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A look at the growth and the polarity of sentiment of Earth Observation and Remote Sensing since 2008, with Python and Pandas

  About 18 months ago I looked at tweets from Twitter containing the phrases Earth Observation or Remote Sensing. Primarily I did this after the first Future EO event that ESA held. You can have a look at the post here: #FutureEO and twitter data mining With the next future EO event this November I Read more about A look at the growth and the polarity of sentiment of Earth Observation and Remote Sensing since 2008, with Python and Pandas[…]

Community GBDX Notebooks

GBDX notebooks are a great way of acessing a vast array of satellite data. You can get yourself a trial account here: https://notebooks.geobigdata.io/ No more downloading satellite imagery, just process it in the cloud. When you think about the sheer volume of satellite data that Digital Globe has and its size to download, processing it Read more about Community GBDX Notebooks[…]

Python for Geospatial work flows part 2: Use Jupyter Notebooks

In part 1 I looked at how to set up a Python 3 environment for use with Geospatial Workflows. If you want to recap take a look here In part 2 we will look at Jupyter Notebooks. Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Read more about Python for Geospatial work flows part 2: Use Jupyter Notebooks[…]

Python for Geospatial work flows part 1: Use anaconda

I have written a lot in the past about using Python for GIS and Earth Observation. If you have not used Python before and you are looking to get started here are a few recommendations to get you up and running. This blog post is for anyone who is new to programming with Python. There Read more about Python for Geospatial work flows part 1: Use anaconda[…]

Sentinel_5P

Sentinel-5P and Python

The data for Sentinel 5P was made available on 11th July 2018. #BreakingSince yesterday #Sentinel5P #opendata is available for downloadWith a resolution of up to 7×3.5 km, it enables detection of air pollution over individual cities.This high spatial resolution is key to locate the origin of pollutants and identifying #pollution hotspots pic.twitter.com/ncoP2ZkCmP — Copernicus EU Read more about Sentinel-5P and Python[…]

K-means in Python 3 on Sentinel 2 data

18 months ago I wrote about unsupervised classification of randomly extracted point data from satellite data. I have been meaning to follow it up with showing how straightforward it is to use the cluster algorithms in Sklearn to classify Sentinel 2 data. I have made this blog into a Juypter Notebook which is available here. Read more about K-means in Python 3 on Sentinel 2 data[…]

GeoPandas

Using GeoPandas to display Shapefiles in Jupyter Notebooks

GeoPandas is a super simple way to work with GIS data using Python. It sits nicely in Jupyter Notebooks as well. This blog is all about displaying and visualising shapefiles in Jupyter Notebooks with GeoPandas. I am going to use a subset of the hexagonal Crop Map of England (CROME) and visualise it in a Read more about Using GeoPandas to display Shapefiles in Jupyter Notebooks[…]

leaflet

Combining OpenCV and leaflet for simple web mapping

I am always on the look out for an easy way to build simple web maps. Ideally I would like to perform OpenCV in the browser but I am not aware of that possibility at present. I work with computer vision and satellite imagery a great deal and have written several blogs on the subject. Read more about Combining OpenCV and leaflet for simple web mapping[…]

colaboratory satellite

Colaboratory notebooks and GDAL

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 about Colaboratory notebooks and GDAL[…]

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[…]

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[…]