September 2017

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

Identifying buildings on medium resolution Satellite data using Monteverdi software

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This is the final part in a series on using Planet’s Open California dataset. I’ve summarised it all here. Detailed mapping of building footprints is fast becoming one of the key challenges/uses for very high spatial resolution Earth Observation data today. Being able to accurately acquire these footprints remotely and at speed is where the… Read More »Identifying buildings on medium resolution Satellite data using Monteverdi software

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