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Relevant bibliographies by topics / Spatial-semantic model

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Academic literature on the topic 'Spatial-semantic model'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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Journal articles on the topic "Spatial-semantic model"

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Li, Wenchao, Xin Liu, Chenggang Yan, Guiguang Ding, Yaoqi Sun, and Jiyong Zhang. "STS: Spatial–Temporal–Semantic Personalized Location Recommendation." ISPRS International Journal of Geo-Information 9, no. 9 (2020): 538. http://dx.doi.org/10.3390/ijgi9090538.

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The rapidly growing location-based social network (LBSN) has become a promising platform for studying users’ mobility patterns. Many online applications can be built based on such studies, among which, recommending locations is of particular interest. Previous studies have shown the importance of spatial and temporal influences on location recommendation; however, most existing approaches build a universal spatial–temporal model for all users despite the fact that users always demonstrate heterogeneous check-in behavior patterns. In order to realize truly personalized location recommendations, we propose a Gaussian process based model for each user to systematically and non-linearly combine temporal and spatial information to predict the user’s displacement from their currently checked-in location to the next one. The locations whose distances to the user’s current checked-in location are the closest to the predicted displacement are recommended. We also propose an enhancement to take into account category information of locations for semantic-aware recommendation. A unified recommendation framework called spatial–temporal–semantic (STS) is introduced to combine displacement prediction and the semantic-aware enhancement to provide final top-N recommendation. Extensive experiments over real datasets show that the proposed STS framework significantly outperforms the state-of-the-art location recommendation models in terms of precision and mean reciprocal rank (MRR).

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Han, Dongfeng, Wenhui Li, and Zongcheng Li. "Semantic image classification using statistical local spatial relations model." Multimedia Tools and Applications 39, no. 2 (2008): 169–88. http://dx.doi.org/10.1007/s11042-008-0203-6.

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Abburu, Sunitha. "Geospatial Semantic Query Engine for Urban Spatial Data Infrastructure." International Journal on Semantic Web and Information Systems 15, no. 4 (2019): 31–51. http://dx.doi.org/10.4018/ijswis.2019100103.

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The research aims at design and develop a special semantic query engine “CityGML Spatial Semantic Web Client (CSSWC)” that facilitates ontology-based multicriteria queries on CityGML data in OGC standard. Presently, there is no spatial method, spatial information infrastructure or any tool to establish the spatial semantic relationship between the 3D city objects in CityGML model. The present work establishes the spatial and semantic relationships between the 3DCityObjects and facilitates ontology-driven spatial semantic query engine on 3D city objects, class with multiple attributes, spatial semantic relations like crosses, nearby, etc., with all other city objects. This is a novel and original work practically implemented generic product for any 3D CityGML model on the globe. A user-friendly form-based interface is designed to compose effective ontology based GeoSPARQL query. CSSWC enhances CityGML applications performance through effective and efficient querying system.

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Wu, Tao, Jianxin Qin, and Yiliang Wan. "TOST: A Topological Semantic Model for GPS Trajectories Inside Road Networks." ISPRS International Journal of Geo-Information 8, no. 9 (2019): 410. http://dx.doi.org/10.3390/ijgi8090410.

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To organize trajectory data is a challenging issue for both studies on spatial databases and spatial data mining in the last decade, especially where there is semantic information involved. The high-level semantic features of trajectory data exploit human movement interrelated with geographic context, which is becoming increasingly important in representing and analyzing actual information contained in movements and further processing. This paper argues for a novel semantic trajectory model named TOST. It considers both semantic and geographic information of trajectory data happens along network infrastructure simultaneously. In TOST, a flexible intersection-based semantic representation is designed to express movement typically constrained by urban road networks by combining sets of local semantic details along the time axis. A relational schema based on this model was instantiated against real datasets, which illustrated the effectivity of our proposed model.

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Mościcka, Albina. "Europeana Data Model in GIS for movable heritage." Geografie 120, no. 4 (2015): 527–41. http://dx.doi.org/10.37040/geografie2015120040527.

