This paper presents a system for weed mapping, using imagery provided by unmanned aerial vehicles(UAVs). Weed control in precision agriculture is based on the design of site-specific control treatmentsaccording to weed coverage. A key component is precise and timely weed maps, and one of the crucialsteps is weed monitoring, by ground sampling or remote detection. Traditional remote platforms, suchas piloted planes and satellites, are not suitable for early weed mapping, given their low spatial andtemporal resolutions. Nonetheless, the ultra-high spatial resolution provided by UAVs can be an efficientalternative. The proposed method for weed mapping partitions the image and complements the spectralinformation with other sources of information. Apart from the well-known vegetation indexes, whichare commonly used in precision agriculture, a method for crop row detection is proposed. Given thatcrops are always organised in rows, this kind of information simplifies the separation between weedsand crops. Finally, the system incorporates classification techniques for the characterisation of pixels ascrop, soil and weed. Different machine learning paradigms are compared to identify the best performingstrategies, including unsupervised, semi-supervised and supervised techniques. The experiments studythe effect of the flight altitude and the sensor used. Our results show that an excellent performance isobtained using very few labelled data complemented with unlabelled data (semi-supervised approach),which motivates the use of weed maps to design site-specific weed control strategies just when farmersimplement the early post-emergence weed control.