Since April this year I tweet irregularly about Google Earth Engine management tips. This blogpost unites all tweets on this topic.
I wrote a little Earth Engine App where I compared the data availability of Sentinel-1C (Top of Atmosphere) and Sentinel-2A (Surface Reflectance) within the EE data catalog. This blogpost is about the code behind the App.
Earth Engine Apps - Dynamic, publicly accessible user interfaces for Earth Engine analyses. This blogpost lists every featured Earth Engine App as well as all App Galleries.
TL;DR Europe’s air pollution decreases at weekends and rises again during the working week. The Netherlands is the country with the highest proportion of Nitrogen Dioxide (NO2) in the air - during working days. A Car-Free-Day 2018 in Spain and Portugal did not show a significant improvement in NO2 values compared to other “normal” Saturdays.
TL;DR Microsoft Bing Maps image metadata can be obtained via the Bing Maps API. Microsoft returns the image recording date as Vintage Start/End. Start and End are sometimes several years apart. VintageEnd is rarely the exact image recording date!
Berlin and Venice are exactly 786 kilometres apart. It turns out, the Sentinel-2 satellites are just as far from the Earth, 786 km! It’s incredible when you consider that, despite the distance, the sensors take very detailed pictures of the Earth.
Today ended the three-day Google Earth Engine User Summit 2018. On social media channels such as Twitter and YouTube, could non-participants indirectly follow the progress and news around the Earth Engine technology. I think @eMapR_Lab describes the situation quite well.
In this blog post I use stock prices of the Borussia Dortmund GmbH & Co. KGaA sports club to detect and characterize abrupt changes within the trend component of the time series. The main objective is, to search and find the optimal segmentation parameter which characterizes the timing and magnitude of abrupt changes best.
Imagine we have the perfect time series. Daily values with 100% accuracy. And imagine we know the driving forces for the course of the time series. How beautiful would that be. Well, welcome to the world of finance & soccer.
The goal of this blog post is to arrange a irregularly (with varying time intervals) spaced raster stack from Landsat into a regular time series to be used in the Breaks For Additive Season and Trend (
bfast) package and function.
Yesterday I noticed that my blog cover image is very colorful and the individual menu options are difficult to read. So I decided to convert the image into a grayscale image and to blur it a little.
Here in Berlin, there are 450 soccer fields (some call it football or fútbol) across the city. How do I know? Well, OpenStreetMap has them all. I downloaded their spatial data1, added temporal information from recorded satellite images, recognised the pitch surfaces and analyzed their recent changes.
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