The discontinuities play an important role both in design and development stages of many geotechnical engineering projects. Because of that considerable time and capital should be spent to determine discontinuity sets by conventional methods. This thesis present the results of the studies associated with the application of the Remote Sensing (RS) and the development of a methodology in accurately and automatically detecting the discontinuity sets. For detection of the discontinuities, automatic lineament analysis is performed by using high resolution satellite imagery for identification of rock discontinuities. The study area is selected as an Andesite quarry area in G&ouml / lbaSi, Ankara, Turkey. For the high resolution data 8-bit Ikonos Precision Plus with 1 meter resolution orthorectified image is used. The automatic lineament extraction process is carried out with LINE module of PCI Geomatica v8.2. In order to determine the most accurate parameters of LINE, an accuracy assessment is carried out. To be the reference of the output, manual lineament extraction with directional filtering in four principal directions (N-S, E-W, NE-SW, NW-SE) is found to be the most suitable method. For the comparison of automatic lineament extraction and manual lineament extraction processes, LINECOMP program is coded in java environment. With the written code, a location and length based accuracy assessment is carried out. After the accuracy assesssment, final parameters of automatically extracted lineaments for rock discontinuity mapping for the study area are determined. Besides these, field studies carried out in the study area are also taken into consideration.

UAV (Unmanned Aircraft Vehicle) har revolutiontionerat ortofotoframställningen med sitt bidrag till ökad säkerhet, lägre kostnader samt effektivare arbetsgång vid framställning av ortofoton. Den traditionella flygfotogrammetrin med flygplan och utplacering av flygsignaler har varit den givna metoden i många år. Att flyga med UAV istället för flygplan sparar tid och pengar däremot är utplacering och inmätning av flygsignaler fortfarande tidskrävande och därför kostsamt. Företaget DJI har tagit fram en ny UAV med namnet DJI Phantom 4 RTK vilken stödjer möjligheten att använda satellitbaserad positionering för direkt georeferering. Den här studien har jämfört två olika georefereringsmetoder för framställning av ortofoton med UAV: direkt georeferering med NRTK (satellitbaserad positionering och nätverks-RTK) samt indirekt georeferering med olika antal markstödspunkter. Studien utfördes vid Högskolan i Gävle på en yta av åtta hektar. En undersökning av avvikelser i plan och höjd resulterade i acceptabla värden enligt de riktlinjer som följdes i HMK – Ortofoto (2017) samt de kontroller som genomfördes enligt SIS-TS 21144:2016. RMS-värdet i plan för den indirekta georefereringsmetoden ligger på 0,0102m. För den direkta georefereringsmetoden ligger RMS-värdet i plan vid användning av markstödpunkter mellan 0,0132 och 0,0148 m. Slutligen för den direkta georefereringsmetoden utan markstödpunkter är RMS-värdet i plan på 0,0136 m. RMS i höjd ligger inom intervallet 0,008-0,025 m. Det som redovisas i studien visar att en accepterad kvalitet av ortofoton går att erhålla baserat på de RMS-värden i plan och höjd med samtliga georefereringsmetoder som testats. Efter genomförda kontroller och utvärdering av de resultat kan det konstateras att de olika georefereringsmetoderna skiljer inte mycket åt varandra kvalitetsmässigt.Dock är den direkta georefereringsmetoden utan markstödpunkter mycket effektivare ur ett tidsperspektiv. Phantom 4 RTK är ny på marknaden och det behöver utföras mer forskning för att få en större insikt av dess potential. Dock krävs det mer forskning kring direkt georeferering för utvärdering av orotofotons kvalitet. / UAV (Unmanned Aircraft Vehicle) has revolutionized the creation of orthophotos with its contribution to increased safety, lower costs and more effective ways when making orthophotos. The traditional aerial photogrammetry with airplanes and placement of flight signals has been the standard method for years. To fly with UAV instead of an airplane is cheaper and saves time, however, the placement and measurements of flight signals is still time consuming and therefore expensive. The company DJI has developed a new UAV called Phantom 4 RTK that supports satellite based technology for direct georeferercing. This study compared two different measuring methods when producing orthophotos with UAV: direct georeferencing with NRTK (Network Real Time Kinematic) and indirect georeferencing when using different number of Ground Control Points (GCP). The study was conducted at the University of Gävle over an area of eight hectares. An investigation of the deviation in plane and height resulted in acceptable units based on the guidelines that were followed in HMK – Ortofoto and the controls that were followed from SIS- TS 21144:2016. The RMS value in plane for the indirect georeferencing method is 0,0102 m. For the direct georeferencing method the RMS value in plane when using ground control points is between 0,0132 and 0,0148 m. At last the RMS value for the direct georeferencing method without ground control points is 0,0136m. The RMS value in height is between the intervals 0,008-0,025 m. The data presented in this study show that an accepted quality in the orthophotos can be acquired based on the RMS values in plane and height for every georeferencing that was tested. After accomplished controls and evaluation the results show that the different georeferencing methods doesn´t differantiate too much from each other based on their quality. However, the direct georeferencing method with ground control points is more effective from a time perspective. Phantom 4 RTK is new on the market and more research is necessary in order to understand the potential of this technology and its posibility to integrate into society. More research is recquired for the direct georeferencing method in order to evaluate the quality of orthophotos.