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The paper proposes to use European resources in GIS as a set of multi-spatial objects with semantic relations to the space. It improves the analysis and visualization of geographic or contextual associations between various items. This paper aims to integrate the Europeana Data Model with GIS for movable heritage based on semantic relations of movable objects with the space. All classes and properties of the EDM were analyzed. Classes and properties containing spatial information were examined and their semantic relations to the space were proposed. All aspects of the relations of movable heritage objects and space were taken into consideration, and examples of the GIS-based pilot resources saved with the use of EDM rules are proposed.

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Han, Hui-Hui, and Lei Fan. "A New Semantic Segmentation Model for Supplementing More Spatial Information." IEEE Access 7 (2019): 86979–88. http://dx.doi.org/10.1109/access.2019.2915088.

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Wang, Shiwei, Long Lan, Xiang Zhang, and Zhigang Luo. "GateCap: Gated spatial and semantic attention model for image captioning." Multimedia Tools and Applications 79, no. 17-18 (2020): 11531–49. http://dx.doi.org/10.1007/s11042-019-08567-0.

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Jiang, Peilong, and Xiao Ke. "Lightweight spatial pyramid pooling network for real-time semantic segmentation." Journal of Physics: Conference Series 2234, no. 1 (2022): 012012. http://dx.doi.org/10.1088/1742-6596/2234/1/012012.

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Abstract In recent years, the state-of-the-art semantic segmentation models have made extremely successful in various challenging scenes. However, the high computation costs of these models make it difficult to deploy to mobile devices. To better serve in computation constraint scenes, the semantic segmentation model should not only have high segmentation performance, but also fast inference speed. In this paper, we proposed an efficient multi-scale context module named LSPPM, which can gather abundant context information at a low computation cost. Base on this, we present a real-time semantic segmentation model called LSPPNet which is specially designed for real-time application. We have done an exhaustive experiment to evaluate LSPPNet in the challenge urban street scenes datasets Cityscapes. Extensive experiment shows that LSPPNet gets a better trade-off between segmentation performance and inference speed. We test LSPPNet on an NVIDIA 2080 super graphics card and it can achieve 75.8% MIoU in Cityscapes test set in real-time speed.

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Feng, Jiangfan, Xuejun Fu, Yao Zhou, Yuling Zhu, and Xiaobo Luo. "Image-Text Joint Learning for Social Images with Spatial Relation Model." Complexity 2020 (March 28, 2020): 1–11. http://dx.doi.org/10.1155/2020/1543947.

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The rapid developments in sensor technology and mobile devices bring a flourish of social images, and large-scale social images have attracted increasing attention to researchers. Existing approaches generally rely on recognizing object instances individually with geo-tags, visual patterns, etc. However, the social image represents a web of interconnected relations; these relations between entities carry semantic meaning and help a viewer differentiate between instances of a substance. This article forms the perspective of the spatial relationship to exploring the joint learning of social images. Precisely, the model consists of three parts: (a) a module for deep semantic understanding of images based on residual network (ResNet); (b) a deep semantic analysis module of text beyond traditional word bag methods; (c) a joint reasoning module from which the text weights obtained using image features on self-attention and a novel tree-based clustering algorithm. The experimental results demonstrate the effectiveness of using Flickr30k and Microsoft COCO datasets. Meanwhile, our method considers spatial relations while matching.

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Wang, Peng, Jing Yang, and Jianpei Zhang. "A Spatial-Temporal-Semantic Method for Location Prediction in Indoor Spaces." Wireless Communications and Mobile Computing 2022 (March 3, 2022): 1–13. http://dx.doi.org/10.1155/2022/5210005.

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While a large number of works concentrated on forecasting trajectories in the outdoor environment, predicting the movement of users in indoor settings has attracted much more attention recently because of the development of smartphones and maturity of Wi-Fi services, e.g., in office buildings. Predicting a user’s movement in indoor spaces can not only help better understand his/her intentions but also improve his/her living experience. While most of the prediction approaches to date tackle the problem by constructing the mathematical models to learn the mobility of objects, they cannot efficiently model the movement of indoor users in the constraint but filled with spatial-temporal-semantic info settings. In order to solve this issue, we propose a frequent subtrajectory-based Markov model that incorporates the spatial location, the temporal aspect, and the shop category context into a unified framework. We first present the frequent subtrajectory algorithm to model and predict adjacent moving points from physical movement perspective, and then, by taking the duration of stay at a specific location into account, we further improve the prediction precision. Finally, by taking location context in the indoor environment (e.g., shop categories) into consideration, we successfully model and predict the user’s future visiting points from the semantic perspective. To validate the effectiveness of our model, we conduct a complete evaluation on a large-scale real-world dataset with more than 261,269 trajectories collected from over 120,000 customers in a shopping mall. The experiment results demonstrate that our method performs significantly superior prediction performance comparing the state-of-the-art models.

