Today streaming services are the most popular way to consume music, and with this the field of Music Information Retrieval (MIR) has exploded. Tangy market is a music investment platform and they want to use MIR techniques to estimate the value of not yet released songs. In this thesis we collaborate with them investigating how a song’s financial success can be predicted using machine learning models. Previous research has shown that well-known algorithms used for tasks such as image recognition and machine translation, also can be used for audio analysis and prediction. We show that a lot of previous work has been done regarding different aspects of audio analysis and prediction, but that most of that work has been related to genre classification and hit song prediction. The popularity prediction of audio is still quite new and this is where we will contribute by researching if low-level audio features can be used to predict streams. We are using an existing dataset with more than 100 000 songs containing low-level features, which we extend with streaming information. We are using the features in two shapes, summarized and full, and the dataset only contains the summarized digital representation of features. We use Librosa to extend the dataset to also have the digital representation of the full version for the audio features.  A previous study by Martín-Gutiérrez et al. [1] successfully used a combination of low-level and high level audio features as well as non musical features such as number of social media followers. The aim of this thesis is to explore five of the low-level features used in a previous study in [1] in order to assess the predictive power that these features have on their own. The five features we explore is; Chromagram, Mel Spectrogram, Tonnetz, Spectral Contrast, and MFCC. These features are selected for our research specifically because they were used in [1], and we want to investigate to what extent these low-level features contribute to the final predictions made by their model. Our conclusion is that neither of these features could be used for prediction with any accuracy, which indicates that other high-level and external features are of more importance. However, Chromagram and Mel Spectrogram in their full feature states show some potential but they will need to be researched more.

Deep neural networks (DNNs) are receiving immense attention because of their ability to solve complex problems. However, running a DNN requires a very large number of computations. Hence, dedicated hardware optimized for running deep learning algorithms known as neuromorphic architectures is often utilized. This dissertation focuses on evaluating andenhancing the accuracy of these neuromorphic architectures considering the designs of components, process variations, and adversarial attacks. The first contribution of the dissertation (Chapter 2) proposes design enhancements in analog Memristive Crossbar Array(MCA)-based neuromorphic architectures to improve classification accuracy. It introduces an analog Winner-Take-All (WTA) architecture and an on-chip training architecture. WTA ensures that the classification of the analog MCA is correct at the final selection level and the highest probability is selected. In particular, this dissertation presents a design of a highly scalable and precise current-mode WTA circuit with digital address generation. The design is based on current mirrors and comparators that use the cross-coupled latch structure. A post-silicon calibration circuit is also presented to handle process variations. On-chip training ensures that there is consistency in classification accuracy among different all analog MCA-based neuromorphic chips. Finally, an enhancement to the analog on-chip training architecture by implementing the Convolutional Neural Network (CNN) on MCA and software considerations to accelerate the training is presented.The second focus of the dissertation (Chapter 3) is on producing correct classification in the presence of malicious inputs known as adversarial attacks. This dissertation shows that MCA-based neuromorphic architectures ensure correct classification when the input is compromised using existing adversarial attack models. Furthermore, it shows that adversarialrobustness can be further improved by compression-based preprocessing steps that can be implemented on MCAs. It also evaluates the impact of the architecture in Chapter 2 under adversarial attacks. It shows that adversarial attacks do not uniformly affect the classification accuracy of different MCA-based chips. Experimental evidence using a variety of datasets and attack models supports the impact of MCA-based neuromorphic architectures and compression-based preprocessing implemented on MCAs to mitigate adversarial attacks. It is also experimentally shown that the on-chip training improves consistency in mitigating adversarial attacks among different chips. The final contribution (Chapter 4) of this dissertation introduces an enhancement of the method in Chapter 3. It consists of input preprocessing using compression and subsequent rescale and rearrange operations that are implemented using MCAs. This approach further improves the robustness against adversarial attacks. The rescale and rearrange operations are implemented using a DNN consisting of fully connected and convolutional layers. Experimental results show improved defense compared to similar input preprocessing techniques on MCAs.

