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Ujaval Gandhi Profile picture
Ujaval Gandhi
@spatialthoughts
Educator | Founder @ Spatial Thoughts | Helping people learn and master geospatial tech - #QGIS, #Python, #GDAL, #EarthEngine | Ex-Google
Sep 1 • 5 tweets • 3 min read
Exciting news for #QGIS users! The "Google Earth Engine Plugin for QGIS" is now updated with new no-code tools that allow you to download and use #EarthEngine datasets in QGIS easily. Check out my newly contributed tutorials for the latest plugin (1/n) 👇 Image
Image Tutorial 1: Downloading Images from Earth Engine: A tutorial showing how to use the Google Earth Engine plugin to create a Sentinel-2 median composite for a region and download it as a GeoTIFF file. (2/n) gee-community.github.io/qgis-earthengi…Image
Mar 14, 2024 • 10 tweets • 7 min read
Want to learn #Python for #Geospatial Analysis? We just launched our "Python Foundation for Spatial Analysis" course on YouTube - completely free and optimized for self-study. Check out the playlist at (1/n) youtube.com/playlist?list=…
Image The course starts with Python fundamentals and covers fundamentals of Jupyter notebooks, variables, data structures, string operations, for-loops, conditionals, and functions - all while focusing on problems in the geospatial domain. (2/n)



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Dec 12, 2023 • 4 tweets • 2 min read
Never thought I would see this day! NRSC has implemented ISRO's India Space Policy 2023 and started releasing 5m resolution ResourceSat2 LISS4 imagery as open data. You can sign up and download data from NRSC's Bhoonidhi Portal (1/n) bhoonidhi.nrsc.gov.in/bhoonidhi/inde…
Image LISS4 sensor has 3 bands (NIR/Red/Green) at 5m GSD. The download comes with 3 GeoTIFFs and a metadata file. The pixels are DNs that need to be converted to reflectances. After conversion - there's a close match to Sentinel-2. You can see the improvement in resolution (2/n)
Dec 11, 2023 • 7 tweets • 3 min read
I conducted a workshop on Monitoring Land Use Land Cover Changes with Google #EarthEngine and #DynamicWorld at #InGARSS2023. Sharing the full workshop material that has some new monitoring examples with JS and #Python code. See the thread below for details and explanation 👇(1/n) Image The workshop focused on understanding and using the #DynamicWorld dataset for monitoring applications. The key takeaway is that the probability bands provide a way to build monitoring applications with simple rule-based models at the global scale at high temporal frequency. (2/n) Image
Jun 11, 2023 • 10 tweets • 6 min read
Releasing a new Google #EarthEngine workshop titled "Creating Publication Quality Charts with GEE" with completely open materials and code. A structured guide to help you create beautiful and informative visualizations from climate and earth observation datasets. A thread (1/n)👇


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Image The workshop starts with an introduction to the GEE charting API. Module 1 covers Time-Series charts with a focus on learning the API and customization options. We work with Weather Forecast (GFS), Climate (TerraClimate), Precipitation (CHIRPS), and MODIS datasets. (2/n)


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Mar 25, 2023 • 4 tweets • 4 min read
Want to improve your #Python geospatial skills? Check out my new video tutorial series covering spatial data analysis and visualization with #Pandas, #GeoPandas, #XArray, #Dask, #STAC #OpenRouteService API and more. Many more in the pipeline. I'll post these in the thread below👇 Tutorial 1: Spatial Queries using #GeoPandas: This tutorial shows you how to use select points from a layer within a certain distance from features in another layer using GeoPandas.
Sep 23, 2022 • 7 tweets • 8 min read
Happy to announce my new course Mapping and Data Visualization with #Python. This has been in the making for over a year and excited to be able to share it with the world! The full course is free for self-study and shared under an open license. An overview thread below 👇 1/n We start with the overview of the Python data visualization landscape and zero in on the core libraries for mapping. Day 1 covers vector data visualization with #Maptplotlib, #Pandas, #GeoPandas, and #Contextily (2/n)
Jun 10, 2022 • 12 tweets • 6 min read
A thread about the newly launched #DynamicWorld landcover dataset by #Google. I had early access and explored this dataset in detail. You may be very excited about this dataset, but likely for the wrong reasons. Sharing some insights, potential use cases, and pitfalls. 1/n First of all - what is it? It's a Landcover dataset based on Sentinel-2 data - but with a key difference. Rather than a static snapshot, it is a time series. *Every* Sentinel-2 scene is classified with class probabilities for 9 landcover classes. 2/n