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  • 1
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    Dynamic parking maps from vehicular crowdsensing (2018)
    Hannover : Leibniz Universität Hannover
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    Call number: S 99.0139(342)
    In: Wissenschaftliche Arbeiten der Fachrichtung Vermessungswesen der Universität Hannover, Nr. 342
    Type of Medium: Series available for loan
    Pages: 137 Seiten , Illustrationen, Diagramme
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Vermessungswesen der Universität Hannover Nr. 342
    URL: http://www.gbv.de/dms/tib-ub-hannover/1030810273.pdf
    Language: English
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2018 , 1 Introduction 1.1 Background and motivation 1.2 Goals of this thesis 1.3 Outline 2 Background on digital maps and data mining 2.1 Digital maps 2.1.1 Navigation maps and map dynamics 2.1.2 OpenStreetMap 2.1.3 Navigation Data Standard (NDS) 2.2 Data mining 2.2.1 Knowledge Discovery in Databases (KDD) process 2.2.2 Taxonomy of data mining methods 2.2.3 Classification 2.2.4 Clustering 2.2.5 Time series analysis 3 Related work about mobile crowdsensing of on-street parking spaces 3.1 On-street parking 3.1.1 Parking occupancy detection 3.1.2 Parking availability estimation and prediction 3.1.3 Parking search and guidance 3.2 Mobile crowdsensing 3.2.1 Mobile crowdsensing in transportation 3.2.2 Mobile crowdsensing for parking 3.3 Research gaps addressed in this thesis 4 LiDAR-based parking availability data acquisition 4.1 Data recording 4.1.1 Sensor equipment 4.1.2 Measurement campaign 4.2 Methodology 4.2.1 Preprocessing 4.2.2 Segmentation 4.2.3 Classification 4.2.4 Repetition of segmentation and classification 4.2.5 Matching to road network 4.3 Results 4.3.1 Object segmentation 4.3.2 Classification 4.3.3 End-to-end evaluation of complete approach 4.3.4 Parking occupancy statistics over the day 4.4 Concluding remarks 5 Learning parking legality maps from parking observations 5.1 Methodology 5.1.1 Location of parked vehicles as method input 5.1.2 Data preprocessing 5.1.3 Definition of feature sets 5.1.4 Learning the parking legality of road subsegments 5.2 Evaluation 5.2.1 Evaluation approach 5.2.2 Results 5.3 Concluding remarks 6 Spatio-temporal analysis of large scale parking availability data and simulation of crowdsensing 6.1 Description and processing of parking dataset from SFpark 6.2 Time series analysis of parking availability data 6.3 Clustering of parking occupancy daily pattern 6.4 Spatial relations in parking availability 6.5 Modelling of crowdsensing based on downsampling for probe vehicles and mobile apps 6.5.1 Scenario based on probe vehicles 6.5.2 Scenario based on mobile apps 6.6 Modelling of probe-vehicle-based crowdsensing from taxi GPS trajectories 6.6.1 Processing overview and description of taxi trajectory dataset 6.6.2 Taxi GPS trajectory processing 6.6.3 Characteristics and aggregation of taxi coverage 6.6.4 Comparison of parking and taxi daily pattern 6.6.5 Simulation of parking availability observations 6.7 Concluding remarks 7 Parking availability estimation and prediction from crowdsensed data 7.1 Spatial interpolation of parking availability 7.2 Parking availability estimation with persistence method 7.3 Estimation and prediction of parking availability based on binary classification 7.3.1 Binary classification approach 7.3.2 Results of binary classification estimation and prediction 7.4 Concluding remarks 8 Benefits of crowdsensed parking availability information 8.1 Types of information for on-street parking 8.2 Experimental setup 8.2.1 Routing strategies 8.2.2 Data sources 8.3 Evaluation of the impact of different parking information 8.3.1 Results for all decisions in the dataset 8.3.2 Results for relevant decisions 8.3.3 Similarity of capacity 8.4 Concluding remarks 9 Conclusion and outlook 9.1 Research questions addressed and overall conclusion 9.2 Applicability of dynamic map approaches to further dynamic phenomena 9.3 Future research directions List of figures List of tables References Acknowledgements Curriculum vitae