<p>  </p> <p>The maintenance of a healthy sewer infrastructure is a major challenge due to the root damages from nearby plants that grow through pipe cracks or loose joints, which may lead to serious pipe blockages and collapse. Traditional inspections based on video surveillance to identify and localize root damages within such complex sewer networks are inefficient, laborious, and error-prone. Therefore, this study aims to develop a robust and efficient approach to automatically detect root damages and localize their circumferential and longitudinal positions in CCTV inspection videos by applying deep neural networks and computer vision techniques. With twenty inspection videos collected from various resources, keyframes were extracted from each video according to the difference in a LUV color space with certain selections of local maxima. To recognize distance information from video subtitles, OCR models such as Tesseract and CRNN-CTC were implemented and led to a 90% of recognition accuracy. In addition, a pre-trained segmentation model was applied to detect root damages, but it also found many false positive predictions. By applying a well-tuned YoloV3 model on the detection of pipe joints leveraging the Convex Hull Overlap (<em>CHO</em>) feature, we were able to achieve a 20% improvement on the reliability and accuracy of damage identifications. Moreover, an end-to-end deep learning pipeline that involved Triangle Similarity Theorem (<em>TST</em>) was successfully designed to predict the longitudinal position of each identified root damage. The prediction error was less than 1.0 feet. </p>

Indiana University-Purdue University Indianapolis (IUPUI) / The maintenance of a healthy sewer infrastructure is a major challenge due to the root damages from nearby plants that grow through pipe cracks or loose joints, which may lead to serious pipe blockages and collapse. Traditional inspections based on video surveillance to identify and localize root damages within such complex sewer networks are inefficient, laborious, and error-prone. Therefore, this study aims to develop a robust and efficient approach to automatically detect root damages and localize their circumferential and longitudinal positions in CCTV inspection videos by applying deep neural networks and computer vision techniques. With twenty inspection videos collected from various resources, keyframes were extracted from each video according to the difference in a LUV color space with certain selections of local maxima. To recognize distance information from video subtitles, OCR models such as Tesseract and CRNN-CTC were implemented and led to a 90% of recognition accuracy. In addition, a pre-trained segmentation model was applied to detect root damages, but it also found many false positive predictions. By applying a well-tuned YoloV3 model on the detection of pipe joints leveraging the Convex Hull Overlap (CHO) feature, we were able to achieve a 20% improvement on the reliability and accuracy of damage identifications. Moreover, an end-to-end deep learning pipeline that involved Triangle Similarity Theorem (TST) was successfully designed to predict the longitudinal position of each identified root damage. The prediction error was less than 1.0 feet.

In recent years, great progress has been made in the area of Artificial Intelligence (AI) due to the possibilities of Deep Learning which steadily yielded new state-of-the-art results especially in many image recognition tasks. Currently, in some areas, human performance is achieved or already exceeded. This great development already had an impact on the area of Optical Music Recognition (OMR) as several novel methods relying on Deep Learning succeeded in specific tasks. Musicologists are interested in large-scale musical analysis and in publishing digital transcriptions in a collection enabling to develop tools for searching and data retrieving. The application of OMR promises to simplify and thus speed-up the transcription process by either providing fully-automatic or semi-automatic approaches. This thesis focuses on the automatic transcription of Medieval music with a focus on square notation which poses a challenging task due to complex layouts, highly varying handwritten notations, and degradation. However, since handwritten music notations are quite complex to read, even for an experienced musicologist, it is to be expected that even with new techniques of OMR manual corrections are required to obtain the transcriptions. This thesis presents several new approaches and open source software solutions for layout analysis and Automatic Text Recognition (ATR) for early documents and for OMR of Medieval manuscripts providing