In the field of remote sensing you often have to present classification results as maps and need a nice, unobtrusive background that gives the viewer an idea where everything is located without being as distracting as a RGB satellite image. A nice choice for this is a grey hillshading or slope relief background that adds some texture information to your map by using a Digital Elevation Model.

Sometimes you are struck by an idea how to analyze satellite images, but how fast can you get from a remote sensing idea to a prototype of the result? The sheer size and amount of openly available satellite images makes developing prototypes rather cumbersome. To get from an idea to a prototype product you need to download gigabytes of data, pre-process it, write the code that calculates your prototype product and save it somewhere. Oftentimes you’ll end up spending more time on downloading and pre-processing than the actual prototyping and calculation. More often than not I discarded ideas because I had no time to test them. Today I’ll show an example of how Google’s Earth Engine can be used to put the rapid back into rapid prototyping for remote sensing.

The Sentinel satellites are an amazing opportunity for scientists all over the world to explore unprecedented amounts of remote sensing data free of charge. I am genuinely happy that this is one of the first large remote sensing missions that has Open Data and Open Access baked in right from the start. All Sentinel data can be accessed through Copernicus Open Access Hub. Searching and downloading of multiple scenes however is not very user friendly. Fortunately the Hub also provides an API we can use to search and download multiple scenes at once. This post aims to show a simple workflow of searching and downloading multiple Sentinel-2 scenes using the Python package sentinelsat

When you are classifying pixels in satellite images you often encounter the dreaded artefact commonly referred to as Donut. A good example is the classification of lakes where you are not able to correctly classify the complete shoreline (resulting in non-closed circles or Open Donuts) and remaining pixels in the wrong class which make up the Hole inside the Donut. Recently a question popped up on GIS.Stackexchange on How to transform raster donuts to circles This problem can be solved with the use of mathematical morphology.