How many Shapefiles on my computer?

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 about How many Shapefiles on my computer?[…]

Fastest image reader? Four ways to open a Satellite image in Python

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 about Fastest image reader? Four ways to open a Satellite image in Python[…]

Video Space

First steps – video from space

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 about First steps – video from space[…]

learning

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[…]

Image Segementation

Superpixel and Earth Observation – Intro

The Rub’ al Khali, also known as the Empty Quarter, is beautiful and is also massive. It is the world’s largest sand desert (also known as an erg) covering an area larger than France. If you have watched Star Wars: Force Awakens you might be interested to know that 6 months of filming took place Read more about Superpixel and Earth Observation – Intro[…]

Google Earth Engine

Building time-lapse imagery with Google Earth Engine

If you have an hour (or 3) to spare then there are certainly worse things to do than to investigate the last 30 or so years of time-lapsed imagery on Google Earth Engine Timelapse. It can make for uncomfortable viewing as ice retreats or urban areas expand at a phenomenal rate. It can inform you; Read more about Building time-lapse imagery with Google Earth Engine[…]

Q3 review 2017

Q3 Earth Observation Review

  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 about Q3 Earth Observation Review[…]

blobs on satellite data

Blob detection on Satellite Imagery, using OpenCV

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 about Blob detection on Satellite Imagery, using OpenCV[…]

buildings classified

Identifying buildings on medium resolution Satellite data using Monteverdi software

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 about Identifying buildings on medium resolution Satellite data using Monteverdi software[…]

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[…]

pan sharpening

Pan Sharpening Sentinel 2 with Planet data

Pan sharpening is the process of increasing the spatial resolution of an RGB (Red, Green, Blue) image. Both Landsat 8 and Landsat 7 have a 15m spatial resolution panchromatic band. The benefit of pan sharpening is clear; it allows the production of a significantly sharpened RGB image. There is plenty written about pan sharpening – Read more about Pan Sharpening Sentinel 2 with Planet data[…]