state-of-the-art technology. Fully Convolutional Networks (FCN) are applied for the segmentation of historical manuscripts and early printed books, to detect staff lines, and to recognize neume notations. The ATR engine Calamari is presented which allows for ATR of early prints and also the recognition of lyrics. Configurable CNN/LSTM-network architectures which are trained with the segmentation-free CTC-loss are applied to the sequential recognition of text but also monophonic music. Finally, a syllable-to-neume assignment algorithm is presented which represents the final step to obtain a complete transcription of the music. The evaluations show that the performances of any algorithm is highly depending on the material at hand and the number of training instances. The presented staff line detection correctly identifies staff lines and staves with an $F_1$-score of above $99.5\%$. The symbol recognition yields a diplomatic Symbol Accuracy Rate (dSAR) of above $90\%$ by counting the number of correct predictions in the symbols sequence normalized by its length. The ATR of lyrics achieved a Character Error Rate (CAR) (equivalently the number of correct predictions normalized by the sentence length) of above $93\%$ trained on 771 lyric lines of Medieval manuscripts and of 99.89\% when training on around 3.5 million lines of contemporary printed fonts. The assignment of syllables and their corresponding neumes reached $F_1$-scores of up to $99.2\%$. A direct comparison to previously published performances is difficult due to different materials and metrics. However, estimations show that the reported values of this thesis exceed the state-of-the-art in the area of square notation. A further goal of this thesis is to enable musicologists without technical background to apply the developed algorithms in a complete workflow by providing a user-friendly and comfortable Graphical User Interface (GUI) encapsulating the technical details. For this purpose, this thesis presents the web-application OMMR4all. Its fully-functional workflow includes the proposed state-of-the-art machine-learning algorithms and optionally allows for a manual intervention at any stage to correct the output preventing error propagation. To simplify the manual (post-) correction, OMMR4all provides an overlay-editor that superimposes the annotations with a scan of the original manuscripts so that errors can easily be spotted. The workflow is designed to be iteratively improvable by training better models as soon as new Ground Truth (GT) is available. / In den letzten Jahre wurden aufgrund der Möglichkeiten durch Deep Learning, was insbesondere in vielen Bildbearbeitungsaufgaben stetig neue Bestwerte erzielte, große Fortschritte im Bereich der künstlichen Intelligenz (KI) gemacht. Derzeit wird in vielen Gebieten menschliche Performanz erreicht oder mittlerweile sogar übertroffen. Diese großen Entwicklungen hatten einen Einfluss auf den Forschungsbereich der optischen Musikerkennung (OMR), da verschiedenste Methodiken, die auf Deep Learning basierten in spezifischen Aufgaben erfolgreich waren. Musikwissenschaftler sind in großangelegter Musikanalyse und in das Veröffentlichen von digitalen Transkriptionen als Sammlungen interessiert, was eine Entwicklung von Werkzeugen zur Suche und Datenakquise ermöglicht. Die Anwendung von OMR verspricht diesen Transkriptionsprozess zu vereinfachen und zu beschleunigen indem vollautomatische oder semiautomatische Ansätze bereitgestellt werden. Diese Arbeit legt den Schwerpunkt auf die automatische Transkription von mittelalterlicher Musik mit einem Fokus auf Quadratnotation, die eine komplexe Aufgabe aufgrund der komplexen Layouts, der stark variierenden Notationen und der Alterungsprozesse der Originalmanuskripte darstellt. Da jedoch die handgeschriebenen Musiknotationen selbst für erfahrene Musikwissenschaftler aufgrund der Komplexität schwer zu lesen sind, ist davon auszugehen, dass selbst mit den neuesten OMR-Techniken manuelle Korrekturen erforderlich sind, um die Transkription zu erhalten. Diese Arbeit präsentiert mehrere neue Ansätze und Open-Source-Software-Lösungen zur Layoutanalyse und zur automatischen Texterkennung (ATR) von frühen Dokumenten und für OMR von Mittelalterlichen Mauskripten, die auf dem Stand der aktuellen Technik sind. Fully Convolutional Networks (FCN) werden zur Segmentierung der historischen Manuskripte und frühen Buchdrucke, zur Detektion von Notenlinien und zur Erkennung von Neumennotationen eingesetzt. Die ATR-Engine Calamari, die eine ATR von frühen Buchdrucken und ebenso eine Erkennung von Liedtexten ermöglicht wird vorgestellt. Konfigurierbare CNN/LSTM-Netzwerkarchitekturen, die mit dem segmentierungsfreien