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=…
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)

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…
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)

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) 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)

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)👇





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)

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.

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)

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

https://twitter.com/rebeccatmoore/status/1534982514132996096?s=20&t=Mkc-LFfX-nrr2FaQ7Xj-tQ