I’ve been writting a lot recently about STAC, COG and Python. These are some of my recent posts: COG and Landsat in QGIS COG and Landsat with Python STAC, COG, Python and QGIS Smiley Face, STAC and COG I am very much aware that the majority of work with Earth Observation data is still within… Read More »Building virtual rasters from STAC
This is a light hearted post which hopefully you find fun. Let’s take a png of a (hand drawn!) smiley face and hide it in Satellite images that I have streamed as a Cloud Optimized Geotiff and found via a SpatioTemporal Asset Catalog (STAC). We are going to be using this 2kb png and hiding… Read More »Smiley Face, STAC and Cloud Optimised Geotiffs
I’ve spent a lot of time in the past trying to find satellite data. What the SpatioTemporal Asset Catalog (STAC) does is to build a common specification for how Satellite images (read Assets, they can include other things like drone imagery) are queried and found online. Why should we care about this? Well, for a… Read More »STAC, COG, Python and QGIS
Alastair and I reviewed the year on the #scenefromabove podcast, you can listen to it below. Jump to the 12minute mark if you want to listen instead. This is my attempt at capturing 2020 in 3minutes. Start your clocks! Here comes 2020, (a Shapefile is more than one file). Earth Observation 2.0, the Analytical Earth… Read More »2020 in 3mins
I don’t think I have come across a GitHub repo any code while looking for more resources to add for awesome-eo-code that is related to evaluating training data for Satellite imagery. I have often wondered how someone new to the industry could make their mark. In a sector that has focused a great deal of… Read More »Training data and Satellite data
Plotting raster data can be done in a few lines of code with Matplotlib. Adding a colour bar (color bar) associated to your data can be as simple as plt.colorbar() for example: This is a nice quick way of displaying a scale for your data. I’ve often wanted to plot discrete (and not continuous) colour… Read More »Colour bar for discrete rasters with Matplotlib
In late June GeoPandas 0.8 was released. This release has made massive steps in Input/Output (IO). A compelling thing about GeoPandas is its simple way to read a GIS vector dataset (Shapefile, GeoJson etc). It’s one line of code, or two if you include the import statement. import geopandas as gpd gpd.read_file(‘your_shapefile.shp’) Now, with the… Read More »GeoPandas and PostGIS
During lockdown in April and May 2020, the #scenefromabove podcast held a series of lunchtime networking chats with people all over the world. Alastair wrote about them here. We weren’t sure how they were going to go, but they introduced some really interesting ideas and themes. Two really stayed with me. From our first chat… Read More »awesome-earthobservation-code
This week, I gave my first ever virtual training course and I wanted to put some notes down for those thinking about giving virtual training or those considering attending a virtual training course. I used Zoom. Firstly, I am indebted to Nick Bearman; I reached out to him to ask about his experiences having read… Read More »Some honest thoughts on virtual training
Jupyter Notebooks or Jupyter Labs are everywhere at the moment. And rightly so; there are so many things you can do with them, such as this, this and this. These three pages were just the first three returned after searching google for “cool things you can do with Jupyter notebooks“ This post is a simple… Read More »Building your own collection of functions and using them in Jupyter
18 months ago I wrote about setting up Anaconda for Python. The post is here I highly recommend using Anaconda. I have now come to a natural break in projects and I want to upgrade my version to Python 3.7 – which is the default version that Anaconda uses (3.7.7 as of the start of… Read More »Python for Geospatial work flows part 4: Updating from Python 3.6 to 3.7 with Anaconda
This is the firstly EO quarterly news of 2020. I try and use these posts to record what happen in the general sphere of Earth Observation / Geospatial across each quarter. If you want a more regular news please check out the #scenefromabove podcast which gets a new episode approximately once every 3 weeks. If… Read More »Earth Observation Q1 2020 review
Last time I wrote about how Cloud Optimized Geotiffs (COGs) are a great way to save you from having to download data and that they can be be easily streamed into QGIS (3.4 and later). If you would like to read that post it is available here. This time though I want to look in… Read More »Using Cloud Optimized Geotiff part 2 – Python
The Cloud Optimized Geotiff (COG). Is it helping you to stop unnecessarily downloading data? It should. One of the biggest Earth Observation ‘things’ in 2019 was that USGS is delivering Landsat 8 as COGs (that have a SpatioTemoral Asset Catalog – STAC as well) But old practices die hard and sometimes, despite the knowledge of… Read More »Using Cloud Optimized Geotiff part 1
I spent a large chunk of 2019 working on a project looking at measuring Natural Capital using Earth Observation. I thought I would share some thoughts on this interesting topic. First off, a definition: Natural capital can be defined as the world’s stocks of natural assets which include geology, soil, air, water and all living… Read More »Natural Capital and Earth Observation