NDVI — Are there other alternatives to measure vegetation? — You have heard about Normalized Difference Vegetation Index (NDVI). What alternative indices can you use for your Earth Observation analysis? The normalized Difference Vegetation Index (NDVI) is a common and popular index used in Remote Sensing to measure the greenness and health of vegetation. It is a simple indicator that…

Find out Packages and Visualise their connections/dependencies. — Geospatial & Earth Observation Python Ecosystem has matured over the years. Therefore, it isn't easy to track all the packages — old and new. Thanks to the latest Python OS Ecosystem for GIS and Earth Observation Website, you can now list out, visualise and explore python packages for GIS and…

Ready to use web applications — learn from their codebase — Many data scientists have adopted Streamlit to create web applications quickly with Python. Back in 2019, I remember how happy I was to discover how fast I could develop web applications with Streamlit. I even wrote back then a tutorial with the subheading "Streamlit revolutionises and makes deploying web apps…

Walkthrough with the API and simple Streamlit Application. — As a geospatial data scientist, you need to know how to work with APIs and retrieve data from a remote server, especially when the data changes. …

Google Releases global 10m Resolution Land Cover Data — Land cover data helps governments, researchers, and geospatial practitioners better understand the earth and its transformations. In the past, we were happy to have yearly land cover data updates for an area of interest. …

With Gradio, Hugging Face and Fastai — After you develop an application, you need to demonstrate your model. The first step is to serve it locally on your computer. However, sharing the world with your application requires deploying and hosting on a server. Developing web applications in Python is getting a lot of traction thanks to the…

Tools, Techniques and datasets — Geospatial Data Science is evolving, and I try to keep abreast of the development trends. I have written and explored fewer of these developments in the last two months in this publication due to some other project engagements. …

Install hustle free geospatial libraries with one line of code in Anaconda. — I have had my share of frustration installing geospatial packages. The same is also true for my students. Mismatching dependency libraries can sometimes be challenging. Lately, I have had a smooth transition into Installing packages through Conda and with almost no failure. …

Ingest, process, and Visualize 3D Point Cloud Data in Python — Working with 3D point cloud data is challenging. The collection of the points in one Lidar image is usually extensive and might reach millions of points depending on the image size. Handling the height dimension also poses other complexities. A point cloud is a set of data points in space…