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The expansion of renewable energies is being driven by the gradual phaseout of fossil fuels in order to reduce greenhouse gas emissions, the steadily increasing demand for energy and, more recently, by geopolitical events. The offshore wind energy sector is on the verge of a massive expansion in Europe, the United Kingdom, China, but also in the USA, South Korea and Vietnam. Accordingly, the largest marine infrastructure projects to date will be carried out in the upcoming decades, with thousands of offshore wind turbines being installed. In order to accompany this process globally and to provide a database for research, development and monitoring, this dissertation presents a deep learning-based approach for object detection that enables the derivation of spatiotemporal developments of offshore wind energy infrastructures from satellite-based radar data of the Sentinel-1 mission.
For training the deep learning models for offshore wind energy infrastructure detection, an approach is presented that makes it possible to synthetically generate remote sensing data and the necessary annotation for the supervised deep learning process. In this synthetic data generation process, expert knowledge about image content and sensor acquisition techniques is made machine-readable. Finally, extensive and highly variable training data sets are generated from this knowledge representation, with which deep learning models can learn to detect objects in real-world satellite data.
The method for the synthetic generation of training data based on expert knowledge offers great potential for deep learning in Earth observation. Applications of deep learning based methods can be developed and tested faster with this procedure. Furthermore, the synthetically generated and thus controllable training data offer the possibility to interpret the learning process of the optimised deep learning models.
The method developed in this dissertation to create synthetic remote sensing training data was finally used to optimise deep learning models for the global detection of offshore wind energy infrastructure. For this purpose, images of the entire global coastline from ESA's Sentinel-1 radar mission were evaluated. The derived data set includes over 9,941 objects, which distinguish offshore wind turbines, transformer stations and offshore wind energy infrastructures under construction from each other. In addition to this spatial detection, a quarterly time series from July 2016 to June 2021 was derived for all objects. This time series reveals the start of construction, the construction phase and the time of completion with subsequent operation for each object.
The derived offshore wind energy infrastructure data set provides the basis for an analysis of the development of the offshore wind energy sector from July 2016 to June 2021. For this analysis, further attributes of the detected offshore wind turbines were derived. The most important of these are the height and installed capacity of a turbine. The turbine height was calculated by a radargrammetric analysis of the previously detected Sentinel-1 signal and then used to statistically model the installed capacity. The results show that in June 2021, 8,885 offshore wind turbines with a total capacity of 40.6 GW were installed worldwide. The largest installed capacities are in the EU (15.2 GW), China (14.1 GW) and the United Kingdom (10.7 GW). From July 2016 to June 2021, China has expanded 13 GW of offshore wind energy infrastructure. The EU has installed 8 GW and the UK 5.8 GW of offshore wind energy infrastructure in the same period. This temporal analysis shows that China was the main driver of the expansion of the offshore wind energy sector in the period under investigation.
The derived data set for the description of the offshore wind energy sector was made publicly available. It is thus freely accessible to all decision-makers and stakeholders involved in the development of offshore wind energy projects. Especially in the scientific context, it serves as a database that enables a wide range of investigations. Research questions regarding offshore wind turbines themselves as well as the influence of the expansion in the coming decades can be investigated. This supports the imminent and urgently needed expansion of offshore wind energy in order to promote sustainable expansion in addition to the expansion targets that have been set.
This project explores Tan Yunxian's journey of becoming a female doctor in the Ming dynasty. Among all the surviving Ming medical books, Tan Yunxian's medical case book is the only one that was written by a woman. It seems natural, considering she had both scholar-official and medical family backgrounds. Yet, social expectations consider it more suitable for a lady to remain in the household, and not treat patients outside. To legitimize Tan Yunxian's pursuit of a medical career, she applied several strategies to resolve potential criticism toward her and her family. These strategies are analyzed through her autobiographical preface in her medical case book. The project also explores Ming male literatis' perspectives toward Tan Yunxian, the factors that contributed to the preservation and publication of her medical case book, and examined her medical cases under the social-historical and micro-history contexts.
The Amduat is one of the most important Netherworld Books which was recorded in various kinds of Ancient Egyptian sources since the beginning of the 18th dynasty, especially the walls of the royal tombs. The main theme of the Amduat is the journey of the sun god through the underworld where the solar bark and its crew is the central scene of the journey. The study focuses on finding the reasons of choosing the crew’s members who manage the sun bark’s journey in the Amduat. It also aims at illustrating the functions and responsibilities of each crew member. Following a historical approach, the study analyzes the Pyramid Texts and Coffin Texts as the most important documents before the New Kingdom, and proceeding to the inscriptions and writings of the monuments which contain portrayals and inscriptions of the Amduat in the New Kingdom. Furthermore, it sheds some light on the solar cycle’s main features and primary aspects, and tries to scrutinize the date, meaning, and symbolisms of the Amduat and its indications in the earlier sources.
Zur Zeit des Nationalsozialismus wurden im Rahmen der „T4“-Aktion sowie der anschließend stattfindenden dezentralen Krankenmorde hunderttausende psychisch kranke und behinderte Menschen getötet. Auch Patientinnen und Patienten der Heil- und Pflegeanstalt Lohr am Main wurden nach vorherigen Selektionen in Tötungsanstalten deportiert und dort ermordet. Der Einfluss der Pflegenden auf die ärztliche Dokumentation, welche einen relevanten Einfluss auf die Auswahl der getöteten Menschen hatte, war erheblich. Die Studie befasst sich unter anderem mit dem Psychiatriealltag in der Heil- und Pflegeanstalt Lohr am Main zur Zeit des Nationalsozialismus, den Lebens- und Arbeitsbedingungen der Kranken sowie des Personals, den therapeutischen Ansätzen mit dem Schwerpunkt Arbeitstherapie, der Analyse von Einzelschicksalen der psychisch kranken Bewohnerinnen und Bewohner, der Vergabehäufigkeit der todbringenden Diagnose „Schizophrenie“ nach den Deportationen 1940 sowie der Vermutung, dass auch in Lohr am Main nach der Beendigung der „T4“-Aktion dezentrale Krankenmorde stattfanden.