This reports purpose is to give a general view on how to handle information collectedthrough photogrammetry and laser scanning with the help of UAS. The report ismade in collaboration with WSP Group and will briefly describe howphotogrammetry and laser scanning, also known as Lidar, works and how those areused in the construction business today. Then the focus will be on how gathered datais handled today, how outsourcing of data should be approached and if there are anyconsequences if data is handled in the wrong way. Interviews have been made with allparts in the UAS business. That includes authorities as Transporstyrelsen,Försvarsmakten. Interviews have also been conducted with consultants that operatethese systems, distributors of the different systems and solicitor. What can been seenis that, today, outsourcing and cloud services are not used to a great extent andtherefore not many have felt the obligation to familiarize themselves with theregulations.Based on the interviews and studies of relevant laws the conclusion can be made that,even if the purpose is not to publish the material when you outsource, it should stillbe treated as a publication.The penalties, when material is handled in the wrong way, are usually up to one yearin prison but if there is severe mismanagement it can be a matter of national securityand can be classified as espionage. Then the penalties are much harder.

abstract: Research literature was reviewed to find recommended tools and technologies for operating Unmanned Aerial Systems (UAS) fleets in an urban environment. However, restrictive legislation prohibits fully autonomous flight without an operator. Existing literature covers considerations for operating UAS fleets in a controlled environment, with an emphasis on the effect different networking approaches have on the topology of the UAS network. The primary network topology used to implement UAS communications is 802.11 protocols, which can transmit telemetry and a video stream using off the shelf hardware. Other implementations use low-frequency radios for long distance communication, or higher latency 4G LTE modems to access existing network infrastructure. However, a gap remains testing different network topologies outside of a controlled environment. With the correct permits in place, further research can explore how different UAS network topologies behave in an urban environment when implemented with off the shelf UAS hardware. In addition to testing different network topologies, this thesis covers the implementation of building a secure, scalable system using modern cloud computation tools and services capable of supporting a variable number of UAS. The system also supports the end-to-end simulation of the system considering factors such as battery life and realistic UAS kinematics. The implementation of the system leads to new findings needed to deploy UAS fleets in urban environments. / Dissertation/Thesis / Masters Thesis Computer Science 2018

With the dominance of Android in the smartphone market, malware targeting Android users has increased over time. Android applications are now being used to control unmanned aircraft systems (UAS) making smartphones the storehouse for all the data that is generated by the UAS. This data can be sensitive in nature which puts the user at the risk of data exfiltration. As most Android-hosted UAS applications are proprietary software, their source code cannot be studied or modified. This thesis discusses an external monitoring system which is devised in order to assess the threat of data exfiltration. The system is further used to analyze the network behavior of the popular Android-hosted UAS application, DJI GO 4. Current methods to limit data exfiltration are discussed along with their limitations and are categorized based on the ease of deployment. Even though the Android framework provides a permission system which helps to limit the capabilities of an application, this security mechanism is coarse-grain in nature. The user either allows access to the required permissions or the application fails to function. Moreover, there is no system in place to provide finer control over the existing permissions that are granted to an application. This thesis proposes a fine-grain and application-specific access control mechanism based on system call interposition. The solution focuses on limiting the I/O operations of the target application without any framework or application modification. / Master of Science / Advances in smartphone technology has led major consumer and commercial unmanned aircraft system (UAS) manufacturers to provide users with the feature to fly the UAS using their smartphones. The UAS generate and store large amounts of data which may be sensitive in nature. This has led the U.S. Department of Defense to ban the use of all commercial off-the-shelf UAS due to the threat of data leakage. This thesis discusses an external monitoring system which maps the network behavior of an Android-hosted UAS application, along with the existing methods to limit data leakage. To overcome the limitations of existing techniques, a fine-grain and application-specific access control mechanism is proposed. The solution provides users with the ability to enforce custom security policies to safeguard their data.

