The purpose of the study was to compile a map of Furuvik theme park using UAS-photogrammetry and evaluate the products quality by performing a map control. The map control is carried out with guidelines from new and old HMK-documents and how such an evaluation is carried out in our neighbouring countries. At the same time, a time study was carried out on the project's workflow as a request from the University of Gävle (HiG) for a future Master's degree program in Land Surveying. The flight was carried out with a multicopter from Altigator. Prior to the flights, flight signals were placed and as well as, known points (stompunkter), were measured with SWEPOS network-RTK (real-time kinematic). The flight resulted in 1036 images, which in PhotoScan were joined together by block adjustment and generated an orthophotomosaic and a digital elevation model were generated. In ArcMap, from the orthomosaic, a map was produced, which was then controlled using measured control points. The results in the plan points show that the difference between objects in the produced map and their known coordinates varies radially between 0.0014 m and 0.029 m. The mean deviation is 0.009 m with the standard uncertainty (Sp) 0.014 m and the root mean square (RMS) 0,014 m. All requirements in HMK-Geodatakvalitet (Geodata Quality), HMK-Flygfotografering (Aerial Photography), HMK-Kartografi (Cartography), and similar documents from the Norwegian and Finnish national land survey were fulfilled. The requirements of the newer HMK documents on geodata quality and aerial photography are reasonable while HMK cartography needs updating as the requirements are too low, 0.07 m To control the height model, 18 control profiles were measured in according to the Swedish technical specification SIS-TS 21144: 2016. RMS in height for the entire area was 0.032 m. The duration of the study's implementation was documented to produce a time study that resulted in 374 hours of work during nine weeks. / Syftet med studien var att med hjälp av UAS-fotogrammetri framställa en karta över Furuviks nöjespark och utvärdera produktens kvalitet i form av en kartkontroll. Kartkontrollen genomfördes med riktlinjer från nya och gamla HMK-dokument samt hur en sådan utvärdering utförs i våra grannländer. Samtidigt utfördes en tidsstudie över projektets arbetsgång som ett önskemål från Högskolan i Gävle (HiG) för ett framtida civilingenjörsprogram inom lantmäteriteknik. Flygningen genomfördes med en multikopter från Altigator. Inför flygningarna placerades flygsignaler ut som liksom stompunkter mättes in med SWEPOS nätverks-RTK (real time kinematic). Flygningen resulterade i 1036 bilder som fogades samman i PhotoScan genom blockutjämning och genererade en ortotfotomosaik samt en markmodell. I ArcMap framställdes, ur ortofotomosaiken, en karta som sedan kontrollerades med hjälp av inmätta markpunkter i form av stickprov. Resultatet i plan av stickproven visar att skillnaden mellan objekt i den producerade kartan och motsvarande objekt inmätta i området varierar radiellt mellan 0,0014 m och 0,029 m. Medelavvikelsen radiellt är 0,014 m med standardosäkerheten (Sp) 0,014 m. Samtliga krav i HMK-Geodatakvalitet, HMK-Flygfotografering, HMK-Kartografi samt norska och finska styrdokument uppfylldes. Kraven i de nyare HMK-dokumenten om geodatakvalitet och flygfotografering har följt den tekniska utvecklingen medans HMK-Kartografi behöver uppdateras då kraven är för låga, 0,07 m. För att kontrollera markmodellen mättes 18 kontrollprofiler in i enlighet med den tekniska specifikationen SIS-TS 21144:2016. Standardosäkerheten i höjd för hela området resulterade i 0,032 m. Tidsåtgången för studiens genomförande dokumenterades för att framställa en tidsstudie som resulterade i 374 arbetstimmar under nio veckor.