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Dissertations / Theses on the topic "Spatial-semantic model"

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Hale, Denise Ann. "The development of semantic memory : a spatial model of animal concepts in schoolchildren, novices and experts." Thesis, London Metropolitan University, 1991. http://repository.londonmet.ac.uk/3390/.

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Cribbin, Timothy Frederick. "Classifying complex topics using spatial-semantic document visualization : an evaluation of an interaction model to support open-ended search tasks." Thesis, Brunel University, 2005. http://bura.brunel.ac.uk/handle/2438/3296.

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In this dissertation we propose, test and develop a novel search interaction model to address two key problems associated with conducting an open-ended search task within a classical information retrieval system: (i) the need to reformulate the query within the context of a shifting conception of the problem and (ii) the need to integrate relevant results across a number of separate results sets. In our model the user issues just one highrecall query and then performs a sequence of more focused, distinct aspect searches by browsing the static structured context of a spatial-semantic visualization of this retrieved document set. Our thesis is that unsupervised spatial-semantic visualization can automatically classify retrieved documents into a two-level hierarchy of relevance. In particular we hypothesise that the locality of any given aspect exemplar will tend to comprise a sufficient proportion of same-aspect documents to support a visually guided strategy for focused, same-aspect searching that we term the aspect cluster growing strategy. We examine spatial-semantic classification and potential aspect cluster growing performance across three scenarios derived from topics and relevance judgements from the TREC test collection. Our analyses show that the expected classification can be represented in spatial-semantic structures created from document similarities computed by a simple vector space text analysis procedure. We compare two diametrically opposed approaches to layout optimisation: a global approach that focuses on preserving the all similarities and a local approach that focuses only on the strongest similarities. We find that the local approach, based on a minimum spanning tree of similarities, produces a better classification and, as observed from strategy simulation, more efficient aspect cluster growing performance in most situations, compared to the global approach of multidimensional scaling. We show that a small but significant proportion of aspect clustering growing cases can be problematic, regardless of the layout algorithm used. We identify the characteristics of these cases and, on this basis, demonstrate a set of novel interactive tools that provide additional semantic cues to aid the user in locating same-aspect documents.

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Солонская, Светлана Владимировна. "Модели, метод и информационная технология обработки сигналов в интеллектуальных радиолокационных комплексах". Thesis, Харьковский национальный университет радиоэлектроники, 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/23588.