Lately Unmanned Aerial Systems (UAS) are used more frequently in surveying. With broader use comes higher demands on the uncertainty in such measurements. The post processing software is an important factor that affects the uncertainty in the finished product. Therefore it is vital to evaluate how results differentiate in different software and how parameters contribute. In UAS-photogrammetry images are acquired with an overlap which makes it possible to generate point clouds in photogrammetric software. These point clouds are often used to create Digital Terrain Models (DTM).  The purpose of this study is to evaluate how the level of uncertainty differentiates when processing the same UAS-data through block adjustment and dense image matching in two different photogrammetric post processing software. The software used are UAS Master and Pix4D. The objective is also to investigate how the level of extraction in UAS Master and the setting for image scale in Pix4D affects the results when generating point clouds. Three terrain models were created in both software using the same set of data, changing only extraction level and image scale in UAS Master and Pix4D respectively.  26 control profiles were measured with network-RTK in the area of interest to calculate the root mean square (RMS) and mean deviation in order to verify and compare the uncertainty of the terrain models. The study shows that results vary when processing the same UAS-data in different software.  The study also shows that the extraction level in UAS Master and the image scale in Pix4D impacts the results differently. In UAS Master the uncertainty decreases with higher extraction level when generating terrain models. A clear pattern regarding the image scale setting in Pix4D cannot be determined. Both software were able to produce elevation models with a RMS-value of around 0,03 m. The mean deviation in all models created in this study were below 0,02 m, which is the requirement for class 1 in the technical specification SIS-TS 21144:2016. However the mean deviation for the ground type gravel in the terrain model created in UAS Master at a low extraction level exceeds the demands for class 1. This indicates all but one of the created models fulfil the requirements for class 1, which is the class containing the highest requirements. / Obemannade flygfarkostsystem (eng. Unmanned Aerial Systems, UAS) används allt mer frekvent för datainsamling inom geodetisk mätning. I takt med att användningsområdena ökar ställs också högre krav på mätosäkerheten i dessa mätningar. De efterbearbetningsprogram som används är en faktor som påverkar mätosäkerheten i den slutgiltiga produkten. Det är därför viktigt att utvärdera hur olika programvaror påverkar slutresultatet och hur valda parametrar spelar in. I UAS-fotogrammetri tas bilder med övertäckning för att kunna generera punktmoln som i sin tur kan bearbetas till digitala terrängmodeller (DTM).  Syftet med studien är att utvärdera hur mätosäkerheten skiljer sig när samma data bearbetas genom blockutjämning och tät bildmatchning i två olika programvaror. Programvarorna som används i studien är UAS Master och Pix4D. Målet är också att utreda hur vald extraktions nivå i UAS Master och vald bildskala i Pix4D påverkar resultatet vid generering av terrängmodeller. Tre terrängmodeller skapades i UAS Master med olika extraktionsnivåer och ytterligare tre skapades i Pix4D med olika bildskalor. 26 kontrollprofiler mättes in med nätverks-RTK i aktuellt område för beräkning av medelavvikelse och kvadratiskt medelvärde (RMS). Detta för att kunna verifiera och jämföra mätosäkerheten i modellerna. Studien visar att slutresultatet varierar när samma data bearbetas i olika programvaror.  Studien visar också att vald extraktionsnivå i UAS Master och vald bildskala i Pix4D påverkar resultatet olika. I UAS Master minskar mätosäkerheten med ökad extraktionsnivå, i Pix4D är det svårare att se ett tydligt mönster. Båda programvaror kunde producera terrängmodeller med ett RMS-värde kring 0,03 m. Medelavvikelsen i samtliga modeller understiger 0,02 m, vilket är kravet för klass 1 från den tekniska specifikationen SIS-TS 21144:2016. Medelavvikelsen för marktypen grus i UAS Master i modellen med låg extraktionsnivå överskrider dock kraven för klass 1. Därmed uppnår alla förutom en av terrängmodellerna kraven för klass 1, vilket är den klass med högst ställda krav.

Master of Science / Department of Agronomy / Antonio R. Asebedo / Current population trends project that current agricultural production will need to increase by 110% by the year 2050 to support the growing worldwide population. Many agriculturalists are looking at precision agriculture technology to help achieve this production increase. One technology that is being heavily researched is the integration of small unmanned aerial systems (sUAS) and their sensors into the agricultural sector. Much research has already been conducted in the agronomic sector utilizing sUAS. However, relatively few advancements involving sUAS have been made in the animal science industry. This thesis focuses on how sUAS can be incorporated into a diverse cropping and livestock operation. Chapter 1 - Evaluating Current Capabilities of sUAS and sUAS Mounted Sensors in Diverse Agricultural Operation: A Literature Review, focuses on the current capabilities of sUAS and explains how they are incorporated into cropping systems and livestock production. Chapter 2 - Wheat Variety Interaction on Multispectral Based Vegetative Indices, focuses on wheat variety interaction with yield, grain protein content, NDVI, NDRE and CCCI. Ten wheat varieties were tested in large plot studies; yield, protein and multispectral data were collected for Feekes 4, 7, 10 and 10.5. Wheat variety was statically significant across all vegetative indices, protein and yield during less than favorable growing conditions. Chapter 3 - Estimating Cattle Rectal Temperature Using Thermography, focuses on determining the ideal location for thermographic readings to be taken to predict rectal temperature of beef cattle. To establish if sUAS-based thermography could be utilized for cattle rectal temperature estimation, 35 crossbreed steers were selected and thermographic readings and rectal temperature data were collected and analyzed for correlation. This study found stronger relation in the animal’s eye than other facial features.

