The cloud has made it easier to process large amount of data, and satellite imagery processing benefits from cloud processing too. One of the cloud services that offers access to satellite images, and abilities to process them in the cloud – no more need to download it to your computer and process it there – is Amazon Web Services. If you’ve never worked with cloud processing, getting started with AWS can be a bit daunting. This tutorial gives beginners an introduction to accessing satellite images – Landsat and Sentinel-2 – on AWS.
Sentinel-2 is the optical satellite of the Copernicus programme. It can be compared to Landsat, although it has a better resolution, of 10 to 20 meters. We’ll be using it for crop monitoring with simple vegetation indices.
What’s GDAL? GDAL is the Geospatial Data Abstraction Library, it’s a library to transform raster and vector data and it’s the Swiss Knife of GIS. Topography and bathymetry digital elevation models can easily be handled using GDAL.
Last time we had the task to create mountain ranges polygons for the whole world. I prepared a small tutorial referred to that. Maybe you can find something interesting for you. It will show you a model on how to select defined regions, slicing raster, smoothing and also exporting desired features. Enjoy!
I often find myself in a situation where I want to work with large areas and datasets of OpenStreetMap data. No matter if you want to use them in a QGIS map or create custom map tiles in Tilemill, with the Overpass API you quickly run into performance issues. Imposm is a great tool to overcome that situation, so you can load OSM extracts (worldfiles in pbf format) with a custom data mapping into a PostGIS enabled PostgreSQL database. The downside: It is hard to set up if you are not a database and system admin guru. This is where…