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Диссертация на соискание ученой степени кандидата технических наук по специальности 05.13.06 – информационные технологии. – Национальный технический университет "Харьковский политехнический институт", Харьков, 2016. Диссертация посвящена решению научно-практической задачи разработки метода для повышения эффективности обнаружения и распознавания сигналов в радиолокационных комплексах путем интеллектуализации обработки сигнальной информации. В работе проанализированы научные достижения в области обработки сигналов, определены задачи обработки сигналов и подходы к их решению. В технологии обработки радиолокационных сигналов предложено выделить два этапа: внутриобзорная и междуобзорная обработка сигналов. На основе данного подхода разработаны спектрально-семантическая и пространственно-семантическая модели обработки радиолокационных сигналов. В работе усовершенствован метод формализации процессов восприятия и преобразования сигналов и сигнальных образов, который основан на компараторной идентификации, и позволяет определять семантическую составляющую сигналов и сигнальных образов на этапе предварительной обработки информации. Предложена реализация информационной технологии обработки сигналов в интеллектуальных радиолокационных комплексах с учетом спектрально-семантической и пространственно-семантической моделей. Данный подход позволяет моделировать процессы обработки и распознавания радиолокационных сигналов и сигнальных образов средствами алгебры конечных предикатов. На этапе внутриобзорной обработки сигналов и сигнальных образов объекты классифицируются по спектральному образу с помощью спектрально-семантической модели. Предварительная обработка пачки сигналов основана на формировании предикатной формы спектрального образа, затем на ее основе определяются значения признаков, и осуществляется идентификация объекта. На этапе междуобзорной обработки сигналов для уточнения результатов идентификации объектов используется пространственно-семантическая модель. Рассматривается система дискретных выборок – элементов обработки по дальности и азимуту. Для описания ситуации вокруг анализируемого в данный момент элемента изображения (элемента зоны обзора РЛС), вводится система предикатных признаков. По оценке значений признаков в каждом элементе обработки и полученным предикатным уравнениям определяется воздушный объект. Предложенная модель позволяет определять отметки воздушных объектов на фоне мешающих отражений и наблюдать динамику изменения в течение нескольких обзоров РЛС.<br>Thesis for a candidate degree in technical science, specialty 05.13.06 – Information Technologies. – National Technical University "Kharkiv Polytechnic Institute". – Kharkiv, 2016. This thesis deals with a topical theoretical and practical task to improve the efficiency of information technologies for the processing and identifying of radar signals. Scientific achievements in signal processing are analysed, tasks to process signals and approaches to their solution are determined in the thesis. It is proposed to distinguish two stages in the technology of radar signal processing: intrasurveillance and intersurveillance signal processing. On the basis of this approach, spectral-semantic and spatial-semantic models are developed. Testing and the evaluation of the research results, which are based on the information technology developed, are made. The results are put into practice in: the module of multisurveillance processing of radar signals and data for surveillance radars of the Ministry of Defence of Ukraine; the research project Development of Systems of Radiomonitoring and Passive Direction Finding; Scientific Production Firm Optima Ltd.; an educational process of the Department of Information Technologies and Mechatronics in Kharkov National Automobile and Highway University.

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Солонська, Світлана Володимирівна. "Моделі, метод та інформаційна технологія обробки сигналів в інтелектуальних радіолокаційних комплексах". Thesis, НТУ "ХПІ", 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/23586.

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Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.06 – інформаційні технології. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2016. У дисертаційній роботі вирішена науково-практична задача розроблення методу для підвищення ефективності виявлення та розпізнавання сигналів в радіолокаційних комплексах шляхом інтелектуалізації обробки сигнальної інформації. У роботі проаналізовано наукові досягнення в галузі обробки сигналів, визначено задачі обробки сигналів та підходи до їх вирішення. У технології обробки радіолокаційних сигналів запропоновано виділити два етапи: внутрішньооглядова й міжоглядова обробка сигналів. На основі цього підходу створено спектрально-семантичну і просторово-семантичну моделі обробки радіолокаційних сигналів. Проведено апробацію й оцінку ефективності результатів дослідження, отриманих на базі розробленої інформаційної технології. Результати впроваджено в модулі багатооглядової обробки радіолокаційних сигналів та інформації для оглядових РЛС МО України, у науково-дослідному проекті "Розробка систем радіоконтролю, радіомоніторингу та систем пасивної пеленгації" ТОВ НПФ "Оптима", а також у навчальний процес кафедри інформаційних технологій та мехатроніки ХНАДУ.<br>Thesis for a candidate degree in technical science, specialty 05.13.06 – Information Technologies. – National Technical University "Kharkiv Polytechnic Institute". – Kharkiv, 2016. This thesis deals with a topical theoretical and practical task to improve the efficiency of information technologies for the processing and identifying of radar signals. Scientific achievements in signal processing are analysed, tasks to process signals and approaches to their solution are determined in the thesis. It is proposed to distinguish two stages in the technology of radar signal processing: intrasurveillance and intersurveillance signal processing. On the basis of this approach, spectral-semantic and spatial-semantic models are developed. Testing and the evaluation of the research results, which are based on the information technology developed, are made. The results are put into practice in: the module of multisurveillance processing of radar signals and data for surveillance radars of the Ministry of Defence of Ukraine; the research project Development of Systems of Radiomonitoring and Passive Direction Finding; Scientific Production Firm Optima Ltd.; an educational process of the Department of Information Technologies and Mechatronics in Kharkov National Automobile and Highway University.

