1,784 research outputs found

    Assessing Building Vulnerability to Tsunami Hazard Using Integrative Remote Sensing and GIS Approaches

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    Risk and vulnerability assessment for natural hazards is of high interest. Various methods focusing on building vulnerability assessment have been developed ranging from simple approaches to sophisticated ones depending on the objectives of the study, the availability of data and technology. In-situ assessment methods have been widely used to measure building vulnerability to various types of hazards while remote sensing methods, specifically developed for assessing building vulnerability to tsunami hazard, are still very limited. The combination of remote sensing approaches with in-situ methods offers unique opportunities to overcome limitations of in-situ assessments. The main objective of this research is to develop remote sensing techniques in assessing building vulnerability to tsunami hazard as one of the key elements of risk assessment. The research work has been performed in the framework of the GITEWS (German-Indonesian Tsunami Early Warning System) project. This research contributes to two major components of tsunami risk assessment: (1) the provision of infrastructure vulnerability information as an important element in the exposure assessment; (2) tsunami evacuation modelling which is a critical element for assessing immediate response and capability to evacuate as part of the coping capacity analysis. The newly developed methodology is based on the combination of in-situ measurements and remote sensing techniques in a so-called โ€œbottom-up remote sensing approachโ€. Within this approach, basic information was acquired by in-situ data collection (bottom level), which was then used as input for further analysis in the remote sensing approach (upper level). The results of this research show that a combined in-situ measurement and remote sensing approach can be successfully employed to assess and classify buildings into 4 classes based on their level of vulnerability to tsunami hazard with an accuracy of more than 80 percent. Statistical analysis successfully revealed key spatial parameters which were regarded to link parameters between in-situ and remote sensing approach such as size, height, shape, regularity, orientation, and accessibility. The key spatial parameters and their specified threshold values were implemented in a decision tree algorithm for developing a remote sensing rule-set of building vulnerability classification. A big number of buildings in the study area (Cilacap city, Indonesia) were successfully classified into the building vulnerability classes. The categorization ranges from high to low vulnerable buildings (A to C) and includes also a category of buildings which are potentially suitable for tsunami vertical evacuation (VE). A multi-criteria analysis was developed that incorporates three main components for vulnerability assessment: stability, tsunami resistance and accessibility. All the defined components were configured in a decision tree algorithm by applying weighting, scoring and threshold definition based on the building sample data. Stability components consist of structure parameters, which are closely related to the building stability against earthquake energy. Building stability needs to be analyzed because most of tsunami events in Indonesia are preceded by major earthquakes. Stability components analysis was applied in the first step of the newly developed decision tree algorithm to evaluate the building stability when earthquake strikes. Buildings with total scores below the defined threshold of stability were classified as the most vulnerable class A. Such the buildings have a high probability of being damaged after earthquake events. The remaining buildings with total scores above the defined threshold of stability were further analyzed using tsunami components and accessibility components to classify them into the vulnerability classes B, C and VE respectively. This research is based on very high spatial resolution satellite images (QuickBird) and object-based image analysis. Object-based image analysis is was chosen, because it allows the formulation of rule-sets based on image objects instead of pixels, which has significant advantages especially for the analysis of very high resolution satellite images. In the pre-processing stage, three image processing steps were performed: geometric correction, pan-sharpening and filtering. Adaptive Local Sigma and Morphological Opening filter techniques were applied as basis for the subsequent building edge detection. The data pre-processing significantly increased the accuracy of the following steps of image classification. In the next step image segmentation was developed to extract adequate image objects to be used for further classification. Image classification was carried out by grouping resulting objects into desired classes based on the derived object features. A single object was assigned by its feature characteristics calculated in the segmentation process. The characteristic features of an object - which were grouped into spectral signature, shape, size, texture, and neighbouring relations - were analysed, selected and semantically modelled to classify objects into object classes. Fuzzy logic algorithm and object feature separation analysis was performed to set the memberยฌship values of objects that were grouped into particular classes. Finally this approach successfully detected and mapped building objects in the study area with their spatial attributes which provide base information for building vulnerability classification. A building vulnerability classification rule-set has been developed in this research and successfully applied to categorize building vulnerability classes. The developed approach was applied for Cilacap city, Indonesia. In order to analyze the transferability of this newly developed approach, the algorithm was also applied to Padang City, Indonesia. The results showed that the developed methodology is in general transferable. However, it requires some adaptations (e.g. thresholds) to provide accurate results. The results of this research show that Cilacap City is very vulnerable to tsunami hazard. Class A (very vulnerable) buildings cover the biggest portion of area in Cilacap City (63%), followed by class C (28%), class VE (6%) and class B (3%). Preventive measures should be carried out for the purpose of disaster risk reduction, especially for people living in such the most vulnerable buildings. Finally, the results were applied for tsunami evacuation modeling. The buildings, which were categorized as potential candidates for vertical evacuation, were selected and a GIS approach was applied to model evacuation time and evacuation routes. The results of this analysis provide important inputs to the disaster management authorities for future evacuation planning and disaster mitigation

