Change detection by Simularity

Source	Any tasking or catalog collection with RGB or panchromatic bands
Required parameters	- Console: If your chosen input data meets the above specifications you will be able to run your job - API: You will need to input an Output title and the Input data
Accuracy	For better performance, ensure the following: -Both STAC items have matching geometric and radiometric processing levels. -The GSD of the two STAC items shouldn’t differ by more than 25%.
Requirements for input imagery	- Both STAC items must be CNAM-compatible (the data was added to storage starting in 2023 & the data comes from a supported collection). - Both STAC items must come from the same optical collection. - Both STAC items must be georectified or orthorectified. - Both STAC items must have an asset with the same bands. - The geometries of the STAC items must overlap by at least 20%. - The cloud coverage of the STAC items must be less than 25%.
Output data format	- A GeoTIFF of the coregistered source image - A GeoTIFF probability map of detected change - A GeoTIFF heatmap for likelihood of change, with warmer colors showing a higher likelihood of change
Output	The result will be added to your account as a STAC item in a new STAC collection. You can retrieve the resulting data in one of the following ways: - Open the console, go to Data management → Jobs - Retrieve the results via the API

Algorithm performance and training data

The process uses the Automated Image Anomaly Detection System (AIADS) algorithm from Simularity. It detects changes local to a given scene, such as new buildings or removed trees. It ignores changes that affect the whole scene, such as leaves changing color from green to brown.

Further information

For more information, please visit the technical documentation.