CTC-loss trainiert werden, werden zur sequentiellen Texterkennung, aber auch einstimmiger Musik, eingesetzt. Abschließend wird ein Silben-zu-Neumen-Algorithmus vorgestellt, der dem letzten Schritt entspricht eine vollständige Transkription der Musik zu erhalten. Die Evaluationen zeigen, dass die Performanz eines jeden Algorithmus hochgradig abhängig vom vorliegenden Material und der Anzahl der Trainingsbeispiele ist. Die vorgestellte Notenliniendetektion erkennt Notenlinien und -zeilen mit einem $F_1$-Wert von über 99,5%. Die Symbolerkennung erreichte eine diplomatische Symbolerkennungsrate (dSAR), die die Anzahl der korrekten Vorhersagen in der Symbolsequenz zählt und mit der Länge normalisiert, von über 90%. Die ATR von Liedtext erzielte eine Zeichengenauigkeit (CAR) (äquivalent zur Anzahl der korrekten Vorhersagen normalisiert durch die Sequenzlänge) von über 93% bei einem Training auf 771 Liedtextzeilen von mittelalterlichen Manuskripten und von 99,89%, wenn auf 3,5 Millionen Zeilen von moderner gedruckter Schrift trainiert wird. Die Zuordnung von Silben und den zugehörigen Neumen erreicht $F_1$-werte von über 99,2%. Ein direkter Vergleich zu bereits veröffentlichten Performanzen ist hierbei jedoch schwer, da mit verschiedenen Material und Metriken evaluiert wurde. Jedoch zeigen Abschätzungen, dass die Werte dieser Arbeit den aktuellen Stand der Technik darstellen. Ein weiteres Ziel dieser Arbeit war es, Musikwissenschaftlern ohne technischen Hintergrund das Anwenden der entwickelten Algorithmen in einem vollständigen Workflow zu ermöglichen, indem eine benutzerfreundliche und komfortable graphische Benutzerschnittstelle (GUI) bereitgestellt wird, die die technischen Details kapselt. Zu diesem Zweck präsentiert diese Arbeit die Web-Applikation OMMR4all. Ihr voll funktionsfähiger Workflow inkludiert die vorgestellten Algorithmen gemäß dem aktuellen Stand der Technik und erlaubt optional manuell zu jedem Schritt einzugreifen, um die Ausgabe zur Vermeidung von Folgefehlern zu korrigieren. Zur Vereinfachung der manuellen (Nach-)Korrektur stellt OMMR4all einen Overlay-Editor zur Verfügung, der die Annotationen mit dem Scan des Originalmanuskripts überlagert, wodurch Fehler leicht erkannt werden können. Das Design des Workflows erlaubt iterative Verbesserungen, indem neue performantere Modelle trainiert werden können, sobald neue Ground Truth (GT) verfügbar ist.

Due to the current and anticipated massive increase of world population, expanding the agriculture cycle is necessary for accommodating the expected human’s demand. However, weeds invasion, which is a detrimental factor for agricultural production and quality, is a challenge for such agricultural expansion. Therefore, controlling weeds on fields by accurate,automatic, low-cost, environment-friendly, and real-time weeds detection technique is required. Additionally, automating the process of detecting, classifying, and counting of weeds per their growth stages is vital for using appropriate weeds controlling techniques. The literature review shows that there is a gap in the research efforts that handle the automation of weeds’ growth stages classification using DL models. Accordingly, in this thesis, a dataset of four weed (Consolida Regalis) growth stages was collected using unnamed arial vehicle. In addition, we developed and trained one-stage and two-stages deep learning models: YOLOv5, RetinaNet (with Resnet-101-FPN, Resnet-50-FPN backbones), and Faster R-CNN (with Resnet-101-DC5, Resnet-101-FPN, Resnet-50-FPN backbones) respectively. Comparing the results of all trained models, we concluded that, in one hand, the Yolov5-small model succeeds in detecting weeds and classifying the weed’s growth stages in the shortest inference time in real-time with the highest recall of 0.794 and succeeds in counting the instances of weeds per the four growth stages in real-time with counting time of 0.033 millisecond per frame. On the other hand, RetinaNet with ResNet-101-FPN backbone shows accurate and precise results in the testing phase (average precision of 87.457). Even though the Yolov5-large model showed the highest precision value in classifying almost all weed’s growth stages in training phase, Yolov5-large could not detect all objects in tested images. As a whole, RetinaNet with ResNet-101-FPN backbone shows accurate and high precision, while Yolov5-small has the shortest real inference time of detection and growth stages classification. Farmers can use the resulted deep learning model to detect, classify, and count weeds per growth stages automatically and as a result decrease not only the needed time and labor cost, but also the use of chemicals to control weeds on fields.

