A landscape site plan is a graphic representation to show the arrangement of landscape items(like trees, buildings, and paths) from a top view. Restyling a site plan includes adjusting the colors, textures and other artistic customization, which is the task that landscape architects spend more time to work on nowadays. Landscape-freestyle shows the potential of using machine learning for automatically restyling landscape site plans. Landscape-freestyle recognizes the features (locations and sizes) of each item (trees, buildings, and paths) by an object-recognition algorithm on a styled site plan or by reading data directly on an AutoCAD file site plan. The user can choose to upload a template image offered by themselves or a preset style template offered by Landscape-freestyle for restyling. If they upload templates themselves, a style-recognition algorithm is used to identify the items and their artistic customization from the template image and use it for styling. This work presents our first approach to restyle site plans: recognize tree images on site plans, extract tree features and redraw them with other style templates. This research aims to expose the importance of machine learning to benefit a traditional working flow in the design field in a friendly and fast way.