Arterial stiffness is an important biometric in predicting cardiovascular diseases, since mechanical properties serve as indicators of several pathologies such as e.g. atherosclerosis. Shear Wave Elastography (SWE) could serve as a valuable non-invasive diagnostic tool for assessing arterial stiffness, with the technique proven efficient in large homogeneous tissue. However the accuracy within arterial applications is still uncertain, following the lack of proper validation. Therefore, the aim of this study was to assess the accuracy of SWE in arterial phantoms of poly(vinyl alcohol) cryogel by developing an experimental setup with an additional mechanical testing setup as a reference method. The two setups were developed to generate identical stress states on the mounted phantoms, with a combination of axial loads and static intraluminal pressures. The acquired radiofrequency-data was analysed in the frequency domain with retrieved dispersion curves fitted to a Lamb-wave based wave propagation model. The results indicated a significant correlation between SWE and mechanical measurements for the arterial phantoms, with an average relative error of 10 % for elastic shear moduli in the range of 23 to 108 kPa. The performed accuracy quantification implies a satisfactory performance level and as well as a general feasibility of SWE in arterial vessels, indicating the potential of SWE as a future cardiovascular diagnostic tool.

The GRP 5000 is a track measuring trolley capable of collecting track geometry data and performing clearance analyses based on laser scanning technology. The obtained laser data can also be used for as-built documentation, and the track recording functionality makes applications such as surveying, tamping assistance and slab track construction possible. The accuracy, huge data amount and time efficiency by which the system operates sets a new standard in railway surveying, and outdates traditional methods of manual and visual inspection. This thesis is a case study of the GRP 5000 with several objectives: a functional and technical description of the system is given; the accuracy of the system is evaluated, showing overall good values except for one of the sensors used; comparisons are made to other railway surveying techniques, such as track recording vehicles, manual devices and other track recording trolleys; possible improvements are pointed out, both based on comparison results as well as testing results. Finally, new and innovative ways of using the trolley, such as offtrack usage, as well as for the obtained laser data, such as GIS, maintenance aspects and CFD aspects, are examined.