The well arranged and instructivee business plan is the basic part of every business project.. Currently is very often used the lean canvas model of the business plan. This model shows us very istructively all the factors influencing the project realisation. The dissertation is divided into the two basic parts. In the thoretical were compared the essential forms of the business plan with their differences. The practical part consists of creating the lean canvas model for the progressive company using the UAV and the MAIA application, monitoring the flight of the UAV. The economic data were analyzed and the structured scheme of the project, with the prediction was realized. The lean canvas model was very useful and optimal for this company and it´s project.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / At present, topographic surveys in road projects are very important, since it allows us to know the conditions and variable characteristics of the area to be executed. Commonly these surveys are carried out using a total station, high precision equipment, which in turn generates a lot of demand for time in the field and a greater number of personnel for a wide study area. For this reason, another alternative for obtaining terrain data is the use of the photogrammetric technique with UAV, since the objective is to increase productivity in the topographic survey. In this article, the productive development generated in the crews was analyzed using the total station survey methodology and the UAV photogrammetric technique using balance charts with the aim of obtaining the productive, contributory and non-contributory works, since the latter two they do not add value to the development of the project. This research was carried out in a rural area with a study area of 3 Ha and its classification by orography is of type 2 soil (undulated terrain), located in the Cuculí town center, Chongoyape district, Chiclayo province in the department of Lambayeque, Peru. In order to demonstrate that the UAV survey reduces non-contributory work by 47.2%, among the most outstanding activities obtained in this research are the leisure time that the staff presents in different situations of the project development, care of topographic equipment and another of the most outstanding was the use of cell phones in the field, which do not provide efficiency in the development of data collection and therefore would increase in time and cost of linear works, sanitation and projects in open places in the stage of design engineering.

As UASs (Uncrewed Aerial System) are further integrated into operations, the need for on-board environmental perception and sensing is necessitated. An accurate and reliable creation of a 3D map resembling an aircraft's surrounding is crucial for accurate collision avoidance and path planning. Consumer UASs are now being equipped with sensors to fulfill such a requirement – but no system has been proven as capable of being fully relied upon. With many sensing options available, there are several constraints regarding size, weight, and cost that must be considered when developing a sensing solution. Additionally, the robustness of the system must not be diminished when moving to a system that minimizes size, weight, or cost. An analysis of different sensing technologies that small rotary-wing aircraft (below 25kg) can be outfitted with for collision avoidance is performed. Several sensing technologies are initially compared through technology analyses and controlled experiments. The topmost systems were then further integrated onto a small low-cost quadcopter for flight testing and data acquisition. Ultimately, a fusion between stereo vision imagery and radar was deemed the most reliable method for providing environmental data needed for collision avoidance. / Master of Science / As drones become further integrated in several industries, it is important that their operations are conducted in a safe manner. Most drones today have a limited ability to sense and react to the environment around them. This results in the risk of the drone colliding with people or obstacles such as buildings, trees, light poles, etc. Thus, an accurate and reliable creation of a 3D map resembling a drone's surrounding is crucial for collision avoidance. This would allow for the avoidance of people and obstacles during automated flights where the drone may encounter obstacles during flight. With many sensing options available, there are several constraints regarding size, weight, and cost that must be considered when developing a sensing solution. Additionally, the reliability of the system must be of the topmost priority to ensure safe operations. An analysis of different sensing technologies that small rotary-wing aircraft (below 25kg) can be outfitted with for collision avoidance is performed. Rotary-wing aircraft are a specific subset of drones that are capable of vertical takeoff, landing, and hover (i.e not planes). An analysis regarding the several sensing technologies was first conducted to select the three topmost solutions. These solutions were then integrated onto a small low-cost quadcopter for data collection and flight testing. Ultimately, a combination of stereo vision imagery and radar was deemed the most reliable method for collecting collision avoidance data.

