This paper presents landscape photo classification mechanisms to enhance our proposed photography support system named Phorec. The system utilizes big data in social photo stock services and online historical weather database to recommend relevant photos based on user's natural contexts, including location, time, and weather condition. In order to help user take better photos, important information, such as camera setting, time, and location, is displayed with the photo selected by the user. Since the aim of Phorec is to support not only photographers but also general users as a travel assistant, we mainly focus on the landscape photos. From the huge collected data, landscape photos classification is required to extract the landscape photos from the uncategorized photos. In this paper, some possible landscape classification techniques are investigated and evaluated with our collected dataset. In addition to the straightforward tag analysis and Exif analysis method, we propose a unique method that utilizes a common face recognition technique for excluding non-landscape photos. Experimental results show that our proposed people-exclusion method works well for excluding non-landscape photos from the collected various photo.