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Farrugia, James A. "Semantic Interoperability of Geospatial Ontologies: A Model-theoretic Analysis." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/FarrugiaJA2007.pdf.

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Kvasova, Daria 1989. "The Role of cross-modal semantic interactions in real-world visuo-spatial attention." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/668665.

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In our everyday life we must effectively orient attention to relevant objects and events in multisensory environments. The impact of cross-modal links for attention orienting to spatial and temporal cues has been widely described. However, real-life scenarios provide a rich web of semantic information through the different sensory modalities. Despite some previous studies have revealed an impact of crossmodal sematic correspondences, the results are mixed with regard to the conditions in which audiovisual semantic congruence can influence attention orienting. Furthermore, the vast majority of the research on crossmodal semantics used simple, stereotyped displays that are far from achieving ecological validity. The present thesis attempts to close this gap by addressing the role of identity-based crossmodal relationships on attention orienting in scenarios closer to real-world conditions. To this end, the experiments presented here attempt to extrapolate and generalize previous findings in more realistic environments by using naturalistic and dynamic stimuli, and address the theoretical questions of task relevance and perceptual load. The outcome of the three empirical studies in this thesis lead to several conclusions. First, that the effect of audio-visual semantic congruence on attention is not strictly automatic. Instead, they suggest that some top-down processing is necessary for audio-visual semantic congruence to trigger spatial orienting. The second conclusion to emerge is that crossmodal semantic congruence can guide attention under goal-directed conditions in visual search, and also under free observation in complex and dynamic scenes. Third, that perceptual load is a limiting factor for these interactions. These findings extend previous knowledge on object-based crossmodal interactions with simple stimuli and clarify how audio-visual semantically congruent relationships play out in realistic scenarios.<br>En nuestra vida cotidiana debemos orientar efectivamente la atención a objetos y eventos relevantes en entornos multisensoriales. El impacto que tienen los enlaces intermodales en la orientación de la atención a señales espaciales y temporales ha sido ampliamente descrito. Sin embargo, los escenarios de la vida real proporcionan una rica red de información semántica a través de las diferentes modalidades sensoriales. A pesar de que algunos estudios previos han revelado un impacto de las correspondencias semánticas entre modalidades, los resultados se mezclan con respecto a las condiciones en que la congruencia semántica audiovisual puede influir en la orientación de la atención. Además, la gran mayoría de la investigación sobre semántica intermodal utilizó representaciones simples y estereotipadas que están lejos de alcanzar la validez ecológica. La presente tesis intenta llenar esta brecha al abordar el papel que las relaciones intermodales basadas en la identidad tienen en la orientación de la atención en escenarios más cercanos a las condiciones del mundo real. Con este fin, los experimentos presentados aquí intentan extrapolar y generalizar hallazgos previos en entornos más realistas mediante el uso de estímulos naturales y dinámicos, y abordar cuestiones teóricas como la relevancia de la tarea y la carga perceptiva. El resultado de los tres estudios empíricos de esta tesis condujo a varias conclusiones. Primero, que el efecto de la congruencia semántica audiovisual en la atención no es estrictamente automático. En cambio, sugieren que es necesario un procesamiento de arriba hacia abajo para que la congruencia semántica audiovisual desencadene en la orientación espacial. La segunda conclusión que surge es que la congruencia semántica intermodal puede guiar la atención en condiciones de búsqueda visual dirigida a un objetivo, y también bajo observación libre en escenas complejas y dinámicas. Tercero, la carga perceptiva es un factor limitante para estas interacciones. Estos hallazgos amplían el conocimiento previo sobre las interacciones intermodales basadas en objetos usando estímulos simples y aclaran cómo las relaciones audiovisuales semánticamente congruentes se desarrollan en escenarios realista

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Kang, Hyunmo. "Managing and exploring media using semantic regions a spatial interface supporting user-defined mental models /." College Park, Md. : University of Maryland, 2003. http://hdl.handle.net/1903/48.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2003.<br>Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Chen, Xi. "Learning with Sparcity: Structures, Optimization and Applications." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/228.

