The extensive Cloud pool of resources and infrastructure can deliver significant improvements to Unmanned Aerial Vehicle (UAV) Cyber-Physical Systems (UAV-CPSs) relying on data or code from a network to operate, but not all sensors, actuators, computation modules and memory depots from a single fixed structure. This chapter is organised around the potential benefits of the Cloud: (1) Big Data (BD) access to visual libraries containing representations/descriptive data, images, video, maps and flight paths, (2) Cloud Computing (CC) functionalities for Grid Computing (GC) on demand for statistical analysis, Machine Learning (ML) algorithms, Computational Intelligence (CI) applications and flight planning, (3) Collective UAV Learning (CUL) where UAVs share their trajectories, control guidelines and mission outcomes and (4) human-machine collaboration through crowdsourcing for analysing high-dimensional high-resolution (HDHR) images/ video, classification of scenes/objects/entities, learning and error correction/ concealment. The Cloud can also expand UAV-CPSs by offering (a) data sets, models, all sorts of literature, HDHR benchmarks and software/hardware simulators, (b) open competitions for UAV-CPS designs with Open Source Hardware (OSH) and (c) Open-Source Software (OSS). This chapter talks about some open challenges and new trends in UAV-CPSs.

The popularity of the Open Source Tool (OST) has expanded significantly. This is the case for Unmanned Aerial Vehicles (UAVs) based on open-source hardware (OSH) as well. Open-source software (OSS) and OSH can be applied in a wide range of applications and can improve several technologies. The chapter begins with an introduction to OSS depicting its rationale, description of fundamental differences between OSS and proprietary software (PS), what benefits OSSs provide to overall UAV community, the motives leading people to pick up an OSS instead of a PS, which helps the academic and research community. This chapter also covers some OSSs used within the UAV community to support all aspects of UAV technology and the Remote Sensing (RS) and photogrammetry data post-processing chain. It is possible to build fully autonomous and operational UAV based only on OSH and OSS. The chapter describes the state of the art for OSS widely used in UAV technology, the software used in all aspects of UAV technology such as ARDUPILOT-based Autopilot firmware, MISSION PLANNER-based ground station, OPENTX transmitter software, MINIM On-Screen Data (OSD) software, Open Drone Map photogrammetry data processing suite, Web drone data-processing suite WebODM. This chapter describes several concepts and characteristics of open software/hardware, built-in functions, and particular features as well as platform requirements. A typical UAV photogrammetry workflow for drone construction with flight planning/execution and OSS data processing is provided.

The growth in the number of aerial images available is stimulating research and development of computational tools capable of extracting information from these image databases. However, developing a new computer vision (CV) software is complicated because many factors influence the extraction of information from aerial images, such as lighting, flight altitude, and optical sensors. The CV has been incorporated in most modern machines such as autonomous vehicles and industrial robots. The aim is to produce a high-quality image database of low-altitude Unmanned Aerial Vehicle (UAV) flights with flight condition-logged for photogrammetry, remote sensing, and CV. This work resulted in a collection of aerial images in the visible and thermal spectrum, and this set of images was captured in different schedules of the day, altitudes of flight, times of the year. The cameras are synchronised with the UAVs autopilot, and they were spatially and spectrally characterised in the laboratory. This research makes available low altitude aerial images of a region in Brazil to all community, with the precise flight and capture information, as well as additional features such as ground truth and georeferenced mosaic. Examples of the use of the database are shown for mosaic generation and development of CV algorithms for autonomous navigation of UAVs [1,2]. Furthermore, this database will serve as a benchmark for the development of the CV algorithms suited for autonomous navigation by images.

Over the past few decades of imaging, these sensing instruments are now more advanced with multiple missions such as surveillance, monitoring, tracking and destruction of spatial objects. Nowadays, unmanned aerial vehicles (UAVs) are much prevalent, which could acquire a comprehensive view and could perform actions even to the lowest target levels at the ground. The UAV can be developed with minimal cost than other remote mission. Hence, it is much cost-effective. This chapter aims at detailing the critical aspects of two different variants of remotesensing (RS) technologies in UAVs: (a) multispectral imaging (MSI) and (b) hyperspectral imaging, which accounts for the spatial and spectral signatures of the observed underlying natural phenomena.

This chapter proposes a framework to super-resolve the low-resolution (LR) images captured using the unmanned aerial vehicle. The framework used a convolution neural network to super-resolve the LR image. This framework also removes the haze present in the LR image. The proposed system is evaluated using peak signal to noise ratio, structural similarity (SSIM) and visual information fidelity (VIFP) in the pixel domain. The experimental results demonstrate the advantage of the proposed method when compared to other state-of-the-art algorithms based on qualitative and quantitative analysis. Future trends in super-resolution (SR) unmanned aerial vehicle (UAV) imaging are discussed at the end of this chapter, followed by the concluding section.

The current awareness in unmanned aerial vehicles (UAVs) has prompted not only military applications but also civilian uses. Aerial vehicles' requirements aspire to guarantee a higher level of safety comparable to see-and-avoid conditions for piloted aeroplanes. The process of probing obstacles in the path of a vehicle and determining whether they pose a threat, alongside measures to avoid these issues, is known as see and avoid or sense and avoid. Other types of decision-making tasks can be accomplished using computer vision and sensor integration since they have a great potential to improve the performance of the UAVs. Macroscopically, UAVs are cyber-physical systems (CPSs) that can benefit from all types of sensing frameworks, despite severe design constraints, such as precision, reliable communication, distributed processing capabilities and data management. This book is paying attention to several issues that are still under discussions in the field of UAV-CPSs. Thus, several trends and needs are discussed to foster criticism from the readers and to provide further food for thought.