    ํ›ˆ๋ จ ์ž๋ฃŒ ์ž๋™ ์ถ”์ถœ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๊ธฐ๊ณ„ ํ•™์Šต์„ ํ†ตํ•œ SAR ์˜์ƒ ๊ธฐ๋ฐ˜์˜ ์„ ๋ฐ• ํƒ์ง€

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€, 2021. 2. ๊น€๋•์ง„.Detection and surveillance of vessels are regarded as a crucial application of SAR for their contribution to the preservation of marine resources and the assurance on maritime safety. Introduction of machine learning to vessel detection significantly enhanced the performance and efficiency of the detection, but a substantial majority of studies focused on modifying the object detector algorithm. As the fundamental enhancement of the detection performance would be nearly impossible without accurate training data of vessels, this study implemented AIS information containing real-time information of vesselโ€™s movement in order to propose a robust algorithm which acquires the training data of vessels in an automated manner. As AIS information was irregularly and discretely obtained, the exact target interpolation time for each vessel was precisely determined, followed by the implementation of Kalman filter, which mitigates the measurement error of AIS sensor. In addition, as the velocity of each vessel renders an imprint inside the SAR image named as Doppler frequency shift, it was calibrated by restoring the elliptic satellite orbit from the satellite state vector and estimating the distance between the satellite and the target vessel. From the calibrated position of the AIS sensor inside the corresponding SAR image, training data was directly obtained via internal allocation of the AIS sensor in each vessel. For fishing boats, separate information system named as VPASS was applied for the identical procedure of training data retrieval. Training data of vessels obtained via the automated training data procurement algorithm was evaluated by a conventional object detector, for three detection evaluating parameters: precision, recall and F1 score. All three evaluation parameters from the proposed training data acquisition significantly exceeded that from the manual acquisition. The major difference between two training datasets was demonstrated in the inshore regions and in the vicinity of strong scattering vessels in which land artifacts, ships and the ghost signals derived from them were indiscernible by visual inspection. This study additionally introduced a possibility of resolving the unclassified usage of each vessel by comparing AIS information with the accurate vessel detection results.