Understanding the land cover types and locations within specific regions enables effective environmental conservation. With sufficiently high spatial and temporal resolution, scientists and planners can identify which natural resources are at risk and the level of risk. This information helps inform decisions about how and where to focus conservation efforts. Current land cover products don't meet these spatial and temporal requirements. Microsoft AI for Earth Program's Land Cover Classification Project will use deep learning algorithms to deliver a scalable Azure pipeline for turning high-resolution US government images into categorized land cover data at regional and national scales. The first application of the platform will produce a land cover map for the Puget Sound watershed. This watershed is Microsoft's own backyard and one of the nation's most environmentally and economically complex and dynamic landscapes.