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The development of modern information technology has enabled collecting data of unprecedented size and complexity. Examples include web text data, microarray & proteomics, and data from scientific domains (e.g., meteorology). To learn from these high dimensional and complex data, traditional machine learning techniques often suffer from the curse of dimensionality and unaffordable computational cost. However, learning from large-scale high-dimensional data promises big payoffs in text mining, gene analysis, and numerous other consequential tasks. Recently developed sparse learning techniques provide us a suite of tools for understanding and exploring high dimensional data from many areas in science and engineering. By exploring sparsity, we can always learn a parsimonious and compact model which is more interpretable and computationally tractable at application time. When it is known that the underlying model is indeed sparse, sparse learning methods can provide us a more consistent model and much improved prediction performance. However, the existing methods are still insufficient for modeling complex or dynamic structures of the data, such as those evidenced in pathways of genomic data, gene regulatory network, and synonyms in text data. This thesis develops structured sparse learning methods along with scalable optimization algorithms to explore and predict high dimensional data with complex structures. In particular, we address three aspects of structured sparse learning: 1. Efficient and scalable optimization methods with fast convergence guarantees for a wide spectrum of high-dimensional learning tasks, including single or multi-task structured regression, canonical correlation analysis as well as online sparse learning. 2. Learning dynamic structures of different types of undirected graphical models, e.g., conditional Gaussian or conditional forest graphical models. 3. Demonstrating the usefulness of the proposed methods in various applications, e.g., computational genomics and spatial-temporal climatological data. In addition, we also design specialized sparse learning methods for text mining applications, including ranking and latent semantic analysis. In the last part of the thesis, we also present the future direction of the high-dimensional structured sparse learning from both computational and statistical aspects.

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Books on the topic "Spatial-semantic model"

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Regier, Terry. The human semantic potential: Spatial language and constrained connectionism. MIT Press, 1996.

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Regier, Terry. Human Semantic Potential: Spatial Language and Constrained Connectionism. MIT Press, 2019.

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Vallar, Giuseppe, and Nadia Bolognini. Unilateral Spatial Neglect. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.012.

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Left unilateral spatial neglect is the most frequent and disabling neuropsychological syndrome caused by lesions to the right hemisphere. Over 50% of right-brain-damaged patients show neglect, while right neglect after left-hemispheric damage is less frequent. Neglect patients are unable to orient towards the side contralateral to the lesion, to detect and report sensory events in that portion of space, as well as to explore it by motor action. Neglect is a multicomponent disorder, which may involve the contralesional side of the body or of extra-personal physical or imagined space, different sensory modalities, specific domains (e.g. ‘neglect dyslexia’), and worsen sensorimotor deficits. Neglect is due to higher-order unilateral deficits of spatial attention and representation, so that patients are not aware of contralesional events, which, however, undergo a substantial amount of unconscious processing up to the semantic level. Cross-modal sensory integration is also largely preserved. Neglect is primarily a spatially specific disorder of perceptual consciousness. The responsible lesions involve a network including the fronto-temporo-parietal cortex (particularly the posterior-inferior parietal lobe, at the temporo-parietal junction), their white matter connections, and some subcortical grey nuclei (thalamus, basal ganglia). Damage to primary sensory and motor regions is not associated to neglect. A variety of physiological lateralized and asymmetrical sensory stimulations (vestibular, optokinetic, prism adaptation, motor activation), and transcranial electrical and magnetic stimulations, may temporarily improve or worsen neglect. Different procedures have been successfully developed to rehabilitate neglect, using both ‘top down’ (training the voluntary orientation of attention) and ‘bottom up’ (the above-mentioned stimulations) approaches.

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Book chapters on the topic "Spatial-semantic model"

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Harbelot, Benjamin, Helbert Arenas, and Christophe Cruz. "A Semantic Model to Query Spatial–Temporal Data." In Lecture Notes in Geoinformation and Cartography. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31833-7_5.

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Phan, T. V., G. T. Anh Nguyen, and Trung Tran Do Quoc. "Management of Buildings with Semantic and 3D Spatial Properties by S_EUDM Data Model." In Lecture Notes in Civil Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5144-4_89.

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Koner, Rajat, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, and Stephan Günnemann. "Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering." In The Semantic Web – ISWC 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88361-4_7.

