PICANTEO: A Modular Change Detection Framework for Remote Sensing Applications

Abstract. This paper presents PICANTEO, a modular and multi-modal change detection framework designed for remote sensing applications in natural disaster response. The framework aims to support damage assessment during both the rapid mapping phase, which occurs in the immediate aftermath of a disaster, and the longer recovery phase. PICANTEO provides automated, reliable disaster-related change detection maps and associated impacted areas to support a wide range of disaster monitoring activities. The integration of uncertainty and ambiguity concepts ensures reliable and qualified results. 
PICANTEO handles multi-modal remote sensing data, including very high-resolution optical imagery, Digital Surface Models, and Synthetic Aperture Radar (SAR) data. Its modular architecture enables users to apply ready-to-use pipelines or implement their own workflows. The provided scalable components can be combined or extended by custom methods to define new applied pipelines. Several real-world case studies demonstrate PICANTEO’s ability to address various disaster scenarios across diverse geographic contexts. Source code is available at: https://github.com/CNES/picanteo.