Der Einfluss des Menschen verändert die Erdoberfläche in gravierendem Maße. Die Anwendung von Landnutzungsmodellen ist etabliert, um derartige Prozesse zu analysieren und um Handlungsempfehlungen für Entscheidungsträger zu geben. Landnutzungsmodelle stehen unter dem Einfluss von Unsicherheiten, welche beim Interpretieren der Ergebnisse berücksichtigt werden müssen. Dennoch gibt es wenige Ansätze, die unterschiedliche Unsicherheitsquellen mit ihren Interdependenzen untersuchen und ihre Auswirkungen auf die projizierte Änderung der Landschaft analysieren. Aus diesem Grund ist das erste Ziel dieser Arbeit einen systematischen Ansatz zu entwickeln, der wesentliche Unsicherheitsquellen analysiert und ihre Fortentwicklung zur resultierenden Änderungskarte untersucht. Eine andere Herausforderung in der Landnutzungsmodellierung ist es, die Eignung von Projektionen abzuschätzen wenn keine Referenzdaten vorliegen. Bayes’sche Netze wurden als eine vielseitige Methode identifiziert, um das erste Ziel zu erreichen. Darüber hinaus wurden die Modellierungsschritte „Definition der Modellstruktur“, „Auswahl der Eingangsdaten“ und „Weiterverarbeitung der Daten“ als wesentliche Unsicherheitsquellen identifiziert. Um das zweite Ziel zu adressieren wurde eine Auswahl an Maßzahlen entwickelt. Diese quantifizieren Unsicherheit mit Hilfe einer projizierten Änderungskarte und ohne den Vergleich mit Referenzdaten. Mit diesen Maßzahlen ist es zusätzlich möglich zwischen quantitativer und räumlicher Unsicherheit zu unterscheiden. Vor allem in räumlichen Anwendungen wie der Landnutzungsmodellierung ist diese Möglichkeit wertvoll. Dennoch kann auch ein absolut sicheres Modell gleichzeitig ein falsches und nutzloses Modell sein. Deswegen wird ein Ansatz empfohlen, der die Beziehung zwischen Unsicherheit und Genauigkeit in bekannten Zeitschritten schätzt. Die entwickelten Ansätze geben wichtige Informationen um die Eignung von modellierten Entwicklungspfaden der Landnutzung zu verstehen. / Human influence has led to substantial changes to the Earth’s surface. Land change models are widely applied to analyze land change processes and to give recommendations for decision-making. Land change models are affected by uncertainties which have to be taken into account when interpreting their results. However, approaches which examine different sources of uncertainty with regard to their interdependencies and their influence on projected land change are rarely applied. The first objective of this thesis is therefore to develop a systematic approach which identifies major sources of uncertainty and the propagation to the resulting land change map. Another challenge in land change modeling is the estimation of the reliability of land change predictions when no reference data are available. Bayesian Belief Networks were identified as a useful technique to reach the first objective. Moreover, the modeling steps of “model structure definition”, “data selection” and “data preprocessing” were detected as relevant sources of uncertainty. To address the second research objective, a set of measures based on probabilities were developed. They quantify uncertainty by means of a single predicted land change map without using a reference map. It is additionally possible to differentiate uncertainty into its spatial and quantitative components by means of these measures. This is especially useful in spatial applications such as land change modeling. However, even a certain model can be wrong and therefore useless. Therefore, an approach is suggested which estimates the relationship between disagreement and uncertainty in known time steps to use this relationship in future time steps. The approaches give important information for understanding the reliability of modeled future development paths of land change.

This thesis is motivated by the need for scalable and reliable methods and technologies that support the construction of network data based on information from text data. Ultimately, the resulting data can be used for answering substantive and graph-theoretical questions about socio-technical networks. One main limitation with constructing network data from text data is that the validation of the resulting network data can be hard to infeasible, e.g. in the cases of covert, historical and large-scale networks. This thesis addresses this problem by identifying the impact of coding choices that must be made when extracting network data from text data on the structure of networks and network analysis results. My findings suggest that conducting reference resolution on text data can alter the identity and weight of 76% of the nodes and 23% of the links, and can cause major changes in the value of commonly used network metrics. Also, performing reference resolution prior to relation extraction leads to the retrieval of completely different sets of key entities in comparison to not applying this pre-processing technique. Based on the outcome of the presented experiments, I recommend strategies for avoiding or mitigating the identified issues in practical applications. When extracting socio-technical networks from texts, the set of relevant node classes might go beyond the classes that are typically supported by tools for named entity extraction. I address this lack of technology by developing an entity extractor that combines an ontology for sociotechnical networks that originates from the social sciences, is theoretically grounded and has been empirically validated in prior work, with a supervised machine learning technique that is based on probabilistic graphical models. This thesis does not stop at showing that the resulting prediction models achieve state of the art accuracy rates, but I also describe the process of integrating these models into an existing and publically available end-user product. As a result, users can apply these models to new text data in a convenient fashion. While a plethora of methods for building network data from information explicitly or implicitly contained in text data exists, there is a lack of research on how the resulting networks compare with respect to their structure and properties. This also applies to networks that can be extracted by using the aforementioned entity extractor as part of the relation extraction process. I address this knowledge gap by comparing the networks extracted by using this process to network data built with three alternative methods: text coding based on thesauri that associate text terms with node classes, the construction of network data from meta-data on texts, such as key words and index terms, and building network data in collaboration with subject matter experts. The outcomes of these comparative analyses suggest that thesauri generated with the entity extractor developed for this thesis need adjustments with respect to particular categories and types of errors. I am providing tools and strategies to assist with these refinements. My results also show that once these changes have been made and in contrast to manually constructed thesauri, the prediction models generalize with acceptable accuracy to other domains (news wire data, scientific writing, emails) and writing styles (formal, casual). The comparisons of networks constructed with different methods show that ground truth data built by subject matter experts are hardly resembled by any automated method that analyzes text bodies, and even less so by exploiting existing meta-data from text corpora. Thus, aiming to reconstruct social networks from text data leads to largely incomplete networks. Synthesizing the findings from this work, I outline which types of information on socio-technical networks are best captured by what network data construction method, and how to best combine these methods in order to gain a more comprehensive view on a network. When both, text data and relational data, are available as a source of information on a network, people have previously integrated these data by enhancing social networks with content nodes that represent salient terms from the text data. I present a methodological advancement to this technique and test its performance on the datasets used for the previously mentioned evaluation studies. By using this approach, multiple types of behavioral data, namely interactions between people as well as their language use, can be taken into account. I conclude that extracting content nodes from groups of structurally equivalent agents can be an appropriate strategy for enabling the comparison of the content that people produce, perceive or disseminate. These equivalence classes can represent a variety of social roles and social positions that network members occupy. At the same time, extracting content nodes from groups of structurally coherent agents can be suitable for enabling the enhancement of social networks with content nodes. The results from applying the latter approach to text data include a comparison of the outcome of topic modeling; an efficient and unsupervised information extraction technique, to the outcomes of alternative methods, including entity extraction based on supervised machine learning. My findings suggest that key entities from meta-data knowledge networks might serve as proper labels for unlabeled topics. Also, unsupervised and supervised learning leads to the retrieval of similar entities as highly likely members of highly likely topics, and key nodes from text-based knowledge networks, respectively. In summary, the contributions made with this thesis help people to collect, manage and analyze rich network data at any scale. This is a precondition for asking substantive and graph-theoretical questions, testing hypotheses, and advancing theories about networks. This thesis uses an interdisciplinary and computationally rigorous approach to work towards this goal; thereby advancing the intersection of network analysis, natural language processing and computing.

