Time series on Landsat data with Google Earth Engine

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 about Time series on Landsat data with Google Earth Engine[…]

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[…]

every_sensor

Every sensor will be used

This post is a copy of original posted below. I was paid to guest write this. https://medium.com/@mail_83112/every-sensor-will-be-used-d27cf0223f69 Pool your information from anything and everything — Inform decision making — Let the most appropriate person make the final decision… namely, the user Last time I looked at how data from space, particularly Earth Observation data, is growing at a staggering Read more about Every sensor will be used[…]

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[…]

Using space technology to quantify data part 1

This post is a copy of original posted below. I was paid to guest write this. https://medium.com/@mail_83112/using-space-technology-to-quantify-data-part-1-519d397049aa Earth Observation data is part of the foundations of MIS; you could say it is at their core. Being fundamentally a Geospatial company, all of their insights are built upon a layer(s) of remotely sensed data. I believe Read more about Using space technology to quantify data part 1[…]