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Note: Zusammenfassungen in deutscher und englischer Sprache

Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2017 , Contents 1 Introduction 1.1 Motivation, research questions and overview 1.1.1 Rainfall estimation at high spatial and temporal resolution 1.1.2 Precipitation estimation with cars 1.1.3 Motion estimation from in-situ sensor data 1.2 Outline 2 Basics 2.1 Precipitation 2.1.1 Resolution, accuracy and precision of precipitation measurements 2.1.2 In-situ point measurements of precipitation by rain gauges 2.1.3 Weather radar 2.2 Wireless Sensor Networks 2.2.1 Modeling sensor networks 2.2.2 Sensor network algorithms and protocols 2.3 Statistics 2.3.1 Basics and notation 2.3.2 Regression 2.3.3 Stochastic processes 2.3.4 Stochastic filtering and the Kalman filter 2.3.5 Geostatistics 2.4 Interpolation methods 2.4.1 Inverse-Distance-Weighted 2.4.2 Ordinary kriging 2.4.3 Regression kriging 2.4.4 Cross-validation for performance assessment 2.5 Optical flow 2.5.1 Optical flow intensity conservation 2.5.2 Gradient-based optical flow 2.5.3 Probabilistic optical flow 3 Related Work 3.1 Quantitative precipitation estimation from rain gauges, weather radar and other data sources 3.1.1 Precipitation estimation with weather radar 3.1.2 Precipitation estimation by interpolation of rain gauges measurements 3.1.3 Geostatistical merging of radar and rain gauge data 3.1.4 Motion-based methods used in nowcasting 3.1.5 New data sources for precipitation estimation 3.2 Decentralized estimation with geosensor networks 3.2.1 Estimation of spatio-temporal field properties with GSN 3.2.2 Object-tracking with GSN 4 Methodology for precipitation intensity estimation at 1-min resolution 4.1 Time-window approach for estimation 4.1.1 Estimation of field motion 4.1.2 Weather radar upsampling 4.1.3 Variogram estimation 4.2 Estimation methods 4.2.1 Spatial rain gauge interpolation methods 4.2.2 Space-time symmetric rain gauge interpolation method 4.2.3 Space-time asymmetric rain gauge interpolation methods 4.2.4 Radar-rain gauge merging methods 4.2.5 Estimation methods solely based on radar 4.3 Summary 5 Methodology for precipitation intensity estimation with car sensors 5.1 Car sensors 5.1.1 Wiper Frequency Sensor 5.1.2 Xanonex optical sensor 5.1.3 Other sensors investigated 5.1.4 Experimental setup and preprocessing 5.2 Theoretical considerations for the calibration of the W-R relationship in the field . 5.3 Dependency between car speed, windscreen angle and sensor readings 5.3.1 Manually-operated windscreen wipers 5.3.2 Automatically-operated windscreen wipers 5.3.3 Xanonex optical sensor 5.4 Summary 6 Methodology for motion estimation with a geosensor network 6.1 Algorithm overview 6.2 Network and field model 6.3 Gradient constraint estimation in the network 6.3.1 Gradient constraint estimation from irregular data 6.3.2 Requirements on node stationarity and sampling synchronicity 6.3.3 Estimation of partial derivative error 6.3.4 Gradient constraint selection and derivation of gradient constraint error 6.4 Temporal coherence: Kalman filter for recursive motion estimation 6.4.1 Estimation of process noise Q 6.4.2 Estimation of measurement noise R 6.4.3 Difference to common Kalman filtering problems 6.5 Algorithm protocol 6.6 Algorithm complexity 6.6.1 Communication complexity 6.6.2 Load balance 6.6.3 Computational complexity of partial derivative estimation 6.6.4 Computational complexity of motion estimation 6.7 Summary 7 Results 7.1 Precipitation intensity estimation at 1-min resolution 7.1.1 Study area and data basis 7.1.2 Performance assessment via cross-validation 7.1.3 Exploratory and visual data analysis 7.1.4 Radar estimation and rain gauge cross-validation results 7.1.5 Summary 7.2 Precipitation intensity estimation with cars 7.2.1 Study area and data basis 7.2.2 Selection of the reference method 7.2.3 Manually-operated windscreen wipers 7.2.4 Automatically-operated windscreen wipers 7.2.5 Xanonex optical rain sensor 7.2.6 Results of experiments on the VW rain track 7.2.7 Summary 7.3 Motion estimation with a geosensor network 7.3.1 Study Area, sensor network and deployment strategies 7.3.2 Error measures 7.3.3 Setting the filter parameters 7.3.4 Results - simulated field 7.3.5 Results - radar field 7.3.6 Summary 8 Summary and discussion of the research hypotheses 8.1 Discussion of research hypotheses 1 and 2: 1-min precipitation intensity estimation 8.2 Discussion of research hypothesis 3: precipitation estimation with cars 8.3 Discussion of research hypothesis 4: decentralized motion estimation 8.4 Outlook 9 Appendix 9.1 Discussion on the 'frozen field' distance function 9.2 Executable Kalman filter equations for the motion estimation algorithm 9.3 Controllability and Observability of the Kalman filter for motion estimation List of Figures List of Tables References

Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2018 , Contents List of figures List of tables 1 Introduction 1.1 Motivation 1.2 Meeting points for shared rides 1.3 Research questions 2 Fundamentals 2.1 Mathematical optimization 2.1.1 Linear Programming 2.1.2 Integer Programming 2.1.3 Combinatorial Optimization 2.1.4 Dynamic Programming 2.1.5 Set cover problem 2.2 Vehicle routing problems (VRP) 2.2.1 The basic VRP 2.2.2 Dial-a-ride problem (DARP) 2.3 Ride-Sharing 2.3.1 Mathematical formulation 2.3.2 Methods 2.3.3 Carpooling 3 Meeting points forshared rides: state of the art 3.1 Meeting points 3.1.1 Meeting points as destination 3.1.2 Intermediate meeting points 3.2 Knowledge gap 4 Real-world meeting points 4.1 Survey based on questionnaire 4.1.1 Setting 4.1.2 Results 4.2 Map-based survey 4.2.1 Setting 4.2.2 Results 5 Study area and data 5.1 Street network 5.2 Meeting point candidates 5.3 Public transport network 5.4 Demand 6 Meeting points for intra urban ride-sharing 6.1 Motivation 6.2 Basic matching problem 6.2.1 Mathematical model 6.2.2 Matching problem 6.3 Simulation experiments 6.3.1 Baseline scenario 6.3.2 Door-to-door service 6.3.3 Convenience-based matching 6.3.4 Meeting point reduction 6.4 Discussion 7 Meeting point recommendations for long-distance ride-sharing 7.1 Motivation 7.2 Proposed method 7.2.1 Preparation phase 7.2.2 Precomputing phase 7.2.3 Operational phase 7.3 Simulation experiment 7.3.1 Simulation setting 7.3.2 Results 7.4 Discussion 8 Meeting points for demand-responsive transportation 8.1 Motivation 8.2 Proposed method 8.2.1 Clustering 8.2.2 Meeting Point Candidates Selection 8.2.3 Route Optimization with Final Meeting Points Selection 8.3 Simulation experiment 8.3.1 Simulation setting 8.3.2 Results 8.4 Discussion 9 Conclusion Reference list Curriculum vitae