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  • 2
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    Observing inter- and intra-annual glacier changes and lake loading effects from synthetic aperture radar remote sensing (2018)
    Hannover : Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
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    Call number: S 99.0139(348)
    In: Wissenschaftliche Arbeiten der Fachrichtung Vermessungswesen der Universität Hannover
    Type of Medium: Series available for loan
    Pages: 145 Seiten , Illustrationen, Diagramme, Karten
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Vermessungswesen der Universität Hannover Nr. 348
    URL: Inhaltsverzeichnis
    Language: English
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2018 , Introduction 1.1 Synthetic Aperture Radar from Spaceborne Remote Sensing 1.2 Satellite-Based Monitoring of the Terrestrial Water Cycle 1.3 Remote Sensing of Water Storage in Central Asia 1.4 GFZ Activities in Central Asia and Study Areas in Kyrgyzstan 1.5 Research Objectives 1.6 Outline and Structure of the Thesis 2 Fundamentals of Synthetic Aperture Radar Remote Sensing 2.1 SAR Satellite Data 2.2 SAR Satellite Missions 2.3 Interferometric SAR 2.4 DInSAR Time Series with PSI 2.5 DInSAR Time Series with SBAS 2.6 Feature Tracking 3 State of the Art 3.1 Inter- and Intra-Annual Glacier Surface Velocities from SAR Data 3.2 Inter- and Intra-Annual Glacier Elevation Changes from SAR Data 3.3 Inter- and Intra-Annual Loading-Induced Crustal Deformations at Water Reservoirs from SAR Data 4 Quantification of Inylchek Glacier Surface Kinematics 4.1 Abstract 4.2 Introduction 4.3 Inylchek Glacier 4.4 Data and Methodology 4.4.1 TerraSAR-X Data Set 4.4.2 Feature Tracking 4.4.3 Decomposition to 3D Velocities 4.5 Results 4.6 Discussion 4.6.1 Error Estimation 4.6.2 Inter-Annual Kinematics of the Upper Southern Inylchek Glacier Branch .... 4.6.3 Lake Level Extent and GLOF 4.7 Conclusions 4.8 Acknowledgements 4.9 Author Contribution 5 Quantification of Inylchek Glacier Elevation Changes 5.1 Abstract 5.2 Introduction 5.3 Data 5.3.1 TanDEM X Data 5.3.2 External DEMs 5.3.3 Glacier Outlines of Inylchek 5.4 Methodology 5.4.1 Interferometric Processing of TanDEM-X Data 5.4.2 Alignment of the SRTM and TDX DEMs 5.4.3 Radar Penetration Correction 5.4.4 DEM Elevation Difference Calculation 5.4.5 Accuracy Assessment 5.5 Results and Discussion 5.5.1 Uncertainty of Measurements 5.5.2 DEM Alignment Quality 5.5.3 Inylchek Elevation Changes 5.6 Conclusions 5.7 Acknowledgements 5.8 Author Contribution 6 Quantification of Toktogul Water-Level-Induced Ground Deformations 6.1 Abstract 6.2 Introduction 6.3 Materials and Methods 6.3.1 Lake Altimetry 6.3.2 DInSAR processing of Envisat ASAR and Sentinel-1 Data 6.3.3 Atmospheric Correction 6.3.4 Deformation Decomposition of SentineH Data 6.3.5 Modelling of Elastic Surface Deformations 6.4 Results 6.4.1 Atmospheric Corrections 6.4.2 Ground Deformation 6.5 Discussion 6.5.1 Atmospheric Corrections 6.5.2 Ground Deformation 6.6 Conclusions 6.7 Acknowledgments 6.8 Author Contribution 7 Subsequent Work 7.1 Scope of the Chapter 7.2 GNSS-derived Inylchek Glacier Surface Kinematics 7.2.1 Abstract 7.2.2 Author Contribution 7.3 Monitoring of Lake Merzbacher's GLOF Event 7.3.1 Abstract 7.3.2 Author Contribution 7.4 Ongoing Work at GFZ Based on the Results of this Thesis 8 Summary and Outlook 8.1 Summary of Main Results 8.1.1 Methodological Aspects 8.1.2 Monitoring of Short-Time Changes 8.2 Outlook Bibliography
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  • 3
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    Multi-view person re-identification (2020)
    Hannover : Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
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    Call number: S 99.0139(356)
    In: Wissenschaftliche Arbeiten der Fachrichtung Vermessungswesen der Universität Hannover