<p dir="ltr">As a new spaceborne laser remote sensing system, the Global Ecosystem Dynamics Investigation, or GEDI, is being widely used for monitoring forest ecosystems. However, its measurements are subject to uncertainties that will affect the calculation of ground elevation and vegetation height. This research intends to investigate the quality of the GEDI elevation data and its relevance to topography and land cover.</p><p dir="ltr">In this study, the elevation of the GEDI data is compared to 3DEP DEM, which has a higher resolution and accuracy. All the experiments in this study are conducted for two locations with vastly different terrain and land cover conditions, namely Tippecanoe County in Indiana and Mendocino County in California. Through this investigation we expect to gain a comprehensive understanding of GEDI’s elevation quality in various terrain and land cover conditions.</p><p dir="ltr">The results show that GEDI data in Tippecanoe County has better elevation accuracy than the GEDI data in Mendocino County. GEDI in Tippecanoe County is almost four times more accurate than in Mendocino County. Regarding land cover, GEDI have better accuracy in low vegetation areas than in forest areas. The ratio can be around three times better in Tippecanoe County and around one and half times better in Mendocino County. In terms of slope, GEDI data shows a clear positive correlation between RMSE and slope. The trend indicates as slope increases, the RMSE increases concurrently. In other words, slope and GEDI elevation accuracy are inversely related. In the experiment involving slope and land cover, the results show that slope is the most influential factor to GEDI elevation accuracy.</p><p dir="ltr">This study informs GEDI users of the factors they must consider for forest biomass calculation and topographic mapping applications. When high terrain slope and/or high vegetation is present, the GEDI data should be checked with other data sources like 3DEP DEM or any ground truth measurements to assure its quality. We expect these findings can help worldwide users understand that the quality of GEDI data is variable and dependent on terrain relief and land cover.</p>

No description available.

