# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
## 2 Background
[PERSON] and [PERSON] [35] used a UAV and its RBG images to train a deep learning algorithm to detect individual trees and classify them. They reported many-to-one segmentation errors that needed to be corrected manually. Additionally, UAV's remote sensing capabilities have been used to detect and segment trees deteriorated by myrtle rust in natural and plantation forests [36]. This work illustrates an experimentation case on paperbark tea trees in Australia, which integrates an on-board hyperspectral camera and several machine learning algorithms for the classification. Once trained, the system proved to be very accurate and efficient in classification, with a global multiclass detection rate of 97%, but requiring about 3 min to evaluate every new instance. All of these works focus more on the scientific aspect and on the quality of the results than on the time-to-delivery challenge. The processing of [29] for the trees classification of each single location, flown over for 10-30 min, needed between 22 h to more than 50 h of processing at the researcher's premises. In [18], the authors related a complex pipeline of algorithms, known as object-based image analysis, which ended with a final map of the areas with weeds. It needed 40 h of processing using commercial software. Most of the classification and segmentation algorithms used on the UAV were initially tested in fixed cameras. For instance, people detection and counting is a recurrent topic in many research works [37; 38; 39]. With the continuous increase in the number of aerial devices being used (satellites, constellations, pseudo-satellites, airborne UAV, and balloons) and of the number of deployed on-site sensors, the storage needed was approximated to exceed one exabyte (one thousand petabytes or one million terabytes) in 2014 [12]. The authors presented the new challenges created by this incremental rise of remote sensing data. Big data infrastructures and processing capabilities need to be applied to next-generation remote sensing applications to convert these data into useful information. The work in [40] addressed the different parallelization strategies for processing the high quantity of data at different architecture levels, including virtualization, cloud computing, grid computing, service-oriented architectures, or specific hardware and multicores. Moreover, it addressed the new trends in remote sensing developments, which have been dictated mainly by government intelligence and scientific agencies, but also by more general users and companies. For new users, time to market is an important cost, and reduction of costs is a must. As an example, AgrCloud [41] is a proposed solution for providing remote sensing processing services through the Internet, able to obtain speed-ups of two-timesusing the MapReduce paradigm in a four-node cluster. The potential of parallelism as a solution in remote sensing has never been addressed for a distributed architecture that includes the UAV processors. Cloud computing is a paradigm that arises from the expansion of services available on the Internet. Initially available as remote disk space, cloud services started to evolve into virtual computer services, parallel computing clusters, and specific software execution sites. Concepts such as platform as a service, software as a service, elastic computing, etc., began to appear to describe all of these services. They are all part of cloud computing and are provided through the Internet, with the actual location of the hardware being irrelevant. Recently, the term cloud-assisted remote sensing has been proposed to expand the cloud services to remote sensing applications, creating the new concept of sensing as a service [42]. Its main advantage is the possibility to share data collected remotely with many users and in real time.