    Type of Medium: Series available for loan
    Pages: x, 111 Seiten , Illustrationen, Diagramme
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Vermessungswesen der Universität Hannover Nr. 356
    URL: Inhaltsverzeichnis
    Language: English
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2020 , Abstract Zusammenfassung Acknowledgments Definition, Acronyms and Symbols 1 Introduction 1.1 Motivation 1.2 Person Re-Identification 1.3 Problem statement and research objective 1.4 Contribution 1.5 Outline of this thesis 2 Related work 2.1 Scope 2.2 Historical overview 2.3 Terminology and strategies 2.4 Handcrafted feature extraction methods 2.5 Data-driven feature extraction methods 2.6 Person view specific methods 2.7 Re-Ranking based methods 2.8 Domain adaptation methods 2.9 Discussion 3 Fundamentals 3.1 Fisheye camera geometry and projection model 3.2 Feature extraction 3.2.1 GOG/XQDA - a handcrafted feature extraction method 3.2.2 TriNet and SRNN - two data-driven feature extraction methods .... 4 A new approach for person re-identification 4.1 General overview 4.2 Input and assumptions 4.3 Projection alignment 4.4 View classification and sampling 4.5 Per-view matching 4.6 Fusion 4.7 Discussion of the approach 5 Experimental evaluation 5.1 General structure of this chapter 5.2 Multi-view investigations 5.2.1 Datasets 5.2.2 Training and inference procedure 5.2.3 Evaluation and discussion 5.3 Bird's eye view investigations 5.3.1 Datasets 5.3.2 Training and inference procedure 5.3.3 Evaluation and discussion 5.4 Influence of data 5.4.1 Datasets 5.4.2 Training and inference procedure 5.4.3 Evaluation and discussion 5.5 Fisheye investigations 5.5.1 Datasets 5.5.2 Training procedure 5.5.3 Projection alignment 5.5.4 Person view classification 5.5.5 Assessment of PRID results 5.5.6 Comparison with a contemporary approach 5.5.7 Qualitative comparison 6 Conclusions and future work A Datasets A.l Our novel datasets A.2 Public datasets References , Sprache der Zusammenfassungen: Englisch, Deutsch
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  • 4
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    A hierarchical deep learning framework towards the verification of geospatial databases (2022)
    Hannover : Leibniz Universität Hannover
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    Call number: S 99.0139(377)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, Nr. 377
    Type of Medium: Series available for loan
    Pages: XVI, 146 Seiten , Diagramme, Illustrationen, Karten
    ISBN: 978-3-7696-5295-6 , 9783769652956
    ISSN: 0065-5325
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 377
    URL: Table of Contents
    Language: English , German
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2021 , Contents 1. Introduction 1.1. Motivation 1.2. Goal and Contributions 1.3. Structure of this Thesis 2. Fundamentals 2.1. Classification 2.2. Artificial Neural Network 2.2.1. Perceptron 2.2.2. Multilayer Percptrons 2.2.3. Training 2.2.3.1. Loss Function 2.2.3.2. Gradient Descent Optimization 2.2.3.3. Step Learning Policy 2.3. Convolution Neural Networks 2.3.1. Components 2.3.1.1. Convolution 2.3.1.2. Pooling 2.3.1.3. Batch Normalization 2.3.2. CNN for Image Classification 2.3.3. CNN for Semantic Segmentation 2.3.3.1. Fully Convolution Networks 2.3.3.2. U-Net 2.3.4. Training 2.3.5. Data Augmentation 3. Related Work 3.1. CNN in general 3.1.1. Image Classification 3.1.2. Semantic Segmentation 3.2. Land Cover Classification 3.3. Land Use Classification 3.3.1. Methods not based on CNN 3.3.2. CNN-based Methods 3.4. Discussion 3.4.1. Land Cover Classification 3.4.2. Land Use Classification 4. Methodology 4.1. Overview 4.2. Land Cover Classification 4.2.1. Network Architecture 4.2.2. Network Variants 4.2.2.1. Network without skip-connections 4.2.2.2. Network with elementwise addition skip-connections 4.2.2.3. Network with learnable skip-connections 4.2.3. Training 4.3. Hierarchical Land Use Classification 4.3.1. Polygon Shape Representation 4.3.2. Patch Preparation 