Accurate and timely weed mapping between and within trees is considered one of the major challenges in the site-specific weed management systems. This research presents the first cloud computing approach based on the incorporation of multispectral Unmanned Aerial Vehicle (UAV) imagery in the Google Earth Engine (GEE) programming environment with the aim of improving the mapping of weed patches between and within trees in a citrus farm. For this purpose, the UAV multispectral bands (red, green, blue, near infrared, and red edge), as well as the estimated vegetation height from the UAV Digital Elevation Model (DEM) were analysed in terms of tree and weed discrimination and used as input into Random Forest (RF) and k-nearest neighbors (KNN) machine learning algorithms. From the DEM, the Digital Terrain Model (DTM) was estimated using the Inverse Distance Weighted Interpolation (IDW) of the elevation values of dense points on the soil. The plant height was derived by subtracting the DTM from the DEM, resulting in the Canopy Height Model (CHM). The experimental results show that: (i) the combination of spectral bands and the CHM can classify both trees and weeds with an overall accuracy reached 96.87%; (ii) the RF classifier was more robust compared to KNN in the classification performance; (iii) when compared to the use of UAV spectral bands, the addition of the CHM can improve the accuracy of crop classification by 13.36% (KNN) and 1.79% (RF). Furthermore, the integration of UAV imagery in the GEE was highly efficient in terms of automation of the UAV imagery processing.