python

Sentinel_5P

Sentinel-5P and Python

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The data for Sentinel 5P was made available on 11th July 2018. #BreakingSince yesterday #Sentinel5P #opendata is available for downloadWith a resolution of up to 7×3.5 km, it enables detection of air pollution over individual cities.This high spatial resolution is key to locate the origin of pollutants and identifying #pollution hotspots pic.twitter.com/ncoP2ZkCmP — Copernicus EU… Read More »Sentinel-5P and Python
colaboratory satellite

Colaboratory notebooks and GDAL

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Last year I heard about Colaboratory by Google and, now that I am using Jupyter Notebooks, it seems the perfect opportunity to explore it further. I previously wrote about how using Jupyter Notebooks is a perfect match for Satellite imagery processing. If you would like to read about that then the post is here. Otherwise,… Read More »Colaboratory notebooks and GDAL
fields

Image reducer in Python

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Google Earth Engine (GEE) has a very nice feature called ‘image reducer’ and, frankly, it is incredibly useful. Say, for example, you have a field boundary and you want to know the mean, median, maximum and minimum NDVI values for it. In GEE you can use the reducer to get these values. You can also… Read More »Image reducer in Python

How many Shapefiles on my computer?

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If you work with Geospatial software you cannot ignore the Shapefile. Whatever your thoughts on them (and it does polarise opinion in the GIS world; look for #Teamshapefile or #switchfromshapefile), I feel that I am ultimately driven by whatever a client would prefer. More often than not that is a preference for a Shapefile. FME… Read More »How many Shapefiles on my computer?

Fastest image reader? Four ways to open a Satellite image in Python

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I wasn’t sure what to call this post… “The Fastest Way to Read a Satellite Image in Python”? “Using Magic Functions in Jupyter Notebooks”? “Four Ways to Read Images into NumPy Arrays”? There are several ways; I’ve not even looked at PIL here, to read your Satellite data into a NumPy array. After all, if… Read More »Fastest image reader? Four ways to open a Satellite image in Python
svm satellite

Support Vector Machines – on recognizing pixel clusters in satellite data

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Scikit-learn, the machine learning library built for Python over 10 years ago is an excellent resource for estimating data and can integrate into geospatial workflows. Helpfully, when choosing an estimator, scikit-learn supplies an interactive diagram to choose the best estimator for the job. One of the big things about machine learning is the need to… Read More »Support Vector Machines – on recognizing pixel clusters in satellite data