A look back at our Checks-by-Monitoring process in 2021 — Written by Matic Lubej and Teo Cerovski. 2021 is over, summer of 2022 is here, and we are just launching a second run of the automated and continuous Checks-by-Monitoring (CbM) process with the Slovenian Paying Agency (PA). Last year we did this for the first time, where we set in…

When an object-based approach is not good enough — This is a multi-part series about machine learning and EO data supporting Common Agriculture Policy. Find information about related blog posts at the bottom. The detection of mowing events is one of the most important aspects of area monitoring and can represent the majority of agricultural activity in countries with…

A detailed guide covering various examples on making animations from satellite imagery — No matter what you do in life, there is one thing connecting us all — communication. To understand each other, we must know how to convey a message to the person (or an audience) in front of us, which points to emphasise, and which details to leave out. …

Observing the Analemma through Satellite Imagery — Shadows play a big role in our lives. Just think about it. We cooled ourselves down in shades when playing outside as kids, they are used in stories/movies as a depiction of evil, and surely we all played with our hands in a session of shadow puppetry? How about Plato’s…

A Python implementation for the curious kind It always amazes me how lazy human beings can get. About 30 seconds into solving this puzzle, relying only on using my brain, I started thinking about how we could implement a search algorithm for finding the (or a) solution. …

Pushing Beyond the Point of “Good Enough” — Foreword These past few weeks must have been quite hard on you. We published the first and the second part of the land cover classification on the country scale using eo-learn. eo-learn is the open-source package for bridging the gap between Earth Observation (EO) and Machine Learning (ML), but in the…

Going from Data to Predictions in the Comfort of Your Laptop — Foreword The second part of the blog series on land use and land cover classification with eo-learn is out! This part picks up from the first one, where we presented a basic approach towards the following: Splitting the area of interest (AOI) into EOPatches Obtaining Sentinel-2 image data and cloud masks …

Mastering Satellite Data in an Open-Source Python Environment — Foreword About a half a year ago the very first commit was pushed to the eo-learn GitHub page. Today, eo-learn has grown into a remarkable piece of open-source software, ready to be put to use by anyone who is curious about EO data. Even the members of the EO-research team here…