The miniaturization of electronics, computers and sensors has created new opportunities for remote sensing applications. Despite the current restrictions on regulation, the use of unmanned aerial vehicles equipped with small thermal, laser or spectral sensors has emerged as a promising alternative for assisting modeling, mapping and monitoring applications in rangelands, forests and agricultural environments. This review provides an overview of recent research that has reported UAV flight experiments on the remote sensing of vegetated areas. To provide a differential trend to other reviews, this paper is not limited to crops and precision agriculture applications, but also includes forest and rangeland applications. This work follows a top-down categorization strategy and attempts to fill the gap between application requirements and the characteristics of selected tools, payloads and platforms. Furthermore, correlations between common requirements and the most frequently used solutions are highlighted.

Opening the sky to new classes of airspace user is a political and economic imperative for the European Union. Drone industries have a significant potential for economical growth according to the latest estimations. To enable this growth safely and efficiently, the CORUS project has developed a concept of operations for drones flying in Europe in very low-level airspace, which they have to share that space with manned aviation, and quite soon with urban air mobility aircraft as well. U-space services and the development of smart, automated, interoperable, and sustainable traffic management solutions are presented as the key enabler for achieving this high level of integration. In this paper, we present the U-space concept of operations (ConOps), produced around three new types of airspace volume, called X, Y, and Z, and the relevant U-space services that will need to be supplied in each of these. The paper also describes the reference high-level U-space architecture using the European air traffic management architecture methodology. Finally, the paper proposes the basis for the aircraft separation standards applicable by each volume, to be used by the conflict detection and resolution services of U-space.

Forest fires are a challenging problem for many countries. They often cause economical lost and ecological damage, and they can sometimes even cost human lives. Finding hot spots immediately after a fire is an important part of fighting forest fires. The main objective is to obtain a temperature map of the burned area, to locate the most critical embers. This information can help firefighter managers make the correct decisions about ground crew movements. The pervasive application described in this article lets firefighters obtain images of hot spots directly from an unmanned aircraft and receive commands from their manager through a communication network. Every firefighter holds a personal electronic device (PED), which includes a touch screen, Wi-Fi connectivity, a GPS receiver, and temperature sensors. Because terrain conditions such as abrupt ravines, rocks, and dense vegetation can introduce obstacles to connectivity, a balloon with a Wi-Fi device is tethered to every firefighter's truck to improve communication. In addition, a fixed-wing unmanned aircraft augments the number of communication layers to three. This article studies the quality of this three-layered network in maintaining the application's bandwidth requirements.

Drones are expected to be used extensively for delivery tasks in the future. In the absence of obstacles, satellite based navigation from departure to the geo-located destination is a simple task. When obstacles are known to be in the path, pilots must build a flight plan to avoid them. However, when they are unknown, there are too many or they are in places that are not fixed positions, then to build a safe flight plan becomes very challenging. Moreover, in a weak satellite signal environment, such as indoors, under trees canopy or in urban canyons, the current drone navigation systems may fail. Artificial intelligence, a research area with increasing activity, can be used to overcome such challenges. Initially focused on robots and now mostly applied to ground vehicles, artificial intelligence begins to be used also to train drones. Reinforcement learning is the branch of artificial intelligence able to train machines. The application of reinforcement learning to drones will provide them with more intelligence, eventually converting drones in fully-autonomous machines. In this work, reinforcement learning is studied for drone delivery. As sensors, the drone only has a stereo-vision front camera, from which depth information is obtained. The drone is trained to fly to a destination in a neighborhood environment that has plenty of obstacles such as trees, cables, cars and houses. The flying area is also delimited by a geo-fence; this is a virtual (non-visible) fence that prevents the drone from entering or leaving a defined area. The drone has to avoid visible obstacles and has to reach a goal. Results show that, in comparison with the previous results, the new algorithms have better results, not only with a better reward, but also with a reduction of its variance. The second contribution is the checkpoints. They consist of saving a trained model every time a better reward is achieved. Results show how checkpoints improve the test results.

The continuous development of technical innovations provides the opportunity to create new economic markets and a wealth of new services. However, these innovations sometimes raise concerns, notably in terms of societal, safety, and environmental impacts. This is the case for services related to the operation of unmanned aerial vehicles (UAV), which are emerging rapidly. Unmanned aerial vehicles, also called drones, date back to the first third of the twentieth century in aviation industry, when they were mostly used for military purposes. Nowadays, drones of various types and sizes are used for many purposes, such as precision agriculture, search and rescue missions, aerial photography, shipping and delivery, etc. Starting to operate in areas with low population density, drones are now looking for business in urban and suburban areas, in what is called urban air mobility (UAM). However, this rapid growth of the drone industry creates psychological fear of the unknown in some parts of society. Reducing this fear will play an important role in public acceptance of drone operations in urban areas. This paper presents the main concerns of society with regard to drone operations, as already captured in some public surveys, and proposes a list of mitigation measures to reduce these concerns. The proposed list is then analyzed, and its applicability to individual, urban, very large demonstration flights is explained, using the feedback from the CORUS-XUAM project. CORUS-XUAM will organize a set of very large drone flight demonstrations across seven European countries to investigate how to safely integrate drone operations into airspace with the support of the U-space.

Unmanned aerial systems (UAS) are becoming a common tool for aerial sensing applications. Nevertheless, sensed data need further processing before becoming useful information. This processing requires large computing power and time before delivery. In this paper, we present a parallel architecture that includes an unmanned aerial vehicle (UAV), a small embedded computer on board, a communication link to the Internet, and a cloud service with the aim to provide useful real-time information directly to the end-users. The potential of parallelism as a solution in remote sensing has not been addressed for a distributed architecture that includes the UAV processors. The architecture is demonstrated for a specific problem: the counting of olive trees in a crop field where the trees are regularly spaced from each other. During the flight, the embedded computer is able to process individual images on board the UAV and provide the total count. The tree counting algorithm obtains an $F_1$ score of $99.09\%$ for a sequence of ten images with 332 olive trees. The detected trees are geolocated and can be visualized on the Internet seconds after the take-off of the flight, with no further processing required. This is a use case to demonstrate near real-time results obtained from UAS usage. Other more complex UAS applications, such as tree inventories, search and rescue, fire detection, or stock breeding, can potentially benefit from this architecture and obtain faster outcomes, accessible while the UAV is still on flight.