  In this paper, we propose a deep learning framework for the automated
counting and geolocation of palm trees from aerial images using convolutional
neural networks. For this purpose, we collected aerial images in a palm tree
Farm in the Kharj region, in Riyadh Saudi Arabia, using DJI drones, and we
built a dataset of around 10,000 instances of palms trees. Then, we developed a
convolutional neural network model using the state-of-the-art, Faster R-CNN
algorithm. Furthermore, using the geotagged metadata of aerial images, we used
photogrammetry concepts and distance corrections to detect the geographical
location of detected palms trees automatically. This geolocation technique was
tested on two different types of drones (DJI Mavic Pro, and Phantom 4 Pro), and
was assessed to provide an average geolocation accuracy of 2.8m. This GPS
tagging allows us to uniquely identify palm trees and count their number from a
series of drone images, while correctly dealing with the issue of image
overlapping. Moreover, it can be generalized to the geolocation of any other
objects in UAV images.