空載光達系統於建置國土測繪基本資料扮演關鍵角色，依國土測繪法，為確保測繪成果品質，應依測量計畫目的及作業精度需求辦理儀器校正。國土測繪中心已於102年度建置航遙測感應器系統校正作業中，提出矩形建物之平屋頂面做為空載光達系統校正之可行性，而其所稱之校正，是以點雲精度評估待校件空載光達系統所得最終成果品質，並不對儀器做任何參數改正，但其校正成果可能因不同人員操作而有差異，因此本研究嘗試建立一套空載光達點雲半自動化精度評估程序，此外探討以山形屋脊線執行點雲精度評估之可行性。 由於光達點雲為離散的三維資訊，不論是以山形屋脊線或矩形建物之平屋頂面作為標物執行點雲精度評估，均須先萃取屋頂面上之點，為避免萃取成果受雜訊影響，本研究引入粗差偵測理論，發展最小一乘法結合李德仁以後驗變方估計原理導出的選擇權迭代法(李德仁法)將非屋頂點視為粗差排除。研究中分別對矩形建物之平屋頂面及山形屋脊線進行模擬及真實資料實驗，其中山形屋脊線作為點雲精度評估之可行性實驗中發現不適合用於評估點雲精度，因此後續實驗僅以萃取矩形建物之平屋頂面點雲過程探討粗差比率對半自動化點雲精度評估程序之影響。模擬實驗成果顯示最小一乘法有助於提升李德仁法偵測粗差數量5%至10%；真實資料實驗，以含有牆面點雲的狀況為例，則有助提升5%的偵測粗差數量。本研究由逐步測試結果提出能夠適用於真實狀況的半自動化之點雲精度評估程序，即使由不同人員操作，仍能獲得一致的成果，顯示本研究半自動化精度評估程序之可信度。 / The airborne LiDAR system plays a crucial role in building land surveying data. Based on the Land Surveying and Mapping Act, to ensure the quality of surveying, instrument calibration is required. The approach proposed by National Land Surveying and Mapping Center (NLSC) in 2013 was confirmed the feasibility for airborne LiDAR system calibration using rectangular horizontal roof plane. The calibration mean to assess the final quality of airborne LiDAR system based on the assessment of the accuracy of the point cloud, and do not adjust the instrument. But the results may vary according to different operators. This study attempts to establish a semi-automatic procedure for the accuracy assessment of point clouds from airborne LiDAR system. In addition, the gable roof ridge lines is discussed for its feasibility for the accuracy assessment of point cloud. No matter that calibration is performed using rectangular horizontal roof plane or gable roof ridge line, point clouds located on roof planes need to be extracted at first. Therefore, Least Absolute Deviation (LAD) combined with the Iteration using Selected Weights (Deren Li method) is developed to exclude the non-roof points which regarded as gross errors and eliminate their influences. The simulated test and actual data test found that gable roof ridge lines are not suitable for accuracy assessment. As for the simulated test using horizontal roof planes, LAD combined with Deren Li method prompts the rate of gross error detection about 5% to 10% than that only by Deren Li method. In actual test, data contains wall points, LAD combined with Deren Li method can prompt about 5%. Meanwhile, a semi-automatic procedure for real operations is proposed by the step-by-step test. Even different operators employ this semi-automatic procedure, consistent results will be obtained and the reliability can achieve.

There has been rapid change in the landcover/landuse in the Mekong delta, Viet Nam. The landcover/landuse has changed very fast due to intense population pressure, agriculture/aquaculture farming and timber collection in the coastal areas of the delta. The changing landuse pattern in the coastal areas of the delta is threatened to be flooded by sea level rise; sea level is expected to rise 33 cm until 2050; 45 cm until 2070 and 1 m until 2100. The coastline along the eastern Mekong delta has never been static, but the loss of mangrove forests along the coast has intensified coastline change. The objective of the present study is to map the changes in landcover/landuse along the eastern coast of the Mekong delta; and to detect the changes in position of the eastern coastline over the time period from 1989 to 2002.To detect changes in landuse, two satellite images of the same season, acquired by the TM sensor of Landsat 5 and the ETM+ sensor of Landsat 7 were used. The TM image was acquired on January 16, 1989 and ETM+ image was acquired on February 13, 2002. The landcover/landuse classes selected for the study are water, forest, open vegetation, soil and shrimp farms. Image differencing and post classification comparison are used to detect the changes between two time periods. Image to image correction technique is used to align satellite images. Maximum likelihood supervised classification technique is used to classify images. The result of the classification consists of five classes for 1989 and 2002, respectively. Overall accuracies of 87.5% and 86.8%, with kappa values of 0.85 and 0.84 are obtained for landuse 1989 and landuse 2002, respectively. The overall accuracy for the change map is 82% with kappa value 0.80. Post classification comparison is carried out in this study based on the supervised classification results. According to the results obtained from the post classification comparison, a significant decrease of 48% in forest and a significant increase of 74% in open vegetation and 21% in shrimp farms area observed over the entire study area. The coastline obtained by the combination of histogram thresholding and band ratio showed an overall advancement towards the South China Sea. The results showed that new land patches emerged along the eastern coast. The amount of new land patches appeared along the coast of the Mekong delta is approximately 2% of the entire study area.