Note: Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2018 , 1 Einleitung 1.1 Hintergrund und Problemstellung 1.2 Ziel der Arbeit 1.2.1 Problemstellung: Erfassung von Trajektorien 1.2.2 Problemstellung: Erkennung von Bewegungsmustern in Trajektorien 1.3 Gliederung 2 Grundlagen 2.1 Modellierung von Objektbewegungen 2.2 Erfassung von Trajektorien 2.2.1 GNSS-Tracking 2.2.2 Videobasiertes Tracking 2.2.3 Vergleich des GNSS- und videobasierten Trackings 2.2.4 Weitere Tracking-Verfahren 2.2.5 Probabilistische Modellierung 2.2.6 Viterbi-Algorithmus 2.3 Erkennung von Bewegungsmustern 2.3.1 Data Mining 2.3.2 Filterung und Glättung 2.3.3 Segmentierung 2.3.4 Distanzmaße zur Bestimmung der Ähnlichkeit von Trajektorien 2.3.5 Maschinelles Lernen im Kontext raum-zeitlicher Daten 2.3.6 Sequenzmustererkennung 2.4 Dynamische Programmierung 2.5 Unterschiedliche Varianten der Datenverarbeitung 2.5.1 Zentrale und dezentrale Verarbeitung 2.5.2 Informationsaustausch 3 Stand der Forschung und verwandte Arbeiten 3.1 Erfassung von Trajektorien 3.1.1 Objektdetektion 3.1.2 Objekt-Tracking 3.1.3 Fusion heterogener Detektionen 3.1.4 Kommerzielle Systeme 3.1.5 Diskussion und Fazit 3.2 Mustererkennung in Trajektorien 3.2.1 Erkennung von wiederkehrenden unbekannten Mustern 3.2.2 Diskussion und Fazit 4 Erfassung von Trajektorien - GPS-unterstütztes Kamera-Tracking 4.1 Überblick über den Lösungsansatz 4.2 Sensoren und Eingangsdaten 4.2.1 GPS-Daten 4.2.2 Kameradaten 4.3 Vorverarbeitung 4.4 Fusion der heterogenen Daten 4.4.1 Detektionsbasierte Modellierung 4.4.2 Rasterbasierte Modellierung 4.4.3 Generierung der Trajektorien 4.5 Laufzeit des Algorithmus 4.6 System design 5 Mustererkennung in Trajektorien 5.1 Definition von Bewegungsmustern 5.2 Überblick über das entwickelte Mustererkennungsverfahren 5.3 Trajektorien als Datengrundlage 5.4 Vorverarbeitung 5.4.1 Datenbereinigung 5.4.2 Datenselektion 5.4.3 Datenintegration und Transformation 5.5 Mustererkennung: Clustering-basierter Ansatz 5.5.1 Segmentierung der Trajektorien 5.5.2 Clustering der Trajektorien 5.6 Mustererkennung: Sequenzbasierter Ansatz 5.6.1 Eingangsdaten 5.6.2 Generierung der Sequenzen aus Bewegungen 5.6.3 Bestimmung des Alphabets 5.6.4 Identifikation wiederkehrender Teilsequenzen 5.6.5 Rücktransformation zu Trajektorien 5.7 Laufzeit des Algorithmus 6 Experimente und Evaluation der Erfassung der Trajektorien 6.1 Verwendete Software 6.2 Verwendete Sensoren 6.3 Korrektheit der Zuordnungen 6.3.1 Experiment: 2 Personen 6.3.2 Experiment: Fußballanalyse 6.4 Geometrische Genauigkeit der Trajektorien 6.5 Laufzeit 6.6 Fazit 7 Experimente und Evaluation der Mustererkennung 7.1 Verwendete Software 7.2 Ergebnisverifikation 7.3 Interessantheitsmaß für Bewegungsmuster 7.4 Parameterstudien 7.4.1 Eingabeparameter 7.4.2 Datendichte 7.4.3 Invarianzen 7.5 Experimente auf realen Datensätzen 7.5.1 Beschreibung der Datensätze und Experimente 7.5.2 Experiment 1 - ACM DEBS 2013-Datensatz 7.5.3 Experiment 2 - GPS-Fußball-Datensatz 7.5.4 Experiment 3 - MapConstruction.org 7.5.5 Experiment 4 - Mantelpaviane 8 Zusammenfassung und Ausblick 8.1 Zusammenfassung 8.2 Ausblick Abbildungsverzeichnis Tabellenverzeichnis Literaturverzeichnis Lebenslauf Danksagung

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

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