina Cloud Native Remote Sensing with Python
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A big thank you to Ujaval Gandhi and The Spatial Thoughts team for delivering such an exceptionally well-structured course! The clear, hands-on approach made transitioning into cloud-native Python, Remote Sensing, and GeoAI workflows seamless and truly enjoyable.
Dr Barnali Basu
Independent Consultant
United Kingdom Cloud Native Remote Sensing with Python
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Before this training on the cloud-native remote sensing with Python, I wasted much time on data preparation and repeatedly hit computational limits. I jumped between data sources—sometimes altering my final analysis simply because I missed the right dataset. This course didn’t just teach new tools—it shifted my entire perspective on remote sensing and Python. Ujaval Gandhi explains concepts in a way that makes the field click for me. More importantly, it taught me not to accept analysis results at face value—I now understand the datasets and can meaningfully interpret the outputs. These days, with AI simplifying coding, anyone can run code. But this training goes beyond running code—it’s about true understanding. The hands-on delivery is superb: exercises are ready to run during and after each session, and the organized records make review effortless. I gained immediately applicable skills in cloud-native remote sensing with Python: cloud-optimized data formats, Xarray, STAC, Dask, DuckDB, and scalable cloud workflows. The course exceeded my expectations—packed with helpful tips and supplemental materials that genuinely strengthened my skills. Unlike other training programs, this one doesn’t require hunting for resources—it’s built around current tools and real data you can apply to your projects right away. If you’re waiting for the ‘perfect time’—stop’. Spatial Thoughts: Ujaval Gandhi and “Vigna Purohit” are the best trainers for today’s geospatial tech landscape.
Baye Terefe Getahun
P.hD Student, Henan University
China Cloud Native Remote Sensing with Python

Read all 7 reviews