update Dec 2022 – This has all be checked for Julia 1.8.3 The top six programming languages for Data Science, according to the first links returned to me from google are: Python R MATLAB Java Julia Scala When I searched for the top five programming languages for GIS, the top results returned this list: Python… Read More »Julia for Geospatial Programming part 1 – setting up
In the summer of 2018 I began a series of posts about setting up Python for anyone who is new to using it. My focus is predominantly on Geospatial Python, but I think the principals should be applicable to any application of Python. If you want a recap then part 1 (setting up in Anaconda)… Read More »Python for Geospatial work flows, part 3: Virtual Environments
Google Earth Engine seems to be updated pretty frequently. Towards the end of 2018, I noticed two interesting things – that some of the Sentinel 5P data is now available and that Google is shutting down Fusion Tables. Fusion Tables will be shut down at the start of December 2019, so you have just under… Read More »Google Earth Engine: Sentinel 5P, and the demise of Fusion Tables
I started writing these reviews in 2017, really to get a feel of the pace of change in Earth Observation / Geospatial today. Looking back over this year the pace has again been incredible. Earth Observation Q1 2018 review Earth Observation Q2 2018 review Earth Observation Q3 2018 review Every three weeks or so we… Read More »Earth Observation Q4 2018 review
Last week I attended the annual GRSG conference (Geological Remote Sensing Group) in London. At the GRSG conference a great deal is crammed into 3 days – talks, papers, networking and workshops. This year I put together a brief workshop on Python for image processing. If you’d like the notebook that I presented you can… Read More »Python Workshop at GRSG
This summer (2018) I was asked to update the book ‘Learn QGIS for 3.4’. I feel honoured to have updated Anita’s original book, now in its fourth edition. Many thanks to her for the opportunity and for kindly reviewing and offering constructive criticism. What QGIS has come to mean to me I’ve always been keen… Read More »Learn QGIS 3.4 fourth edition
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 »A look at the growth and the polarity of sentiment of Earth Observation and Remote Sensing since 2008, with Python and Pandas
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 »Community GBDX Notebooks
Another quarter has flown by. Following on from my Q1 & Q2 reviews earlier this year, it is time to take a look at the main events of Q3. If you missed my Q1 or Q2 review they are available below. Earth Observation Q1 2018 review Earth Observation Q2 2018 review If you’d like… Read More »Earth Observation Q3 2018 review
Almost a year ago I wrote a post on building time-lapse imagery with Google Earth Engine. It shows how to process the Landsat 8 top of atmosphere collection into an mp4 format. It talks about choosing the path and row and filtering on clouds, selecting the bands and converting to 8 bit imagery. I did… Read More »Time series on Landsat data with Google Earth Engine
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 »Python for Geospatial work flows part 2: Use Jupyter Notebooks
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 »Python for Geospatial work flows part 1: Use anaconda
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 »Sentinel-5P and Python
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 »K-means in Python 3 on Sentinel 2 data
Photo from unsplash.com https://unsplash.com/photos/oxgK2f_rxDc Another quarter has flown by. Following on from my Q1 review earlier this year, it’s time to take a look at the main events of Q2. If you missed my Q1 review it is available below. Earth Observation Q1 2018 review If you’d like an overview of the events in 2017… Read More »Earth Observation Q2 2018 review