4.3.2.1. Tiling 4.3.2.2. Scaling 4.3.2.3. Combination of tiling and scaling 4.3.3. Network Architecture 4.3.3.1. Base Network for Mask Representation: LuNet-lite 4.3.3.2. LuNet-lite with Multi-Task Learning 4.3.3.3. Achieving Consistency with the Class Hierarchy 4.3.3.4. Network Architecture for Implicit Representation 4.3.4. Training 4.3.4.1. LuNet-lite 4.3.4.2. LuNet-lite-MT 4.3.4.3. LuNet-lite-JO and LuNet-lite-BG-JO 4.3.5. Inference at Object Level 5. Datasets and Test Setup 5.1. Datasets 5.1.1. Hameln 5.1.2. Schleswig 5.1.3. Mecklenburg-Vorpommern (MV) 5.1.4. Vaihingen and Potsdam 5.2. Evaluation Metrics 5.3. Experimental Setup 5.3.1. Land Cover Classification 5.3.1.1. Test Setup 5.3.1.2. Overview of all Experiments 5.3.1.3. Prediction Variability of FuseNet-lite 5.3.1.4. Impact of the Hyperparameter Settings 5.3.1.5. Effectiveness of the learnable Skip-Connections 5.3.1.6. Performance of FuseNet-lite 5.3.1.7. Combining Datasets 5.3.2. Land Use Classification 5.3.2.1. Input Configurations 5.3.2.2. Test Setup 5.3.2.3. Overview of all Experiments 5.3.2.4. Prediction Variability of LuNet-lite-JO 5.3.2.5. Impact of the Hyperparameter Settings 5.3.2.6. Impact of Joint Optimization 5.3.2.7. Impact of the Polygon Representation 5.3.2.8. Impact of Land Cover Information 5.3.2.9. Impact of the Patch Generation 5.3.2.10. Evaluation on all Datasets 5.3.2.11. Combining Datasets 6. Experiments 6.1. Evaluation of Land Cover Classification 6.1.1. Prediction Variability of FuseNet-lite 6.1.2. Investigations of the Hyperparameter Settings 6.1.2.1. Base Learning Rate 6.1.2.2. Mini Batch Size 6.1.2.3. The Weight of the Penalty Term in the Focal Loss 6.1.3. Effectiveness of the learnable Skip-Connections 6.1.4. Evaluation on the individual Datasets 6.1.4.1. Hameln, Schleswig and MV 6.1.4.2. Vaihingen and Potsdam 6.1.4.3. Answers to the Questions raised in Section 5.3.1.6 6.1.5. Training on the combined Datasets 6.1.6. Discussion 6.2. Evaluation of Land Use Classification 6.2.1. Prediction Variability of LuNet-lite-JO 6.2.2. Investigations of the Hyperparameter Settings 6.2.2.1. Base Learning Rate 6.2.2.2. Mini Batch Size 6.2.2.3. The Weight of the Penalty Term in the Focal Loss 6.2.3. Impact of Joint Optimization 6.2.4. Impact of the Polygon Representation 6.2.5. Impact of Land Cover Information 6.2.6. Impact of the Patch Generation Approach 6.2.7. Evaluation on all Datasets 6.2.8. Training on combined Datasets 6.2.9. Discussion 7. Conclusion and Outlook 7.1. Conclusion 7.2. Outlook References , Sprache der Kurzfassungen: Englisch, Deutsch
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  • 5
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    Probabilistic Pose Estimation and 3D Reconstruction of Vehicles from Stereo Images (2020)
    Hannover : Leibniz Universität Hannover
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    Call number: S 99.0139(362)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, Nr. 362
    Type of Medium: Series available for loan
    Pages: XV, 143 Seiten , Illustrationen, Diagramme
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 362
    URL: Inhaltsverzeichnis
    Language: English
    Note: 1 Introduction 1.1 Contributions 1.2 Thesis outline 2 Basics 2.1 Convolutional Neural Networks 2.1.1 Training 2.1.2 CNN Architectures 2.2 Active Shape Model 2.3 Monte Carlo based optimisation 3 State of the art 3.1 Data driven approaches 3.1.1 Viewpoint prediction 3.1.2 3D pose prediction 3.1.3 3D pose and shape prediction 3.2 Model driven approaches 3.2.1 Shape priors 3.2.2 Scene priors 3.2.3 Shape aware reconstruction 3.2.4 Optimisation 3.3 Discussion 4 Methodology 4.1 Overview 4.1.1 Input 4.1.2 Problem statement 4.1.3 Scene layout 4.1.4 Detection of vehicles 4.2 Subcategory-aware 3D shape prior 4.2.1 Geometrical representation 4.2.2 Mode Learning 4.3 Multi-Task CNN 4.3.1 Input branch 4.3.2 Vehicle type branch 4.3.3 Viewpoint branch 4.3.4 Keypoint/Wireframe branch 4.3.5 Training 4.4 Probabilistic vehicle reconstruction 4.4.1 3D likelihood 4.4.2 Keypoint likelihood 4.4.3 