์ „์ฒœํ›„ ์ง€๊ตฌ ๊ด€์ธก ์œ„์„ฑ์ธ SAR๋ฅผ ํ†ตํ•œ ์„ ๋ฐ• ํƒ์ง€๋Š” ํ•ด์–‘ ์ž์›์˜ ํ™•๋ณด์™€ ํ•ด์ƒ ์•ˆ์ „ ๋ณด์žฅ์— ๋งค์šฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ๊ธฐ๊ณ„ ํ•™์Šต ๊ธฐ๋ฒ•์˜ ๋„์ž…์œผ๋กœ ์ธํ•ด ์„ ๋ฐ•์„ ๋น„๋กฏํ•œ ์‚ฌ๋ฌผ ํƒ์ง€์˜ ์ •ํ™•๋„ ๋ฐ ํšจ์œจ์„ฑ์ด ํ–ฅ์ƒ๋˜์—ˆ์œผ๋‚˜, ์ด์™€ ๊ด€๋ จ๋œ ๋‹ค์ˆ˜์˜ ์—ฐ๊ตฌ๋Š” ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ฐœ๋Ÿ‰์— ์ง‘์ค‘๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ํƒ์ง€ ์ •ํ™•๋„์˜ ๊ทผ๋ณธ์ ์ธ ํ–ฅ์ƒ์€ ์ •๋ฐ€ํ•˜๊ฒŒ ์ทจ๋“๋œ ๋Œ€๋Ÿ‰์˜ ํ›ˆ๋ จ์ž๋ฃŒ ์—†์ด๋Š” ๋ถˆ๊ฐ€๋Šฅํ•˜๊ธฐ์—, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„ ๋ฐ•์˜ ์‹ค์‹œ๊ฐ„ ์œ„์น˜, ์†๋„ ์ •๋ณด์ธ AIS ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธ๊ณต ์ง€๋Šฅ ๊ธฐ๋ฐ˜์˜ ์„ ๋ฐ• ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์‚ฌ์šฉ๋  ํ›ˆ๋ จ์ž๋ฃŒ๋ฅผ ์ž๋™์ ์œผ๋กœ ์ทจ๋“ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ด์‚ฐ์ ์ธ AIS ์ž๋ฃŒ๋ฅผ SAR ์˜์ƒ์˜ ์ทจ๋“์‹œ๊ฐ์— ๋งž์ถ”์–ด ์ •ํ™•ํ•˜๊ฒŒ ๋ณด๊ฐ„ํ•˜๊ณ , AIS ์„ผ์„œ ์ž์ฒด๊ฐ€ ๊ฐ€์ง€๋Š” ์˜ค์ฐจ๋ฅผ ์ตœ์†Œํ™”ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ด๋™ํ•˜๋Š” ์‚ฐ๋ž€์ฒด์˜ ์‹œ์„  ์†๋„๋กœ ์ธํ•ด ๋ฐœ์ƒํ•˜๋Š” ๋„ํ”Œ๋Ÿฌ ํŽธ์ด ํšจ๊ณผ๋ฅผ ๋ณด์ •ํ•˜๊ธฐ ์œ„ํ•ด SAR ์œ„์„ฑ์˜ ์ƒํƒœ ๋ฒกํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์œ„์„ฑ๊ณผ ์‚ฐ๋ž€์ฒด ์‚ฌ์ด์˜ ๊ฑฐ๋ฆฌ๋ฅผ ์ •๋ฐ€ํ•˜๊ฒŒ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ๊ณ„์‚ฐ๋œ AIS ์„ผ์„œ์˜ ์˜์ƒ ๋‚ด์˜ ์œ„์น˜๋กœ๋ถ€ํ„ฐ ์„ ๋ฐ• ๋‚ด AIS ์„ผ์„œ์˜ ๋ฐฐ์น˜๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์„ ๋ฐ• ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ›ˆ๋ จ์ž๋ฃŒ ํ˜•์‹์— ๋งž์ถ”์–ด ํ›ˆ๋ จ์ž๋ฃŒ๋ฅผ ์ทจ๋“ํ•˜๊ณ , ์–ด์„ ์— ๋Œ€ํ•œ ์œ„์น˜, ์†๋„ ์ •๋ณด์ธ VPASS ์ž๋ฃŒ ์—ญ์‹œ ์œ ์‚ฌํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ฐ€๊ณตํ•˜์—ฌ ํ›ˆ๋ จ์ž๋ฃŒ๋ฅผ ์ทจ๋“ํ•˜์˜€๋‹ค. AIS ์ž๋ฃŒ๋กœ๋ถ€ํ„ฐ ์ทจ๋“ํ•œ ํ›ˆ๋ จ์ž๋ฃŒ๋Š” ๊ธฐ์กด ๋ฐฉ๋ฒ•๋Œ€๋กœ ์ˆ˜๋™ ์ทจ๋“ํ•œ ํ›ˆ๋ จ์ž๋ฃŒ์™€ ํ•จ๊ป˜ ์ธ๊ณต ์ง€๋Šฅ ๊ธฐ๋ฐ˜ ์‚ฌ๋ฌผ ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ†ตํ•ด ์ •ํ™•๋„๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ œ์‹œ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ ์ทจ๋“ํ•œ ํ›ˆ๋ จ ์ž๋ฃŒ๋Š” ์ˆ˜๋™ ์ทจ๋“ํ•œ ํ›ˆ๋ จ ์ž๋ฃŒ ๋Œ€๋น„ ๋” ๋†’์€ ํƒ์ง€ ์ •ํ™•๋„๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ์ด๋Š” ๊ธฐ์กด์˜ ์‚ฌ๋ฌผ ํƒ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํ‰๊ฐ€ ์ง€ํ‘œ์ธ ์ •๋ฐ€๋„, ์žฌํ˜„์œจ๊ณผ F1 score๋ฅผ ํ†ตํ•ด ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ํ›ˆ๋ จ์ž๋ฃŒ ์ž๋™ ์ทจ๋“ ๊ธฐ๋ฒ•์œผ๋กœ ์–ป์€ ์„ ๋ฐ•์— ๋Œ€ํ•œ ํ›ˆ๋ จ์ž๋ฃŒ๋Š” ํŠนํžˆ ๊ธฐ์กด์˜ ์„ ๋ฐ• ํƒ์ง€ ๊ธฐ๋ฒ•์œผ๋กœ๋Š” ๋ถ„๋ณ„์ด ์–ด๋ ค์› ๋˜ ํ•ญ๋งŒ์— ์ธ์ ‘ํ•œ ์„ ๋ฐ•๊ณผ ์‚ฐ๋ž€์ฒด ์ฃผ๋ณ€์˜ ์‹ ํ˜ธ์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ๋ถ„๋ณ„ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด์™€ ํ•จ๊ป˜, ์„ ๋ฐ• ํƒ์ง€ ๊ฒฐ๊ณผ์™€ ํ•ด๋‹น ์ง€์—ญ์— ๋Œ€ํ•œ AIS ๋ฐ VPASS ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ์„ ๋ฐ•์˜ ๋ฏธ์‹๋ณ„์„ฑ์„ ํŒ์ •ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ ๋˜ํ•œ ์ œ์‹œํ•˜์˜€๋‹ค.Chapter 1. Introduction - 1 - 1.1 Research Background - 1 - 1.2 Research Objective - 8 - Chapter 2. Data Acquisition - 10 - 2.1 Acquisition of SAR Image Data - 10 - 2.2 Acquisition of AIS and VPASS Information - 20 - Chapter 3. Methodology on Training Data Procurement - 26 - 3.1 Interpolation of Discrete AIS Data - 29 - 3.1.1 Estimation of Target Interpolation Time for Vessels - 29 - 3.1.2 Application of Kalman Filter to AIS Data - 34 - 3.2 Doppler Frequency Shift Correction - 40 - 3.2.1 Theoretical Basis of Doppler Frequency Shift - 40 - 3.2.2 Mitigation of Doppler Frequency Shift - 48 - 3.3 Retrieval of Training Data of Vessels - 53 - 3.4 Algorithm on Vessel Training Data Acquisition from VPASS Information - 61 - Chapter 4. Methodology on Object Detection Architecture - 66 - Chapter 5. Results - 74 - 5.1 Assessment on Training Data - 74 - 5.2 Assessment on AIS-based Ship Detection - 79 - 5.3 Assessment on VPASS-based Fishing Boat Detection - 91 - Chapter 6. Discussions - 110 - 6.1 Discussion on AIS-Based Ship Detection - 110 - 6.2 Application on Determining Unclassified Vessels - 116 - Chapter 7. Conclusion - 125 - ๊ตญ๋ฌธ ์š”์•ฝ๋ฌธ - 128 - Bibliography - 130 -Maste

