I’m writing a series of irregular short posts on some tips and tricks on using Geospatial Python. They may not be best practice, but I hope they are of help. I’ll try and give some context to when I’ve used the ‘tip’. Open files once if possible and.. use a context manager Something I’ve been… Read More »Tips and Tricks #4 Open raster files once (if possible)
I’m writing a series of irregular short posts on some tips and tricks on using Geospatial Python. They may not be best practice, but I hope they are of help. I’ll try and give some context to when I’ve used the ‘tip’. Using many image processing techniques for input When you are building your machine… Read More »Tips and Tricks #3 improve Machine Learning with multiple inputs from a single image
I’m writing a series of irregular short posts on some tips and tricks on using Geospatial Python. They may not be best practice, but I hope they are of help. I’ll try and give some context to when I’ve used the ‘tip’. Use tqdm as a diagnostic tool to understand your iterables You are going… Read More »Tips and Tricks #2 Consider tqdm
I’m starting a new series of irregular short posts on some tips and tricks on using Geospatial Python. They may not be best practice, but I hope they are of help. I’ll try and give some context to when I’ve used the ‘tip’. First up, interoperability. Specifically in Rasterio This is already very well documented… Read More »Tips and Tricks #1 – changing the dimensions of arrays
Way back in Feb 2019 I wrote a series of posts on Geospatial Julia and here they all are: Julia for Geospatial Programming part 1 – setting up Julia for Geospatial part 2 – speed test Julia for Geospatial part 3 – reading, displaying and writing raster data The good news is that for the… Read More »Julia part 4
This post was my ‘script’ from the scenefromeabove S11E6 podcast. If I have missed something it is entirely on me, this gives perhaps a 5% flavour of the Earth Observation world. I have not included all the links, the ones included are at random. If missed please do go back and look at the show… Read More »2021 year in review – scenefromeabove
Quite often I get sent / send shapefiles (zipped up mostly). I wondered if there was any way of reducing the filesize, sometimes these files can get quite big! In this notebook I wanted to run some checks / experiments on whether I could reduce the size of a shapefile. I tried various options, simplifiying… Read More »Reducing Shapefile size
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