Wireframe likelihood 4.4.4 Position prior 4.4.5 Orientation prior 4.4.6 Shape prior 4.4.7 Inference 4.5 Discussion 5 Experimental setup 5.1 Objectives 5.2 Test data 5.2.1 KITTI benchmark 5.2.2 ICSENS data set 5.3 Parameter settings and training 5.3.1 Learning the ASM 5.3.2 Training of the CNN 5.4 Evaluation strategy and evaluation criteria 5.4.1 Detection 5.4.2 Multi-Task CNN 5.4.3 Probabilistic model for vehicle reconstruction 5.4.4 Comparison to related methods 6 Results and discussion 6.1 Detection 6.2 Evaluation of the CNN components 6.2.1 Evaluation of the viewpoint branch 6.2.2 Evaluation of the vehicle type branch 6.3 Ablation studies of the model components 6.3.1 Analysis of the observation likelihoods 6.3.2 Analysis of the state priors 6.4 Analysis of the full model for vehicle reconstruction 6.4.1 Evaluation of the pose 6.4.2 Evaluation of the shape 6.4.3 Analysis of further aspects 6.5 Comparison to related methods 6.6 Discussion 6.6.1 Likelihood terms 6.6.2 State priors 6.6.3 Full model 6.6.4 Inference 7 Conclusion and outlook , Sprache der Kurzfassungen: Englisch, Deutsch
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  • 6
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    Schema-Matching in räumlichen Datensätzen durch Zuordnung von Objektinstanzen (2020)
    Hannover : Leibniz Universität Hannover
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    Call number: S 99.0139(359)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, Nr. 359
    Type of Medium: Series available for loan
    Pages: 134 Seiten , Diagramme, Karten
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 359
    URL: Inhaltsverzeichnis
    Language: German , English
    Note: 1 Einleitung 1.1 Motivation 1.2 Zielsetzung 1.3 Gliederung 2 Verwandte Arbeiten 2.1 Grundbegriffe 2.1.1 Raumbezogene Objekte 2.1.2 Ähnlichkeit 2.1.3 Relation 2.1.4 Schema 2.2 Data-Matching 2.2.1 Klassifikation von Zuordnungsverfahren auf Objektebene 2.2.2 Herausforderungen bei der Objektzuordnung 2.2.3 Ausgewählte, merkmalsbasierte Verfahren 2.2.4 Ausgewählte, relationale Verfahren 2.3 Schema-Matching 2.3.1 Klassifikation von Zuordnungsverfahren auf Schemaebene 2.3.2 Herausforderungen bei der Zuordnung auf Schemaebene 2.3.3 Ausgewählte Schema-Matching-Verfahren im geographischen Kontext 3 Grundlagen 3.1 Ähnlichkeitsmaße 3.1.1 Geometrische Ähnlichkeit 3.1.2 Topologische Ähnlichkeit 3.1.3 Semantische Ähnlichkeit 3.2 Relationstypen 3.2.1 Relationen auf Objektebene 3.2.2 Relationen auf Schemaebene 3.3 Graphentheorie 3.3.1 Graph-Definitionen 3.3.2 Graph-Matching 3.3.3 Graph-Partitionierung / Graph-Cut 3.4 Ganzzahlige lineare Programmierung 4 Entwicklung von Data-Matching-Verfahren für verschiedene Objektgeometrien 4.1 Zuordnung von Polygonobjekten 4.1.1 Geometrischer Parameter 4.1.2 Heterogenitätsparameter 4.1.3 Erzeugung eines kombinierten Ergebnisses für das Schema-Matching 4.2 Zuordnung von unterschiedlichen Objektgeometrien 5 Entwicklung von Schema-Matching-Verfahren basierend auf Instanzdaten 5.1 Formale Problemdefinition 5.1.1 Synthetisches Beispiel 5.2 Einfache Lösungsverfahren 5.2.1 Beschränkung auf 1:1-Zuordnungen (Max-Match) 5.2.2 Beschränkung auf zwei Cluster (Min-Cut) 5.3 Einsatz von Heuristiken 5.4 Einsatz der ganzzahligen linearen Programmierung 5.4.1 Optimierungsziele und Bedingungen 5.4.2 Kombination von Optimierungszielen 5.4.3 Einführung einer festen Clustergröße (MaxScoreHardConstraintFixedSize) 5.4.4 Optimale Lösung ohne Nullcluster (MaxScoreHardConstraintFixedSizeNonEmpty) 5.4.5 Vereinfachtes Programm (MaxScoreHardConstraintFixedSizeUnique) 6 Experimente mit Realdaten und Untersuchungsergebnisse 6.1 Datenquellen und Datenvorverarbeitung 6.1.1 Datenquellen 6.1.2 Testgebiete 6.1.3 Datenvorverarbeitung 6.2 Ergebnisse des Data-Matching 6.2.1 Testgebiet A: ALKIS OSM in Hannover 6.2.2 Testgebiet B: ALKIS ATKIS in Hameln 6.2.3 Testgebiet C: ATKIS GDF in Hannover-Wedemark 6.2.4 Zusammenfassung der Data-Matching-Ergebnisse 6.3 Ergebnisse des Schema-Matching 6.3.1 Testgebiet B: ALKIS ATKIS in Hameln 6.3.2 Testgebiet A: ALKIS OSM in Hannover 6.3.3 Testgebiet C: ATKIS GDF in Hannover-Wedemark 6.3.4 Zusammenfassung aller Schema-Matching-Ergebnisse 7 Zusammenfassung und Ausblick , Kurzfassungen in Deutscher und Englischer Sprache