All microscope imaging is largely affected by inherent resolution limitations because of out-of-focus light and diffraction effects. The traditional approach to restoring the image resolution is to use a deconvolution algorithm to “invert” the effect of convolving the volume with the point spread function. However, these algorithms fall short in several areas such as noise amplification and stopping criterion. In this paper, we try to reconstruct an explicit volumetric representation of the fluorescence density in the sample and fit a neural network to the target z-stack to properly minimize a reconstruction cost function for an optimal result. Additionally, we do a weighted sampling of the point spread function to avoid unnecessary computations and prioritize non-zero signals. In a baseline comparison against the Richardson-Lucy method, our algorithm outperforms RL for images affected with high levels of noise.

No description available.

Time series data, as a pervasive kind of data format, have played one key role in numerous realworld scenarios. Effective time series modeling can help with accurate forecasting, resource optimization, risk management, etc. Considering its great importance, how can we model the nature of the pervasive time series data? Existing works have used adopted statistics analysis, state space models, Bayesian models, or other machine learning models for time series modeling. However, these methods usually follow certain assumptions and don't reveal the core and underlying rules of time series. Moreover, the recent advancement of deep learning has made neural networks a powerful tool for pattern recognition. This dissertation will target the problem of time series modeling using deep learning techniques to achieve accurate forecasting of time series. I will propose a principled approach for deep time series modeling from a novel distribution perspective. After in-depth exploration, I categorize and study two essential characteristics of time series, i.e., the distribution regularity and the distribution shift, respectively. I will investigate how can time series data involving the two characteristics be analyzed by distribution extraction, distribution scaling, and distribution transformation. By applying more recent deep learning techniques to distribution learning for time series, this defense aims to achieve more effective and efficient forecasting and decision-making. I will carefully illustrate of proposed methods of three themes and summarize the key findings and improvements achieved through experiments. Finally, I will present my future research plan and discuss how to broaden my research of deep time series modeling into a more general Data-Centric AI system for more generalized, reliable, fair, effective, and efficient decision-making.

Permafrost degradation is observed all over the world as a consequence of climate change and the associated Arctic amplification, which has severe implications for the environment. Landslides, increased rates of surface deformation, rising likelihood of infrastructure damage, amplified coastal erosion rates, and the potential turnover of permafrost from a carbon sink to a carbon source are thereby exemplary implications linked to the thawing of frozen ground material. In this context, satellite earth observation is a potent tool for the identification and continuous monitoring of relevant processes and features on a cheap, long-term, spatially explicit, and operational basis as well as up to a circumpolar scale. A total of 325 articles published in 30 different international journals during the past two decades were investigated on the basis of studied environmental foci, remote sensing platforms, sensor combinations, applied spatio-temporal resolutions, and study locations in an extensive review on past achievements, current trends, as well as future potentials and challenges of satellite earth observation for permafrost related analyses. The development of analysed environmental subjects, utilized sensors and platforms, and the number of annually published articles over time are addressed in detail. Studies linked to atmospheric features and processes, such as the release of greenhouse gas emissions, appear to be strongly under-represented. Investigations on the spatial distribution of study locations revealed distinct study clusters across the Arctic. At the same time, large sections of the continuous permafrost domain are only poorly covered and remain to be investigated in detail. A general trend towards increasing attention in satellite earth observation of permafrost and related processes and features was observed. The overall amount of published articles hereby more than doubled since the year 2015. New sources of satellite data, such as the Sentinel satellites and the Methane Remote Sensing LiDAR Mission (Merlin), as well as novel methodological approaches, such as data fusion and deep learning, will thereby likely improve our understanding of the thermal state and distribution of permafrost, and the effects of its degradation. Furthermore, cloud-based big data processing platforms (e.g. Google Earth Engine (GEE)) will further enable sophisticated and long-term analyses on increasingly larger scales and at high spatial resolutions. In this thesis, a specific focus was put on Arctic permafrost coasts, which feature increasing vulnerability to environmental parameters, such as the thawing of frozen ground, and are therefore associated with amplified erosion rates. In particular, a novel monitoring framework for quantifying Arctic coastal erosion rates within the permafrost domain at high spatial resolution and on a circum-Arctic scale is presented within this thesis. Challenging illumination conditions and frequent cloud cover restrict the applicability of optical satellite imagery in Arctic regions. In order to overcome these limitations, Synthetic Aperture RADAR (SAR) data derived from Sentinel-1 (S1), which is largely independent from sun illumination and weather conditions, was utilized. Annual SAR composites covering the months June–September were combined with a Deep Learning (DL) framework and a Change Vector Analysis (CVA) approach to generate both a high-quality and circum-Arctic coastline product as well as a coastal change product that highlights areas of erosion and build-up. Annual composites in the form of standard deviation (sd) and median backscatter were computed and used as inputs for both the DL framework and the CVA coastal change quantification. The final DL-based coastline product covered a total of 161,600 km of Arctic coastline and featured a median accuracy of ±6.3 m to the manually digitized reference data. Annual coastal change quantification between 2017–2021 indicated erosion rates of up to 67 m per year for some areas based on 400 m coastal segments. In total, 12.24% of the investigated coastline featured an average erosion rate of 3.8 m per year, which corresponds to 17.83 km2 of annually eroded land area. Multiple quality layers associated to both products, the generated DL-coastline and the coastal change rates, are provided on a pixel basis to further assess the accuracy and applicability of the proposed data, methods, and products. Lastly, the extracted circum-Arctic erosion rates were utilized as a basis in an experimental framework for estimating the amount of permafrost and carbon loss as a result of eroding permafrost coastlines. Information on permafrost fraction, Active Layer Thickness (ALT), soil carbon content, and surface elevation were thereby combined with the aforementioned erosion rates. While the proposed experimental framework provides a valuable outline for quantifying the volume loss of frozen ground and carbon release, extensive validation of the utilized environmental products and resulting volume loss numbers based on 200 m segments are necessary. Furthermore, data of higher spatial resolution and information of carbon content for deeper soil depths are required for more accurate estimates. / Als Folge des Klimawandels und der damit verbundenen „Arctic Amplification“ wird weltweit eine Degradation des Dauerfrostbodens (Permafrost) beobachtet, welche schwerwiegende Auswirkungen auf die Umwelt hat. Erdrutsche, erhöhte Oberflächen- verformungsraten, eine zunehmende Wahrscheinlichkeit von Infrastrukturschäden, verstärkte Küstenerosionsraten und die potenzielle Umwandlung von Permafrost von einer Kohlenstoffsenke in eine Kohlenstoffquelle sind dabei beispielhafte Auswirkun- gen im Zusammenhang mit dem Auftauen von gefrorenem Bodenmaterial. In diesem Kontext ist die Satelliten-gestützte Erdbeobachtung ein wirkmächtiges Werkzeug zur Identifizierung und kontinuierlichen Überwachung relevanter Prozesse und Merkmale auf einer kostengünstigen, langfristigen, räumlich expliziten und operativen Basis und auf einem zirkumpolaren Maßstab. Insgesamt 325 Artikel, die in den letzten zwei Jahrzehnten in 30 verschiedenen internationalen Zeitschriften veröffentlicht wurden, wurden auf Basis der adressierten Umweltschwerpunkte, Fernerkundungsplattformen, Sensorkombinationen, angewand- ten raum-zeitlichen Auflösungen und den Studienorten in einem umfassenden Überblick über vergangene Errungenschaften und aktuelle Trends untersucht. Zusätzlich wur- den zukünftige Potenziale und Herausforderungen der Satelliten-Erdbeobachtung für Permafrost-bezogene