    Tele-Autonomous control involving contact

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    Object localization and its application in tele-autonomous systems are studied. Two object localization algorithms are presented together with the methods of extracting several important types of object features. The first algorithm is based on line-segment to line-segment matching. Line range sensors are used to extract line-segment features from an object. The extracted features are matched to corresponding model features to compute the location of the object. The inputs of the second algorithm are not limited only to the line features. Featured points (point to point matching) and featured unit direction vectors (vector to vector matching) can also be used as the inputs of the algorithm, and there is no upper limit on the number of the features inputed. The algorithm will allow the use of redundant features to find a better solution. The algorithm uses dual number quaternions to represent the position and orientation of an object and uses the least squares optimization method to find an optimal solution for the object's location. The advantage of using this representation is that the method solves for the location estimation by minimizing a single cost function associated with the sum of the orientation and position errors and thus has a better performance on the estimation, both in accuracy and speed, than that of other similar algorithms. The difficulties when the operator is controlling a remote robot to perform manipulation tasks are also discussed. The main problems facing the operator are time delays on the signal transmission and the uncertainties of the remote environment. How object localization techniques can be used together with other techniques such as predictor display and time desynchronization to help to overcome these difficulties are then discussed

    Classification of Plants Using Images of their Leaves

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    Plant recognition is a matter of interest for scientists as well as laymen. Computer aided technologies can make the process of plant recognition much easier; botanists use morphological features of plants to recognize them. These features can also be used as a basis for an automated classification tool. For example, images of leaves of different plants can be studied to determine effective algorithms that could be used in classifying different plants. In this thesis, those salient features of plant leaves are studied that may be used as a basis for plant classification and recognition. These features are independent of leaf maturity and image translation, rotation and scaling and are studied to develop an approach that produces the best classification algorithm. First, the developed algorithms are used to classify a training set of images; then, a testing set of images is used for verifying the classification algorithms

    Unique Measure for Geometrical Shape Object Detection-based on Area Matching

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    Object classifier often operates by making decisions based on the values of several shape properties measured from an image of the object. The paper introduces a unique definition of measure for 2-D geometrical object shape detection. Using this definition different object shapes can be identified on the basis of their degree of fitness parameter. Basically, we have fitted a 2-D polygon/curve on the object as a best fitted polygon/curve and computed the parameter degree of fitness which is the ratio of the matching area and non-matching area due to the fitted polygon/curve and the object both. The results show the effectiveness of the proposed measure.Defence Science Journal, 2012,ย 62(1), pp.58-66,ย DOI:http://dx.doi.org/10.14429/dsj.62.94

    Marked point processes for the automatic detection of bomb craters in aerial wartime images

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    Many countries were the target of air strikes during the Second World War. The aftermath of such attacks is felt until today, as numerous unexploded bombs or duds still exist in the ground. Typically, such areas are documented in so-called impact maps, which are based on detected bomb craters. This paper proposes a stochastic approach to automatically detect bomb craters in aerial wartime images that were taken during World War II. In this work, one aspect we investigate is the type of object model for the crater: we compare circles with ellipses. The respective models are embedded in the probabilistic framework of marked point processes. By means of stochastic sampling the most likely configuration of objects within the scene is determined. Each configuration is evaluated using an energy function which describes the conformity with a predefined model. High gradient magnitudes along the border of the object are favoured and overlapping objects are penalized. In addition, a term that requires the grey values inside the object to be homogeneous is investigated. Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing provides the global optimum of the energy function. Afterwards, a probability map is generated from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively, which results in an impact map. Our results, based on 22 aerial wartime images, show the general potential of the method for the automated detection of bomb craters and the subsequent automatic generation of an impact map. ยฉ Authors 2019

    Remote Sensing of the Oceans

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    This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

    Extended Object Tracking: Introduction, Overview and Applications

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    This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure
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