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  • 7
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    Global image orientation from pairwise relative orientations (2016)
    Hannover : Leibniz Universität Hannover
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    Call number: S 99.0139(328)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz-Universität Hannover ; 328
    Type of Medium: Series available for loan
    Pages: 151 Seiten , Illustrationen, Diagramme
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 328
    URL: Inhaltsverzeichnis
    URL: Volltext
    Language: English
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    Terrestrial laser scanning data analysis for deformation monitoring (2019)
    Hannover : Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
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    Call number: S 99.0139(353)
    In: Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, Nr. 353
    Type of Medium: Series available for loan
    Pages: xii, 116 Seiten , Illustrationen, Diagramme
    ISBN: 978-3-7696-5251-2 , 9783769652512
    ISSN: 0065-5325
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Universität Hannover Nr. 353
    Language: English
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2019 , Contents Declaration Abstract Zusammenfassung List of Figures List of Tables Abbreviation 1 Introduction 1.1 Motivation of the study 1.2 Proposal and content 2 On the application of TLS in deformation monitoring 2.1 Fundamentals of TLS 2.1.1 Range measurement system 2.1.2 Beam deflection system 2.2 Error sources for TLS measurements 2.2.1 Influence factors for the errors 2.2.2 State of the art in TLS calibration 2.3 Deformation monitoring with TLS measurements 2.3.1 Design of measurement scheme 2.3.2 Data collection 2.3.3 Data pre-processing 2.3.4 General methodology in TLS-based deformation monitoring 3 The influence of a simplified stochastic model on a congruence based deformation analysis 3.1 Modelling the deformation 3.1.1 Conventional deformation model (Descriptive model) 3.1.2 Advanced deformation model (Causal model) 3.2 Hypothesis test for congruency 3.3 Influence of simplified VCMs on the congruency test 4 On the stochasticity of TLS measurement 4.1 State of the art for the stochastic models of TLS measurements 4.2 Challenge of specifying variance-covariance values 4.3 Statistical evaluation of stochastic model 5 Approximating the 3D point clouds with B-spline models for deformation monitoring 5.1 State of the art on the approximation of 3D point clouds 5.2 B-spline approximation in a linear Gauss-Markov model 5.3 Model selection methodology based on hypothesis testing 5.4 Comparison between B-splines and polynomial approximation 6 Conclusion and Outlook Contributions of Authors Paper 1 Paper 2 Paper 3 Paper 4 Bibliography Curriculum Vitae Acknowledgement , Zusammenfassung in Englisch und Deutsch
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  • 9
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    Local and Large Scale InSAR Measurement of Ground Surface Deformation (2019)
    Hannover : Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
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    Call number: S 99.0139(354)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, Nr. 354
    Type of Medium: Series available for loan
    Pages: 155 Seiten , Illustrationen, Diagramme, Karten
    ISBN: 978-3-7696-5252-9 , 9783769652529
    ISSN: 0065-5325
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 354
    Language: English
