Earth Observation

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. https://medium.com/@mail_83112/every-sensor-will-be-used-d27cf0223f69 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 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. https://medium.com/@mail_83112/using-space-technology-to-quantify-data-part-1-519d397049aa 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

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

Q4 2017 Earth Observation

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I started writing these reviews last year, really to get a feel of the pace of change in Earth Observation / Geospatial today. Looking back over the year the pace has been incredible. Q1 review Q2 review Q3 review First off I want to mention https://unsplash.com. This is an excellent website supplying free images to… Read More »Q4 2017 Earth Observation

First steps – video from space

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There are many Satellites in orbit today capable of recording video from space; there is even a camera attached to the ISS. This is a relatively new and exciting area in Earth Observation. I say new… this article is almost four years old at the time of writing. https://blogs.scientificamerican.com/plugged-in/high-definition-video-from-space-is-available-for-purchase-finally/ What does this mean? Is anyone… Read More »First steps – video from space

Sentinel 2 level 2a data from AWS

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There are a variety of ways to download Sentinel 2 data, for example here, here or here. One of the easiest ways, and it seems to at least to me the fastest, is through AWS. You can go straight to the bucket, or search and explore for data through one of the eight recommended featured… Read More »Sentinel 2 level 2a data from AWS

Q3 Earth Observation Review

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  Another quarter has flown by so, following on from my Q1&Q2 reviews, it’s time to take a look at the main events of Q3. If you missed my Q1 or Q2 reviews they are available below. Q1 http://www.acgeospatial.co.uk/blog/earth-observation-q1-2017/ Q2 http://www.acgeospatial.co.uk/blog/earth-observation-q2-review/   Q3 2017 Here are some of the things you may or may not… Read More »Q3 Earth Observation Review

Blob detection on Satellite Imagery, using OpenCV

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OpenCV is a fantastic tool for quickly processing satellite data. It is incredibly powerful and very fast. It has also just recently been updated; the full announcement is here. Previously I used Image Segmentation with the Watershed Algorithm to successfully map circular irrigation features using OpenCV. Like many geospatial/image processes (where there are multiple ways… Read More »Blob detection on Satellite Imagery, using OpenCV