三維城市模型之建置需求漸趨繁多，可提供都市規劃、城市導航及虛擬實境等相關應用，過去研究多以建置LOD2城市模型為主，且較著重於屋頂結構。近年來，逐漸利用垂直影像及傾斜影像作為原始資料，提供建物牆面之建置，並且，隨著無人機系統(Unmanned Aircraft System, UAS）發展，可利用其蒐集高解析度且高重疊垂直及傾斜拍攝之建物影像，並採影像密匹配技術產製高密度點雲，進而快速取得建物包含屋頂及牆面之三維資訊，而這些資訊可進一步提供後續建置LOD3建置層級之模型，而在建置前，首先須對資料進行特徵分析，萃取特徵點、線、面，進而提供建置模型所需之資訊。 因此，本研究期望利用密匹配點雲，計算其點雲特徵，並採用Mean Shift分群法（Comaniciu and Meer, 2002）萃取建物點雲資訊，並提供一最佳分群策略。首先，本研究將以UAS為載具，設計一野外率定場率定相機，並蒐集建物高重疊UAS影像密匹配產製高密度點雲，針對單棟建物高密度點雲，實驗測試點雲疏化程度後，依據疏化成果計算點雲特徵，並以此批點雲資料實驗測試Mean shift分群法（Cheng, 1995）中之參數，後設計分群流程以分離平面點群及曲面點群，探討分群成果以決定最佳分群策略。實驗結果顯示本研究提出之分群策略，可自動區分平面點群及曲面點群，並單獨將平面點群分群至各牆面。 / Unmanned Aerial System (UAS) offer several new possibilities in a wide range of applications. One example is the 3D reconstruction of buildings. In former times this was either restricted by earthbound vehicles to the reconstruction of facades or by air-borne sensors to generate only very coarse building models. UAS are able to observe the whole 3D scene and to capture images of the object of interest from completely different perspectives. Therefore, this study will use UAS to collected images of buildings and to generate point cloud from dense image matching for modeling buildings. In the proposed approach, this method computes principal orientations by PCA and identifies clusters by Mean shift clustering. Analyze the factors which can affect the clustering methods and try to decrease the use of threshold, and this result can cluster the façade of buildings automatically and offer the after building reconstruction for LOD3.

<p dir="ltr">Efficient management of plant diseases and their spread within fields requires a system capable of early and accurate disease identification and its severity estimation. Many plant diseases have distinct visual symptoms, which can be used to correctly identify, classify, and manage them. Recent technological advancements have led to increased adoption of deep neural networks (DNN) for developing deep learning (DL)-based computer vision systems. An accurate disease identification and severity estimation system using a DL-based computer vision framework is critical for efficiently managing corn diseases under field conditions and further restricting the spread of disease. Image processing and machine learning methods for disease identification and classification have been employed in the last two decades using high-cost sensors that need frequent calibration. Researchers have used low-cost red, green, and blue (RGB) sensors to mostly identify single diseases affecting crops, whereas, in real-world applications, a single leaf can be affected by multiple diseases. This research identifies gaps in knowledge of DL applications to field crops by reviewing 70 research articles published between 1983 and 2022. It creates a much-needed disease database for corn grown under field conditions by adding custom-acquired image data to other publicly available image repositories. The image data was used to train and evaluate the performance of commonly used DL-based image classification models for differentiating single diseases on individual corn leaves under field conditions. However, many disease lesions of different shapes and sizes can simultaneously develop on infected leaves. The performance of DL-based image classification and object detection models was evaluated to accurately identify multiple simultaneous diseases with varying symptoms. Disease identification under field conditions is necessary to implement an effective disease management system. However, recent work has demonstrated poor generalization accuracies of DL models trained on lab-acquired imagery for identifying diseases in the field. Therefore, after achieving promising results for disease identification, DL generalization performance was assessed and improved using different dataset combinations with varying backgrounds. A novel neural network architecture using a hierarchical structure was also proposed, which resulted in improved generalization performance. Additionally, disease severity must be estimated to implement an effective management response. DL models were evaluated to estimate the severity of multiple corn diseases under field conditions using aerial and ground-based platforms to identify specific lesions from above and below the canopy. A progressive web application was designed to empower end users with disease recognition capabilities. Overall, this research reports findings of the performance of deep learning image processing, object detection, and segmentation models for identifying single/multiple diseases on field corn and the development of tools that can potentially be a component of production-ready disease diagnosis systems for implementing effective management practices.</p>