Idag blir användningen av drönare allt mer vanlig för dokumentation av markytor. Det är ett billigare alternativ för att dokumentera små och otillgängliga områden. Genom tekniken går det bland annat att framställa olika digitala modeller som representerar jordens yta. En sådan modell kan vara en terrängmodell (DTM) som är en modell av markytan exklusive vegetation, hus eller annat som befinner sig på marken. Modeller kan framställas genom flygdata såsom laserskannad (LiDAR-data) eller flygfotograferade data (flygbilder). För att framställa en digital modell från rådata används olika programvaror. Den här studien utvärderar två olika programvarors förmåga att framställa digitala terrängmodeller från flygbilder. Främst undersöks levererade osäkerheter och användarvänligheten i programmen. Referensdata som användes i denna studie tillhandahölls av Norconsult och samlades in vid ett projekt över Hammarbyhöjdsskogen i Stockholm, hösten 2018. Den data som erhölls från projektet till denna studie var flygbilder samt terrestra detaljmätningar. Programmen som studien utvärderar är UAS Master som både använder datorseende och fotogrammetriska metoder och SURE Aerial som använder datorseende. Genom studien visade det sig att fler än de ursprungliga programvarorna behövdes för att framställa de digitala terrängmodellerna och vidare jämföra dessa. En orsak var att UAS Master saknade förmågor att redigera och visa punktmoln i 3D-vy och vidare skapa en DTM. Detta resulterade i att använda Trimble Business Center för slutarbetet. En annan orsak var att SURE Aerial visade sig vara avsett för framställning av digitala ytmodeller (representation av den faktiska, synliga ytan). För att framställa en DTM av punktmolnet användes både Cloud Compare och Agisoft Photoscan (numera Metashape). Geo användes sedan för att ta ut höjdavvikelserna från modellen. Två slutsatser som kunde dras utifrån denna studie var: 1) trots de olika tillvägagångssätten erhölls snarlika resultat för marktypernas lägesosäkerheter för respektive programvara (asfalt: 0,039 m; grus: ca 0,040 m; gräs: ca 0,048 m), varpå alla blev godkända enligt HMK – Flygfotografering 2017; 2) SURE Aerial är ett enklare och snabbbare program men med UAS Master har man som användare bättre förståelse över processerna och erhåller bättre dokumentation. / Drones have become a more and more frequent tool to document the surface of the ground, especially in smaller areas that otherwise are too expensive to observe by other means. This technology makes it possible to create digital terrain models (DTM) that represents the surface of the ground excluding vegetation, houses or other objects on the ground. These models can be created by laser scanned data (LiDAR-data) or aerial photogrammetry (aerial photos).  In order to create a digital model from raw data are various software needed. This study aims to test two software’s ability to create digital terrain models from UAS photos. The software were evaluated by the uncertainties of the models, as well as the user-friendliness of each software. All data used in this study was collected by Norconsult for another project in 2018 and consist of UAS photos and data from terrestrial measurements.  The softwares used in this study for comparison are UAS Master (using both computer vision and photogrammetric methods) and SURE Aerial (using computer vision). It turned out that additional use of software were needed to create DTMs that were comparable. UAS Master could not show or edit point clouds in 3D, because of this the software Trimble Business Centre had to be used. This program was also used to obtain height deviations. SURE Aerial on the other hand turned out to only be able to create digital surface models (models of the visible ground). The software Cloud Compare and Agisoft Photoscan (nowadays Metashape) were therefore used to create the DTM from the point cloud. The height deviations from the ladder DTM were obtained from the software Geo. Two conclusions could be drawn from this study: 1) the uncertainties of the different surface types were similar in the software despite the different ways to create the DTMs (asphalt: 0.039 m; gravel: 0.040 m; grass: 0.048 m). All of which meet the requirements according to HMK – Flygfotografering 2017; 2) SURE Aerial is a lot easier and quicker to work with but UAS Master give the user a lot more feedback in the way of documentation throughout the different processes.

<p>Emerging mobile mapping systems include a wide range of platforms, for instance, manned aircraft, unmanned aerial vehicles (UAV), terrestrial systems like trucks, tractors, robots, and backpacks, that can carry multiple sensors including LiDAR scanners, cameras, and georeferencing units. Such systems can maneuver in the field to quickly collect high-resolution data, capturing detailed information over an area of interest. With the increased volume and distinct characteristics of the data collected, practical quality control procedures that assess the agreement within/among datasets acquired by various sensors/systems at different times are crucial for accurate, robust interpretation. Moreover, the ability to derive semantic information from acquired data is the key to leveraging the complementary information captured by mobile mapping systems for diverse applications. This dissertation addresses these challenges for different systems (airborne and terrestrial), environments (urban and rural), and applications (agriculture, archaeology, hydraulics/hydrology, and transportation).</p> <p>In this dissertation, quality control procedures that utilize features automatically identified and extracted from acquired data are developed to evaluate the relative accuracy between multiple datasets. The proposed procedures do not rely on manually deployed ground control points or targets and can handle challenging environments such as coastal areas or agricultural fields. Moreover, considering the varying characteristics of acquired data, this dissertation improves several data processing/analysis techniques essential for meeting the needs of various applications. An existing ground filtering algorithm is modified to deal with variation in point density; digital surface model (DSM) smoothing and seamline control techniques are proposed for improving the orthophoto quality in agricultural fields. Finally, this dissertation derives semantic information for diverse applications, including 1) shoreline retreat quantification, 2) automated row/alley detection for plant phenotyping, 3) enhancement of orthophoto quality for tassel/panicle detection, and 4) point cloud semantic segmentation for mapping transportation corridors. The proposed approaches are tested using multiple datasets from UAV and wheel-based mobile mapping systems. Experimental results verify that the proposed approaches can effectively assess the data quality and provide reliable interpretation. This dissertation highlights the potential of modern mobile mapping systems to map challenging environments for a variety of applications.</p>