This week I attended an excellent talk given by Dr Jon Blower, CTO at IEA in Reading, entitled “Satellites, sensors and statistics – from environmental data to decisions” which was a meetup of the Society of Data Miners at the Royal Statistical Society in London. It is always good to attend events like this when… Read More »Space is open
This week Planet successfully launched 88 of its doves, taking another massive step forward in mapping the entire planet daily. I have written about the dramatic increase in temporal resolution data several times in the past. It is worth repeating though – I think – that while Remote Sensing is not new, the level of… Read More »Data, data everywhere
What is Big Data? Big data is a term for data sets that are so large or complex that traditional data processing application softwares are inadequate to deal with them. https://en.wikipedia.org/wiki/Big_data In an event this week in central London I heard big data described as “non-spreadsheet” data. I think Earth Observation (EO) data could fit… Read More »Earth Observation: Big Data
With remote sensing we often talk about the 3 resolutions: spatial, temporal and spectral. In recent years a massive step change has occurred with temporal data. Even this week there has been another huge leap forward. Planet have just announced that they are to launch a world record 88 of its doves and, if successful,… Read More »EO point based unsupervised classification
“As the most complex of all the Sentinel satellites, Sentinel-3A carries a suite of cutting-edge instruments to measure oceans, land, ice and atmosphere.” I haven’t used Sentinel 3 before; my preferred workhorse is Sentinel 2 and its multispectral data. I’m interested in the surface of the earth and its characteristics. Mapping features, classifying terrains and… Read More »Sentinel 3 for beginners
Last week I wrote about using Earth Observation data without ever looking at a satellite image; extracting the values from an image and then presenting the data in an informative way. http://www.acgeospatial.co.uk/blog/eo-without-a-satellite-image/ Just how easy is it to extract the values from images? Sentinel 2a is operating with 12 bands; that means every location that… Read More »Extracting values from satellite imagery
To enable a user to use space derived data without ever actually seeing a satellite image. To move from pixels to analytics. To break through the ‘that’s a nice image – but so what?’ barrier? To add value. I wrote about my thoughts on this last week; http://www.acgeospatial.co.uk/blog/six-thinking-hats-eo/ the need to move towards more than just… Read More »Using Earth Observation data without ever looking at a satellite image
I have been reading Edward de Bono’s Six Thinking Hats – “Run better meetings, make faster decisions”. Having not come across this book before I have become increasingly drawn into this idea of the hats and their associated ways of thinking. The idea is that you put on one of these hats at a time… Read More »Six thinking hats of Earth Observation
The barriers to entry have never been lower I was gifted a Raspberry Pi Zero. It cost £4. Combine this with a 32gb MicroSD card, a phone charger for a smart phone (Android OS in my case), a micro USB to USB cable and an old laptop – its amazing what you can do. £4… Read More »QGIS and Raspberry Pi Zero
For me 2016 marked a point when the term ‘Remote Sensing’ was used less frequently and ‘Earth Observation’ significantly more. Make of that what you will. With the impending deluge of data (not sure at what point we move from impending to actual) the race is now on to derive information from the pixels. You… Read More »2016 – A year of progress in Earth Observation
October 2016 brought the announcement from Google that it had all Landsat and Sentinel data on its cloud. Amazon are also hosting all this data, ESRI are using the Amazon cloud and so are Planet (both of these for over a year). Never before has so much Earth Observation data been available to everyone. I… Read More »Processing images on the Google Cloud
The Great Britain and Northern Island cycling team are good. In fact, they are very good. In Bejing 2008 they won 8 golds, 4 silver and 2 bronze (14 medals; 2nd place France with 6 medals) In London 2012 they won 8 golds, 2 silver and 2 bronze (12 medals, 2nd place Germany with 6… Read More »Marginal gains in EO – Google goes for timelapse
I have previously written about automatically edge detecting. Using OpenCV is a nice way of quickly running the edge detect and getting some information back about the image. You can read about the Canny edge detection algorithm here Here is how Wikipedia details the process of the canny edge detection (if you didn’t click the… Read More »Reclassify and Numpy
Template matching is a useful technique. Some common uses include manufacturing as a part of quality control, a way to navigate a mobile robot, or as a way to detect edges in images. But can techniques such as these be useful for data captured from satellites? I am especially interested here in Optical data, but… Read More »Template matching with Earth Observation Data
You know about planet (formerly Planet Labs), right? Their aim is to map every part of the Earth’s surface everyday using their Dove satellites. One Pixel for every area every day. Temporal data at its best! If you get an image every day and you want to monitor it (change being the obvious reason) a… Read More »Edge detecting with Planet API