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2019 , 1. Introduction 1.1. Research Objectives 1.2. Outline and Structure of the Thesis 2. Theoretical Background 2.1. Introduction 2.2. SAR Imaging 2.2.1. SAR Image Distortions 2.2.2. SAR Imaging Modes 2.2.3. SAR Missions 2.3. SAR Interferometry 2.3.1. InSAR Workflow 2.3.2. InSAR Decorrelation 2.3.3. Errors in InSAR 2.3.4. Examples of Interferograms 2.3.5. Decomposition of Line-of-Sight Measurements 2.4. Multi Temporal InSAR 2.4.1. Scattering Mechanisms in SAR Images 2.4.2. Interferogram Stacking 2.4.3. Persistent Scatterer InSAR 2.4.4. Small Baseline InSAR 2.5. Analysis of Displacement Time Series 2.5.1. Continuous Wavelet Transform 2.5.2. Cross Wavelet Transform 2.5.3. Application of CWT and XWT to InSAR Time Series 3. Methodological Contribution 37 3.1. Introduction 3.2. Challenges in Large-scale InSAR 3.3. Proposed Method 3.3.1. Interferogram Formation 3.3.2. Adaptive Correction of Interferograms 3.3.3. Estimating the Displacement Rate 3.3.4. Estimating the Time Series of Displacement 4. InSAR Monitoring of Localized Landslide in Taihape, New Zealand 4.1. Abstract 4.2. Introduction 4.3. Study Area 4.4. Methods 4.4.1. InSAR Measurement 4.4.2. Ancillary Data 4.4.3. Cause-Effect Analysis 4.5. Results 4.5.1. Small-baseline Interferograms 4.5.2. Time-series Results 4.6. Discussion 4.6.1. Suitability of InSAR Measurements for Monitoring the Taihape Landslide 4.6.2. Interpretation of InSAR Results 4.6.3. Comparison with Ground Truth 4.6.4. Comparison with Rainfall and Groundwater Level 4.7. Conclusion 4.8. Acknowledgments 4.9. Supplementary Materials 5. InSAR Measurement of Regional Land Subsidence in Tehran, Iran 5.1. Abstract 5.2. Introduction 5.3. Study Area and Problem Description 5.4. Datasets 5.4.1. SAR Data 5.4.2. Leveling 5.4.3. Groundwater Level 5.5. Methods 5.5.1. Multi-temporal InSAR Analysis 5.5.2. Merging InSAR Time Series 5.5.3. Cause-Effect Analysis 5.6. Results 5.6.1. Southwest of Tehran 5.6.2. IKA Airport 5.6.3. Varamin County 5.6.4. Time Series of Displacement 5.6.5. Accuracy, Precision and Consistency Assessments 5.7. Discussion 5.7.1. Structural Control of the Displacement 5.7.2. Comparison with Groundwater 5.7.3. Elastic vs. Inelastic Compaction 5.8. Conclusion 5.9. Acknowledgments 5.10. Supplementary materials 5.10.1. Significance of Tropospheric Delay 5.10.2. Decomposition of LOS Measurement 5.10.3. Under/Overestimation of Displacement Rates 6. Sentinel-1 InSAR Measurement of Anthropogenic Deformation in Germany 6.1. Summary 6.2. Introduction 6.3. Sentinel-1 InSAR Processing 6.4. Large-scale Sentinel-1 Processing 6.5. Anthropogenic Ground Motion in Berlin 6.6. Mining-induced Deformation in Leipzig 6.7. Conclusions and Prospect 6.8. Acknowledgements 7. Subsequent Work: Measurement of Localized Deformations over Extensive Areas 7.1. Introduction 7.2. SAR Datasets 7.3. Sentinel-1 Interferograms 7.4. Corrected Interferograms 7.5. Displacement Maps and Time Series 7.6. Discussion 7.7. Conclusion 8. Cooperation Works 8.1. Quantifying Land Subsidence in the Rafsanjan Plain, Iran Using InSAR Measurements 8.1.1. Abstract 8.1.2. Author Contribution 8.2. Characterizing Post-construction Settlement of Masjed-Soleyman Dam Using TerraSAR-X SpotLight InSAR 8.2.1. Abstract 8.2.2. Author Contribution 8.3. InSAR Observation of the 18 August 2014 Mormori (Iran) Earthquake 8.3.1. Author Contribution 9. Summary and Future Work 9.1. Future works , Zusammenfassung in Englisch und Deutsch Seite 3-6
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    Seismic noise analysis and isolation concepts for the ALPS II experiment at DESY (2020)
    Hannover : Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
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    Call number: S 99.0139(351)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, Nr. 351
    Type of Medium: Series available for loan
    Pages: xxix, 177 Seiten , Illustrationen, Diagramme
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 351
    URL: https://www.repo.uni-hannover.de/handle/123456789/9921
    URL: Table of Contents
    Language: English , German
    Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2019 , Contents 1. Introduction 1.1. Motivations and background 1.2. Research hypotheses and aims 1.3. Outline of this work 2. Fundamentals and theory of seismic noise 2.1. Fundamentals of mechanical vibration 2.1.1. Theory of oscillation 2.1.1.1. Oscillation and waves 2.1.1.2. Standing waves and resonance 2.1.1.3. Types of noise 2.1.1.4. Signal-to-Noise Ratio 2.1.2. The oscillatory systems 2.1.2.1. Mass-Spring-Damper model 2.1.2.2. Equation of motion 2.1.2.3. Free damped oscillation 2.1.2.4. Forced damped oscillation 2.1.3. Modal analysis 2.1.3.1. Fourier transform 2.1.3.2. Windowing 2.1.3.3. Averaging and overlapping 2.1.4. Data evaluation 2.1.4.1. Presenting spectra and spectral densities 2.1.4.2. RMS value in the frequency domain 2.1.4.3. Transfer function 2.1.4.4. Spectrogram 2.2. Seismic noise sources 2.2.1. Natural sources 2.2.1.1. Geodynamical aspects 2.2.1.2. Geological aspects at Hamburg, DESY 2.2.2. Human-made sources 2.2.2.1. Impact by stationary objects 2.2.2.2. Impact by traffic on site, machines and human work 2.2.2.3. Technical devices in the laboratory 2.3. Methods of seismic isolation 2.3.1. Passive constructions 2.3.1.1. Principle of a simple pendulum 2.3.1.2. Principle of a spring pendulum 2.3.1.3. The inverted pendulum concept 2.3.1.4. The anti-spring concept 2.3.1.5. The harmonic oscillator as transfer function 2.3.2. Control theory 2.3.2.1. Simple controller 2.3.2.2. Feed-forward controller 2.3.2.3. Feedback controller 2.3.2.4. Combined controller 3. The Any Light Particle Search experiment 3.1. ALPS and its seismic noise requirements 3.1.1. The physics of ALPS 3.1.2. Optical resonators 3.1.3. Control loop design 3.1.4. Frequency region and absolute length requirements 3.1.5. Infrastructure and status 3.2. Tools and techniques used for seismicmeasurements, analyses, and isolations 3.2.1. Seismic measuring instruments 3.2.1.1. Seismometers 3.2.1.2. Acquisition devices 3.2.1.3. Selected measurement chain 3.2.2. Data management and analyses 3.2.2.1. Notations for documentation 3.2.2.2. Analysing procedure 3.2.3. Finite Element Method simulation 3.2.3.1. Simple isolation simulations 3.2.3.2. Over-determined isolation systems 3.2.3.3. Selected FEM tools 4. Seismic noise analysis 57 4.1. Method of frequency-weighted and averaged FFT 4.1.1. Problem definition and motivation 4.1.2. The solution approaches 4.1.2.1. Stitching 4.1.2.2. LPSD 4.1.2.3. New solution approach 4.1.3. The MfwaFFT algorithm 4.1.3.1. Data preparation 4.1.3.2. FFT generation 4.1.3.3. Windowing of the iteration steps 4.1.3.4. Weighting 4.1.3.5. Summing up 4.1.4. Advantages and disadvantages 4.1.5. Discussion in the field of geodesy 4.2. Measurement Preparation 4.2.1. Calibration of seismic devices 4.2.1.1. Single instruments 4.2.1.2. Cross-calibration 4.2.2. Accuracy analysis 4.2.2.1. Measuring device accuracy and precision 4.2.2.2. Digital uncertainties and errors 4.3. Seismic measurements on-site 4.3.1. On-site noise conditions (HERA) 4.3.1.1. ALPS IIa laboratory (HERA West) 4.3.1.2. ALPS IIc site (HERA North) 4.3.1.3. Reference (HERA South) 4.3.2. Optic-related components of the ALPS II experiment 4.3.2.1. Optical tables 4.3.2.2. CBB and mirror mountings 4.3.3. Associated noise sources 4.3.3.1. Dipole magnet girders 4.3.3.2. Filter Fan Units 4.4. Filtering of signal 4.4.1. Spatial transfer functions 4.4.2. Low-pass filter due to the cavity pole frequency 4.4.3. Filter by the control loop 4.5. Data evaluation 4.5.1. Specifications for the ALPS IIa isolation 4.5.2. Specifications for an ALPS IIc isolation 4.5.3. Specifications for a JURA isolation 5. Development of seismic isolation systems 5.1. Procedure for handling seismic noise and isolation problems 5.2. State-of-the-art seismic isolation concepts 5.2.1. The LIGO system 5.2.2. The VIRGO system 5.3. Development of a seismic isolation system 5.3.1. CAD draft of a test model 5.3.2. FEM simulations 5.3.3. Design drawing 5.3.4. Evaluation and validation 5.4. Seismic isolation concept for ALPS IIc and JURA 6. Conclusion 6.1. Summary 6.2. Outlook , Sprache der Zusammenfassungen: Englisch, Deutsch
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