The increasing availability of satellite imagery acquired from existing and new sensors allow a wide variety of new applications that depend on the use of diverse spectral and spatial resolution data sets. One of the pre-conditions for the use of hybrid image data sets is a consistent geo-correction capacity. We demonstrate how a novel fast template matching approach implemented on a Graphics Processing Unit (GPU) allows us to accurately and rapidly geo-correct imagery in an automated way. The key difference with existing geo-correction approaches, which do not use a GPU, is the possibility to match large source image segments (8192 by 8192 pixels) with relatively large templates (512 by 512 pixels). Our approach is sufficiently robust to allow for the use of various reference data sources. The need for accelerated processing is relevant in our application context, which relates to mapping activities in the European Copernicus emergency management service. Our new method is demonstrated over an area North-West of Valencia (Spain) for a large forest fire event in July 2012. We use DEIMOS-1 and RapidEye imagery for the delineation of burnt fire scar extent. Automated geo-correction of each full resolution image sets takes approximately 1 minute. The reference templates are taken from the TerraColor data set and the Spanish national ortho-imagery data base, through the use of dedicate web map services (WMS). Geo-correction results are compared to the vector sets derived in the related Copernicus emergency service activation request.