Making Sense of Big Data

Assessing fire extent and fire severity with analysis ready Sentinel-2 data in the Open Data Cube python environment.

Differenced normalised burn ratio (dNBR). Contains modified Copernicus data (2020) processed by Digital Earth Australia. ©Copernicus data (2020).

In 2019 to 2020, Australia suffered through some of the most devastating fires ever seen on record. In the unfortunate events that occurred, 10.2 million hectares of land burned, 3100 homes were destroyed, 3 billion animals were killed, and 33 lives were lost [1]. The socioeconomic and ecological impacts of the fires however have extended well beyond the immediate loss. Assessing and analysing the events of the fires quantitively and qualitatively is key to informing policy and decision makers for recovery efforts.

The Open Data Cube initiative is an excellent example of an open source project specifically designed around unlocking…


Hands-on Tutorials

Local and urban scale collection, processing and mapping is now accessible to everyone, including you

Photo by Diana Măceşanu on Unsplash

Photogrammetry (the creation of three-dimensional data from two dimensional imagery) is not a new technique — in fact, according the Wikipedia, the use of photos for topographical mapping was first proposed in 1840 by French Surveyor Dominique Arago [1]. Classic examples include stereophotography which exploits the parallax effect of two images taken from slightly different perspectives to induce a 3D effect whilst more advanced examples of photogrammetry use digital images with spatial metadata taken from many differing angles using the power of computers to crunch out orthomosaic imagery and pointclouds.

Through the power of the open-source community, one application stands…


Assessing the accuracy of fire severity and extent classifications using Python 3, GeoPandas and ‘normal’ Pandas with data derived from Planet and analysis ready data from Sentinel-2 through the Open Data Cube.

Amos J Bennett

Spatial science with a background in civil engineering, remote sensing and GIS. Python fanatic.

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