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 — Copernicus EU… Read More »Sentinel-5P and Python
earth observation

Earth Observation Q2 2018 review

Photo from Another quarter has flown by. Following on from my Q1 review earlier this year, it’s time to take a look at the main events of Q2. If you missed my Q1 review it is available below. Earth Observation Q1 2018 review If you’d like an overview of the events in 2017… Read More »Earth Observation Q2 2018 review
news 2018

Earth Observation Q1 2018 review

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I started writing these reviews a year ago, really to get a feel of the pace of change in Earth Observation / Geospatial today. If you are interested in the major EO related events in 2017, please go to the link below: Q4 2017 Earth Observation Q1 2018 China launched two Superview-1 EO satellites into… Read More »Earth Observation Q1 2018 review

Scene from above Podcast

Since late last year Alastair Graham and I have been working together on a Podcast. It is something we have both been keen to explore for a while and it certainly felt that the time was right. The aim, at least for me, was to produce something that I would want to listen to. To… Read More »Scene from above Podcast

Every sensor will be used

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This post is a copy of original posted below. I was paid to guest write this. Pool your information from anything and everything — Inform decision making — Let the most appropriate person make the final decision… namely, the user Last time I looked at how data from space, particularly Earth Observation data, is growing at a staggering… Read More »Every sensor will be used
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

Using space technology to quantify data part 1

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This post is a copy of original posted below. I was paid to guest write this. Earth Observation data is part of the foundations of MIS; you could say it is at their core. Being fundamentally a Geospatial company, all of their insights are built upon a layer(s) of remotely sensed data. I believe… Read More »Using space technology to quantify data part 1

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