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We present a new fully data-driven algorithm that uses photometric data fromthe Canada-France-Imaging-Survey (CFIS; $u$), Pan-STARRS 1 (PS1; $griz$), andGaia ($G$) to discriminate between dwarf and giant stars and to estimate theirdistances and metallicities. The algorithm is trained and tested using theSDSS/SEGUE spectroscopic dataset and Gaia photometric/astrometric dataset. At[Fe/H]$<-1.2$, the algorithm succeeds in identifying more than 70% of thegiants in the training/test set, with a dwarf contamination fraction below 30%(with respect to the SDSS/SEGUE dataset). The photometric metallicity estimateshave uncertainties better than 0.2 dex when compared with the spectroscopicmeasurements. The distances estimated by the algorithm are valid out to adistance of at least $\sim 80$ kpc without requiring any prior on the stellardistribution, and have fully independent uncertainities that take into accountboth random and systematic errors. These advances allow us to estimate thesestellar parameters for approximately 12 million stars in the photometricdataset. This will enable studies involving the chemical mapping of the distantouter disc and the stellar halo, including their kinematics using the Gaiaproper motions. This type of algorithm can be applied in the Southernhemisphere to the first release of LSST data, thus providing an almost completeview of the external components of our Galaxy out to at least $\sim 80$ kpc.Critical to the success of these efforts will be ensuring well-definedspectroscopic training sets that sample a broad range of stellar parameterswith minimal biases. A catalogue containing the training/test set and allrelevant parameters within the public footprint of CFIS is available online.

Dwarfs or giants? Stellar metallicities and distances in the Canada-France-Imaging-Survey from $ugrizG$ multi-band photometry