Massive Open Online Courses on Earth Observation 2017

In Q4 2017 there have been at least four Earth Observation related MOOCs that I have been aware of. I have keenly followed these three: Future Learn Earth Observation EO College “Echoes in space” IEA’s Big Data (not solely focused on EO, but there is a decent part devoted to it). All this information was/is Read more about Massive Open Online Courses on Earth Observation 2017[…]

svm satellite

Support Vector Machines – on recognizing pixel clusters in satellite data

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 about Support Vector Machines – on recognizing pixel clusters in satellite data[…]

Earth Observation – UK Space Townhall

A highly simplified version of the ESA strategy for Earth Observation is to observe, understand (predict), decide (inform) and to innovate. This week I heard about, and took part in discussions relating to the UK’s ability to build on and to enhance its place in the EO market. We heard from several speakers, some of Read more about Earth Observation – UK Space Townhall[…]

Earth Observation: Big Data

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 about Earth Observation: Big Data[…]

EO point based unsupervised classification

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 about EO point based unsupervised classification[…]

Extracting values from satellite imagery

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 about Extracting values from satellite imagery[…]

Using Earth Observation data without ever looking at a satellite image

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 about Using Earth Observation data without ever looking at a satellite image[…]

Six thinking hats of Earth Observation

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 about Six thinking hats of Earth Observation[…]

2016 – A year of progress in Earth Observation

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 about 2016 – A year of progress in Earth Observation[…]

Processing images on the Google Cloud

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 about Processing images on the Google Cloud[…]

Template matching with Earth Observation Data

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 about Template matching with Earth Observation Data[…]

Machine learning Landsat / Sentinel data

What changes can be measured using Landsat and/or Sentinel-2 data? In large areas change detection (land use for example) is commonly used for these data sets. If companies like Orbital Insights are counting cars, using shadows from floating oil tanks to determine capacity and measuring levels of construction, what smaller objects and data analytics can be Read more about Machine learning Landsat / Sentinel data[…]