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AbstractVisual Question Answering (VQA) is concerned with answering free-form questions about an image. Since it requires a deep semantic and linguistic understanding of the question and the ability to associate it with various objects that are present in the image, it is an ambitious task and requires multi-modal reasoning from both computer vision and natural language processing. We propose Graphhopper, a novel method that approaches the task by integrating knowledge graph reasoning, computer vision, and natural language processing techniques. Concretely, our method is based on performing context-driven, sequential reasoning based on the scene entities and their semantic and spatial relationships. As a first step, we derive a scene graph that describes the objects in the image, as well as their attributes and their mutual relationships. Subsequently, a reinforcement learning agent is trained to autonomously navigate in a multi-hop manner over the extracted scene graph to generate reasoning paths, which are the basis for deriving answers. We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs. Our results show that we keep up with human performance on manually curated scene graphs. Moreover, we find that Graphhopper outperforms another state-of-the-art scene graph reasoning model on both manually curated and automatically generated scene graphs by a significant margin.

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Van Pham, Dang. "Proposing Spatial - Temporal - Semantic Data Model Managing Genealogy and Space Evolution History of Objects in 3D Geographical Space." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67101-3_13.

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Kolbe, Thomas H., and Andreas Donaubauer. "Semantic 3D City Modeling and BIM." In Urban Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_34.

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AbstractSemantic 3D city modeling and building information modeling (BIM) are methods for modeling, creating, and analyzing three-dimensional representations of physical objects of the environment. Digital modeling of the built environment has been approached from at least four different domains: computer graphics and gaming, planning and construction, urban simulation, and geomatics. This chapter introduces the similarities and differences of 3D models from these disciplines with regard to aspects like scale, level of detail, representation of spatial and semantic characteristics, and appearance. Exemplified by the international standards CityGML and Industry Foundation Classes (IFC), information models from semantic 3D city modeling and BIM and their corresponding modeling approaches are explored, and the relationships between them are discussed. Based on use cases from infrastructure planning, approaches for integrating information from semantic 3D city modeling and BIM, such as semantic transformation between CityGML and IFC, are described. Furthermore, the role of semantic 3D city modeling and BIM for recent developments in urban informatics, such as smart cities and digital twins, is investigated and illustrated by real-world examples.

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Yao, Wei, and Jianwei Wu. "Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics." In Urban Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_22.

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AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.

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Arora, H., C. Langenhan, F. Petzold, V. Eisenstadt, and K. D. Althoff. "METIS-GAN: An approach to generate spatial configurations using deep learning and semantic building models." In ECPPM 2021 – eWork and eBusiness in Architecture, Engineering and Construction. CRC Press, 2021. http://dx.doi.org/10.1201/9781003191476-37.

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Yang, Yanwu. "Semantic User Model Inferences for Travel Recommender Systems." In Tourism Informatics. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-818-5.ch002.

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This chapter proposes a semantic user model based on a description logic language to represent user’s knowledge and information, and a set of domain-dependent rules specific to the tourism domain in terms of spatial criteria (i.e., distance) and cognition to infer useful user features such as interests and preferences as important inputs for travel recommender systems (TRS). We also identify a spatial Web application scenario in the tourism domain, which is intended to provide personalized information about a variety of spatial entities in order to assist the user in traveling in an urban space.

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Meng, Xiangfu, Lulu Zhao, Xiaoyan Zhang, Pan Li, Zeqi Zhao, and Yue Mao. "An Interactive Personalized Spatial Keyword Querying Approach." In Advances in Data Mining and Database Management. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8446-9.ch010.

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Existing spatial keyword query methods usually evaluate text relevancy according to the frequency of occurrence of query keywords in the text information associated to spatial objects, without considering the degree of preference of users to different query keywords, and without considering semantic relevancy. To deal with the above problems, this chapter proposes an interactive personalized spatial keyword querying approach which is divided into two stages. In the offline processing stage, Gibbs algorithm is adopted to estimate the thematic probability distribution of text information associated to spatial objects, and then an LDA model is used for semantic expansion of spatial data set.

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Borrmann, André, and Ernst Rank. "Query Support for BIMs using Semantic and Spatial Conditions." In Handbook of Research on Building Information Modeling and Construction Informatics. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-928-1.ch018.

