The required abilities and important behaviors for an autonomous agricultural vehicle can be grouped into three categories: guidance and safe navigation; identification of physical and biological characteristics and execution of agricultural operations along with mapping and analysis of the field. Automated guidance in the field comprises classical behaviors studied by mobile robotic, such as following a specific path and obstacles avoidance. Nonetheless, a wide range of agricultural field inherent challenges is present, e.g. intra rows navigation, end of the row turn path, soil and topography condition adjusting and discernment among insurmountable obstacles and common objects, such as top branches, tall grass and planting failures. This study aimed to use a multirotor Unmanned Aircraft System (UAS), for images collecting in the visible (RGB) and near infrared (NIR) spectrum of an agricultural area of interest, for 3D and normalized difference vegetation index (NDVI) maps production. The image processing was achieved by the software Pix4D (Pix4d AS, Lausanne – Switzerland). This software establishes the relation and merge the collected images, creating a DSM (Digital Surface Model), DTM (Digital Terrain Model), Orthomosaic and vegetation index of the analyzed area. Georeferencing was performed in an indirect manner. This method uses Ground Control Points (GCP) in order to estimate the position of adjacent points. Geographic coordinates were obtained by a GPS, model Hiper AG (Topcon Positioning Systems, Inc.). The areas used for data acquisition are located at the University of São Paulo, Brazil. In order to test the 3D mapping, data were collected at unstructured environments. Aerial images were acquired at asphalt and non-asphalt paved roads inside the Campus. Additionally, pasture and primary crops were also monitored. A 3D model of an area of interest, achieved by a point cloud is expected. When these maps were assembled, georeferenced checkpoints were manually generated, which was the path for the autonomous navigation system for the agricultural mobile robot. Besides presenting XYZ coordinates, the points also show the original image coloration. This allowed the use of new management tools in rural areas, as the FMIS (farming management information system). It is expected that, in the near future, these techniques could be effective to other areas, such as agricultural logistics, spatial and temporal variability control, agricultural robots, supplies deficit, soil deficiency, among others.