Analysen diskutiert. Auf die zeitliche Entwicklung der un- tersuchten Umweltthemen, eingesetzten Sensoren und Satelliten-Plattformen sowie die Zahl der jährlich erscheinenden Artikel wurde detailliert eingegangen. Studien zu atmosphärischen Eigenschaften und Prozessen, wie etwa der Freisetzung von Treibhaus- gasemissionen, waren stark unterrepräsentiert. Deutliche geografische Schlüssel-Gebiete, auf welche sich der Großteil der Studien konzentrierte, konnten in Untersuchungen zur räumlichen Verteilung der Studienorte identifiziert werden. Gleichzeitig sind große Teile des kontinuierlichen Permafrost-Gebiets nur spärlich abgedeckt und müssen noch im Detail untersucht werden. Es wurde ein allgemeiner Trend zu einer zunehmenden Aufmerksamkeit bezüglich der Satelliten-gestützten Erdbeobachtung von Permafrost und verwandten Prozessen und Merkmalen beobachtet. Die Gesamtzahl der veröf- fentlichten Artikel hat sich dabei seit dem Jahr 2015 mehr als verdoppelt. Neue Quellen für Satellitendaten, wie beispielweise die Sentinel-Satelliten und die Methane Remote Sensing LiDAR Mission (Merlin), sowie neuartige methodische Ansätze, wie Datenfusion und Deep Learning, werden dabei voraussichtlich unser Verständnis bzgl. des thermischen Zustands und der Verteilung von Permafrost-Vorkommen sowie die Auswirkungen seines Auftauens verbessern. Darüber hinaus werden Cloud-basierte Big-Data-Verarbeitungsplattformen (z.B. Google Earth Engine (GEE)) anspruchsvolle und langfristige Analysen in immer größeren Maßstäben und mit hoher räumlicher Auflösung erleichtern. In dieser Arbeit wurde ein besonderer Fokus auf arktische Permafrost-Küsten gelegt, die eine zunehmende Vulnerabilität gegenüber Umweltparametern wie dem Auftauen von gefrorenem Boden aufweisen und daher von verstärkten Erosionsraten betroffen sind. Ein neuartiger Ansatz zur Quantifizierung der arktischen Küstene- rosion innerhalb des Permafrost-Gebiets mit hoher räumlicher Auflösung und auf zirkum-arktischem Maßstab wird in dieser Dissertation präsentiert. Schwierige Be- leuchtungsbedingungen und häufige Bewölkung schränken die Anwendbarkeit optischer Satellitenbilder in arktischen Regionen ein. Um diese Einschränkungen zu überwinden, wurden Synthetic Aperture RADAR (SAR) Daten von Sentinel-1 (S1) verwendet, die weitgehend unabhängig von Sonneneinstrahlung und Wetterbedingungen sind. Jährli- che SAR-Komposite, welche die Monate Juni bis September abdecken, wurden mit einem Deep Learning (DL)-Ansatz und einer Change Vector Analysis (CVA)-Methode kombiniert, um sowohl ein qualitativ hochwertiges und zirkum-arktisches Küstenli- nienprodukt als auch ein Produkt für die Änderungsraten (Erosion und küstennahe Aggregation von Sedimenten) der Küste zu generieren. Jährliche Satelliten-Komposite in Form von der Standardabweichung (sd) und des Medians der SAR Rückstreuung wurden hierbei berechnet und als Eingabedaten sowohl für den DL-Ansatz als auch für die Quantifizierung der CVA-basierten Küstenänderung verwendet. Das endgül- tige DL-basierte Küstenlinienprodukt deckt insgesamt 161.600 km der arktischen Küstenlinie ab und wies eine Median-Abweichung von ±6,3 m gegenüber den ma- nuell digitalisierten Referenzdaten auf. Im Zuge der Quantifizierung von jährlichen Küstenveränderungen zwischen 2017 und 2021 konnten Erosionsraten von bis zu 67 m pro Jahr und basierend auf 400 m Küstenabschnitten identifiziert werden. Insgesamt wiesen 12,24% der untersuchten Küstenlinie eine durchschnittliche Erosionsrate von 3,8 m pro Jahr auf, was einer jährlichen erodierten Landfläche von 17,83 km2 entspricht. Mehrere Qualitäts-Datensätze, die beiden Produkten zugeordnet sind, wurden auf Pixelbasis bereitgestellt, um die Genauigkeit und Anwendbarkeit der präsentierten Daten, Methoden und Produkte weiter einordnen zu können. Darüber hinaus wurden die extrahierten zirkum-arktischen Erosionsraten als Grund- lage in einem experimentellen Ansatz verwendet, um die Menge an Permafrost-Verlust und Kohlenstofffreistzung als Konsequenz der erodierten Permafrost-Küsten abzu- schätzen. Dabei wurden Informationen zu Permafrost-Anteil, Active Layer Thickness (ALT), Höhenmodellen und der Menge an im Boden gespeichertem Kohlenstoff mit den oben genannten Erosionsraten kombiniert. Während der präsentierte experimentelle Ansatz einen wertvollen Ausgangspunkt für die Quantifizierung des Volumenverlusts von gefrorenem Boden und der Kohlenstofffreisetzung darstellt, ist eine umfassende Validierung der verwendeten Umweltprodukte und der resultierenden Volumenzah- len erforderlich. Zusätzlich werden für genauere Abschätzungen Daten mit höherer räumlicher Auflösung und Informationen zum Kohlenstoffgehalt für tiefere Bodentiefen benötigt.