De virvelgator som bildas i atmosfären bakom höga berg på öar påminner till utseendet starkt om de periodiska flöden som uppstår vid strömning kring en cirkulär cylinder. Friktionen mellan fluiden och cylinderns yta gör att det bildas en vak nedströms cylindern. Periodisk virvelspridning där von Kármán-virvlar sänds ut kan uppstå. Utifrån Reynolds tal går det att karaktärisera strömningen och med hjälp av en numerisk modell kan tillstånden simuleras. Saknas en turbulensmodell i algoritmen blir resultaten för höga Reynolds tal felaktiga. De atmosfäriska virvelgatorna uppstår dock inte på grund av friktion. Istället krävs blockering av luftmassor och variationer i densitet för att virvlarna ska utvecklas. För att dra slutsatser om de atmosfäriska virvelgatorna har 11 satellitbilder med virvelgator analyserats. Sambandet λ = 3.9b-5.3 (förklaringsgrad r2 = 0.91) hittades mellan virvelgatans våglängd λ och bredden b på ön. Kvoten λ/b beräknades till medelvärdet 4.33 vilket är jämförbart med resultat från en liknande studie. / The visual appearance of the atmospheric vortex street behind a high mountain on an island is very similar to the periodic pattern caused by the flow past a circular cylinder. The friction between the fluid and the surface of the cylinder creates a wake downstream of the cylinder and periodic von Kármán vortex shedding occurs. The flow may be characterized by means of the Reynolds number and using a numerical model the different states can be simulated. If the algorithm lacks a turbulence model, the results for high Reynolds numbers will be wrong. The atmospheric vortex streets do not, however, arise due to friction. Instead, blocking of air masses and density variations are needed for the vortices to develop. To be able to draw conclusions about atmospheric vortex streets 11 satellite images showing the vortex streets have been analyzed. The relation λ = 3.9b-5.3 (coefficient of determination r2 = 0.91) was found, where λ is the wavelength of the vortex street and b the width of the island. The mean value of the ratio λ/b is 4.33 which is comparable with results from a similar study.

Satellite remote sensing has revolutionized the acquisition of large amounts of data, employing both active and passive sensors to capture critical information about our planet. These data can be analysed by using deep learning methodologies that demonstrate excellent capabilities in extracting the semantics from the data. However, one of the main challenges in exploiting the power of deep learning for remote sensing applications is the lack of labeled training data. Deep learning architectures, typically demand substantial quantities of training samples to achieve optimal performance. Motivated by the above-mentioned challenges, this thesis focuses on the limited availability of labeled datasets. These challenges include issues such as ambiguous labels in case of large-scale remote sensing datasets, particularly when dealing with the analysis of multi-source satellite remote sensing images. By employing novel deep learning techniques and cutting-edge methodologies, this thesis endeavors to contribute to advancements in the field of remote sensing. In this thesis, the problems related to limited labels are solved in several ways by developing (i) a novel spectral index generative adversarial network to augment real training samples for generating class-specific remote sensing data to provide a large number of labeled samples to train a neural-network classifier; (ii) a mono- and dual-regulated contractive-expansive-contractive convolutional neural network architecture to incorporate spatial-spectral information of multispectral data and minimize the loss in the feature maps and extends this approach to the analysis of hyperspectral images; (iii) a hybrid deep learning architecture with a discrete wavelet transform and attention mechanism to deal with few labeled samples for scene-based classification of multispectral images; and (iv) a weak supervised semantic learning technique that utilises weak or low-resolution labeled samples with multisource remote sensing images for predicting pixel-wise land-use-land-cover maps. The experiments show that the proposed approaches perform better than the state-of-the-art methods on different benchmark datasets and in different conditions.

Die vorliegende Bachelorarbeit ergänzt das bereits existierende nicht-photorealistische 3D-Landschaftsmodell im 'Ethno-Nature Park Uch-Enmek' nach Osten. Zentrum der durchgeführten Modellierungsarbeiten im zwei- und dreidimensionalen Raum ist die Siedlung Karakol. Der existente Workflow - von den Primärdaten bis zum 3D-Modell - wird unabhängig getestet und auf Verbesserungsmöglichkeiten untersucht. Die Schwerpunkte liegen dabei auf der Eignung der existenten Quellen für die Modellierung, der Eignung bisher geschaffener Modellierungswerkzeuge sowie der Quantifizierung des Erfassungsaufwands.

Saltmarshes provide a diverse range of ecosystem services and are protected under a number of international designations. Nevertheless they are generally declining in extent in the United Kingdom and North West Europe. The drivers of this decline are complex and poorly understood. When considering mitigation and management for future ecosystem service provision it will be important to understand why, where, and to what extent decline is likely to occur. Few studies have attempted to forecast saltmarsh morphodynamics at a system level over decadal time scales. There is no synthesis of existing knowledge available for specific site predictions nor is there a formalised framework for individual site assessment and management. This project evaluates the extent to which machine learning model approaches (boosted regression trees, neural networks and Bayesian networks) can facilitate synthesis of information and prediction of decadal-scale morphological tendencies of saltmarshes. Importantly, data-driven predictions are independent of the assumptions underlying physically-based models, and therefore offer an additional opportunity to crossvalidate between two paradigms. Marsh margins and interiors are both considered but are treated separately since they are regarded as being sensitive to different process suites. The study therefore identifies factors likely to control morphological trajectories and develops geospatial methodologies to derive proxy measures relating to controls or processes. These metrics are developed at a high spatial density in the order of tens of metres allowing for the resolution of fine-scale behavioural differences. Conventional statistical approaches, as have been previously adopted, are applied to the dataset to assess consistency with previous findings, with some agreement being found. The data are subsequently used to train and compare three types of machine learning model. Boosted regression trees outperform the other two methods in this context. The resulting models are able to explain more than 95% of the variance in marginal changes and 91% for internal dynamics. Models are selected based on validation performance and are then queried with realistic future scenarios which represent altered input conditions that may arise as a consequence of future environmental change. Responses to these scenarios are evaluated, suggesting system sensitivity to all scenarios tested and offering a high degree of spatial detail in responses. While mechanistic interpretation of some responses is challenging, process-based justifications are offered for many of the observed behaviours, providing confidence that the results are realistic. The work demonstrates a potentially powerful alternative (and complement) to current morphodynamic models that can be applied over large areas with relative ease, compared to numerical implementations. Powerful analyses with broad scope are now available to the field of coastal geomorphology through the combination of spatial data streams and machine learning. Such methods are shown to be of great potential value in support of applied management and monitoring interventions.