With the increasing number of small Uncrewed Aerial Systems (sUAS) in the airspace, the need for robust Detect and Avoid (DAA) technologies is clear. This is especially true when considering the potential for non-cooperative aircraft with unknown intent. Many UAS use high resolution cameras to perform omnidirectional scans of their nearby airspace to localize traffic. These scans can be quite computationally expensive and often necessitate the use of costly and heavy hardware components. Ground-based solutions such as centralized, stationary towers are often expensive, difficult to proliferate, and have the disadvantage of not being onboard the aircraft and as such not always local to the airspace conflict. A feasibility exploration of acoustic detection and localization of non-cooperative aircraft using a low-cost microphone array, computationally inexpensive beamforming algorithms, and filtering techniques, is performed. The cost of the system is minimized by utilizing widely proliferated microphone hardware originally designed for short-range voice detection, as well as a small Uncrewed Aerial Systems (sUAS) from a developmental kit. Lastly, an exploration is conducted to maximize the detection range of the microphone system. A comparison of filtering techniques to try to filter sUAS self-noise is compared to alternative methods such as a ballistic sampling period where the motors of the sUAS are momentarily turned off to reduce noise. A final recommendation of a multi-sensor suite of microphones, cameras, along with other potential sensors, is determined. / Master of Science / As the number of drones increases throughout many industries, safe usage becomes very important. Industries such as search and rescue, infrastructure surveying, package delivery, and more, all have novel uses for drones that could change the way those industries operate. It is easy to imagine the benefit of same-day shipping with package-carrying drones, the quick location of a missing person by a search and rescue drone, and so on. However, obstacles such as buildings, trees, and other air traffic pose an obvious risk. Current methods to detect other aircraft often rely on cameras onboard the aircraft to spot nearby traffic. Other methods include using centralized stations on the ground to relay information about positioning between cooperating aircraft. These technologies provide functionality, but often can be expensive, heavy, require computers with large processing power, or assume the cooperation of the aircraft. An analysis of audio based detection of nearby drones is conducted. The microphones used were originally intended for use in home applications as a voice assistant. Programming techniques were used to listen and identify the sound of a nearby drone. Depending on the location of the drone, its sound would arrive to the microphones in unique time delays, providing a method of estimating the drone's position. Testing was performed on the ground and in the air to analyze the distance at which this microphone group could find a drone. Ultimately, a recommendation for the inclusion of microphones in a suite of sensors was made.

Disaster damage assessments are a critical component to response and recovery operations. In recent years, the field of remote sensing has seen innovations in automated damage assessments and UAS collection capabilities. However, little work has been done to explore the intersection of automated methods and UAS photogrammetry to detect tornado damage. UAS imagery, combined with Structure from Motion (SfM) output, can directly be used to train models to detect tornado damage. In this research, we develop a CNN that can classify tornado damage in forests using SfM-derived orthophotos and digital surface models. The findings indicate that a CNN approach provides a higher accuracy than random forest classification, and that DSM-based derivatives add predictive value over the use of the orthophoto mosaic alone. This method has the potential to fill a gap in tornado damage assessment, as tornadoes that occur in wooded areas are typically difficult to survey on the ground and in the field; an improved record of tornado damage in these areas will improve our understanding of tornado climatology. / Master of Science / Disaster damage assessments are a critical component to response and recovery operations. In recent years, the field of remote sensing has seen innovations in automated damage assessments and Unmanned Aerial System (UAS) collection capabilities. However, little work has been done to explore the intersection of automated methods and UAS imagery to detect tornado damage. UAS imagery, combined with 3D models, can directly be used to train machine learning models to automatically detect tornado damage. In this research, we develop a machine learning model that can classify tornado damage in forests using UAS imagery and 3D derivatives. The findings indicate that the machine learning model approach provides a higher accuracy than traditional techniques. In addition, the 3D derivatives add value over the use of only the UAS imagery. This method has the potential to fill a gap in tornado damage assessment, as tornadoes that occur in wooded areas are typically difficult to survey on the ground and in the field; an improved record of tornado damage in these areas will improve our understanding of tornado climatology.