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A query language for Building Information Models allows users and third-party application programmers to not only analyze the digital building under specific criteria but also to extract partial models from a full building model. This functionality is of crucial importance, since the full BIM is meant to comprise the information of all domains involved in the planning process, but an individual user or programmer is normally interested in only a small subset of it. To specify this subset, a formal language is required which makes it possible to formulate conditions the resulting data set has to fulfill. This concept is also known as providing a certain view of the data available. This chapter gives an overview of the currently available query technologies for BIMs and compares the different options in terms to expressive power and ease of use. The emphasis of the chapter, however, lies in the introduction of spatial query technology for BIMs that has been developed by the authors. Spatial operators extend the analysis and submodel specification capabilities of a query language substantially by providing an intermediate level of abstraction that is close to the human understanding of the geometric-toplological properties of building components and the relationships between them.

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Conference papers on the topic "Spatial-semantic model"

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Wang, Xiaolin, and Yingwei Luo. "Model semantic network for massive spatial information." In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6049835.

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Li, Shiqi, Tiejun Zhao, and Hanjing Li. "Improving Spatial Semantic Analysis by a Combining Model." In 2010 International Conference on E-Business and E-Government (ICEE). IEEE, 2010. http://dx.doi.org/10.1109/icee.2010.363.

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Chen, Ying Dong, Rong Guo Chen, Zhen Lin Liu, Zhen Chen, Zhan Wei Lu, and Min Qiang Fan. "Creation of Spatial Information Service Semantic Topology Description Model." In 2009 1st International Conference on Information Science and Engineering (ICISE 2009). IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.439.

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Memar, Sara, Mohammadreza Ektefa, and Lilly Suriani Affendey. "Developing context model supporting spatial relations for semantic video retrieval." In Knowledge Management (CAMP). IEEE, 2010. http://dx.doi.org/10.1109/infrkm.2010.5466951.

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Yu, Feiyang, and Horace S Ip. "Automatic Semantic Annotation of Images using Spatial Hidden Markov Model." In 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006. http://dx.doi.org/10.1109/icme.2006.262459.

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Yan, Y., J. Li, and Z. He. "Research on Ontology Based Semantic Integration Model in Spatial Data Sharing." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.738.

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Wang, Xingang, Kuo Guo, and Zhigang Gai. "A Semi-Formal Multi-Policy Secure Model for Semantic Spatial Trajectories." In the 2017 International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3058060.3058063.

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Zhan, Yunjun, Yanbin Yuan, Yanyan Wu, and Peipei Qi. "Experiment about Image Emporium Buildings Intelligent Recognition Using Spatial Semantic Model." In 2009 International Conference on Computational Intelligence and Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cise.2009.5362834.

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Xia, Huiqiong, Deren Li, and Zhenfeng Shao. "A spatio-temporal semantic model based on spatial information multi-grid." In Geoinformatics 2006: Geospatial Information Science, edited by Jianya Gong and Jingxiong Zhang. SPIE, 2006. http://dx.doi.org/10.1117/12.712624.

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Rishe, Naphtali, and Qiang Li. "Storage of Spatial Data in Semantic Databases." In ASME 1994 International Computers in Engineering Conference and Exhibition and the ASME 1994 8th Annual Database Symposium collocated with the ASME 1994 Design Technical Conferences. American Society of Mechanical Engineers, 1994. http://dx.doi.org/10.1115/cie1994-0473.

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Abstract This paper describes how the semantic binary database model [Rishe-92-DDS] and its implementation [Rishe-91-FS] are extended to represent spatial data: functions over multi-dimensional space, e.g. the ocean temperature as a function of the four-dimensional space-time. The implementation, utilizing extended linear quad-trees, is compact in storage and allows efficient resolution of queries.

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Reports on the topic "Spatial-semantic model"

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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose to use residual connections to improve each UNet block and residual paths to solve the semantic gap between UNet encoder and decoder which improves the performance of the model. Adding spatial information as an extra channel helped increase the RMSE performance of the first break predictions. Other techniques like data augmentation, multitask loss, and normalization methods, were further explored to evaluate model improvement.

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