Готові списки джерел за темами / Spatial information extraction

Добірка наукової літератури з теми "Spatial information extraction"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 29 січня 2023

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Статті в журналах з теми "Spatial information extraction"

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Zenasni, Sarah, Eric Kergosien, Mathieu Roche, and Maguelonne Teisseire. "Spatial Information Extraction from Short Messages." Expert Systems with Applications 95 (April 2018): 351–67. http://dx.doi.org/10.1016/j.eswa.2017.11.025.

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2

Hickman, Betty L., Michael P. Bishop, and Michael V. Rescigno. "Advanced computational methods for spatial information extraction." Computers & Geosciences 21, no. 1 (February 1995): 153–73. http://dx.doi.org/10.1016/0098-3004(94)00063-z.

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Shen, Xiangfei, and Wenxing Bao. "Hyperspectral Endmember Extraction Using Spatially Weighted Simplex Strategy." Remote Sensing 11, no. 18 (September 15, 2019): 2147. http://dx.doi.org/10.3390/rs11182147.

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Spatial information is increasingly becoming a vital factor in the field of hyperspectral endmember extraction, since it takes into consideration the spatial correlation of pixels, which generally involves jointing spectral information for preprocessing and/or endmember extraction in hyperspectral imagery (HSI). Generally, simplex-based endmember extraction algorithms (EEAs) identify endmembers without considering spatial attributes, and the spatial preprocessing strategy is an independently executed module that can provide spatial information for the endmember search process. Despite this interest, to the best of our knowledge, no one has studied the integration framework of the spatial information-embedded simplex for hyperspectral endmember extraction. In this paper, we propose a spatially weighted simplex strategy, called SWSS, for hyperspectral endmember extraction that investigates a novel integration framework of the spatial information-embedded simplex for identifying endmember. Specifically, the SWSS generates the spatial weight scalar of each pixel by determining its corresponding spatial neighborhood correlations for weighting itself within the simplex framework to regularize the selection of the endmembers. The SWSS could be implemented in the traditional simplex-based EEAs, such as vertex component analysis (VCA), to introduce spatial information into the data simplex framework without the computational complexity excessively increasing or endmember extraction accuracy loss. Based on spectral angle distance (SAD) and root-mean-square-error (RMSE) evaluation criteria, experimental results on both synthetic and C u p r i t e real hyperspectral datasets indicate that the simplex-based EEA re-implemented by the SWSS has a significant improvement on endmember extraction performance over the techniques on their own and without re-implementing.
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4

Chaitanya, Aravapalli Sri, Suvarna Vani Koneru, and Praveen Kumar Kollu. "Road Network Extraction using Atrous Spatial Pyramid Pooling." International Journal of Innovative Technology and Exploring Engineering 8, no. 9 (July 30, 2019): 31–33. http://dx.doi.org/10.35940/ijitee.h7459.078919.

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Road extraction from satellite images has several Applications such as geographic information system (GIS). Having an accurate and up-to-date road network database will facilitate transportation, disaster management and GPS navigation. Most active field of research for automatic extraction of road network involves semantic segmentation using convolutional neural network (CNN). Although they can produce accurate results, typically the models give up performance for accuracy and vice-versa. In this paper, we are proposing architecture for semantic segmentation of road networks using Atrous Spatial Pyramid Pooling (ASPP). The network contains residual blocks for extracting low level features. Atrous convolutions with different dilation rates are taken and spatial pyramid pooling is performed on these features for extracting the spatial information. The low level features from residual blocks are added to the multi scale context information to produce the final segmentation image. Our proposed model significantly reduces the number of parameters that are required to train the model. The proposed model was trained on the Massachusetts roads dataset and the results have shown that our model produces superior results than that of popular state-of-the art models.
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Tao, Chao, Ji Qi, Yansheng Li, Hao Wang, and Haifeng Li. "Spatial information inference net: Road extraction using road-specific contextual information." ISPRS Journal of Photogrammetry and Remote Sensing 158 (December 2019): 155–66. http://dx.doi.org/10.1016/j.isprsjprs.2019.10.001.

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Abdul-samad, Sarmad. "COLOR FEATURE WITH SPATIAL INFORMATION EXTRACTION METHODS FOR CBIR: A REVIEW." Iraqi Journal for Computers and Informatics 45, no. 1 (May 1, 2019): 15–19. http://dx.doi.org/10.25195/ijci.v45i1.45.

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Inn then last two decades the Content Based Image Retrieval (CBIR) considered as one of the topic of interest for theresearchers. It depending one analysis of the image’s visual content which can be done by extracting the color, texture and shapefeatures. Therefore, feature extraction is one of the important steps in CBIR system for representing the image completely. Color featureis the most widely used and more reliable feature among the image visual features. This paper reviews different methods, namely LocalColor Histogram, Color Correlogram, Row sum and Column sum and Colors Coherences Vectors were used to extract colors featurestaking in consideration the spatial information of the image.
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Syed, Mehtab Alam, Elena Arsevska, Mathieu Roche, and Maguelonne Teisseire. "GeoXTag: Relative Spatial Information Extraction and Tagging of Unstructured Text." AGILE: GIScience Series 3 (June 10, 2022): 1–10. http://dx.doi.org/10.5194/agile-giss-3-16-2022.

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Abstract. Spatial information has gained more attention in natural language processing tasks in different interdisciplinary domains. Moreover, the spatial information is available in two forms: Absolute Spatial Information (ASI) e.g., Paris, London, and Germany and Relative Spatial Information (RSI) e.g., south of Paris, north Madrid and 80 km from Rome. Therefore, it is challenging to extract RSI from textual data and compute its geotagging. This paper presents two strategies and the associated prototypes to address the following tasks: 1) extraction of relative spatial information from textual data and 2) geotagging of this relative spatial information. Experiments show promising results for RSI extraction and tagging.
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Tan, Kok Kiong, Arun Shankar Narayanan, Choon Huat Koh, Kevin Caves, and Helen Hoenig. "Extraction of spatial information for low-bandwidth telerehabilitation applications." Journal of Rehabilitation Research and Development 51, no. 5 (2014): 825–40. http://dx.doi.org/10.1682/jrrd.2013.09.0217.

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Xu, Mingming, Bo Du, and Liangpei Zhang. "Spatial-Spectral Information Based Abundance-Constrained Endmember Extraction Methods." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 6 (June 2014): 2004–15. http://dx.doi.org/10.1109/jstars.2013.2268661.

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Grosse-Wentrup, Moritz, and Martin Buss. "Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction." IEEE Transactions on Biomedical Engineering 55, no. 8 (August 2008): 1991–2000. http://dx.doi.org/10.1109/tbme.2008.921154.

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Дисертації з теми "Spatial information extraction"

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Chen, Pu-Huai. "Extraction of spatial information from stereoscopic SAR images." Thesis, University College London (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395765.

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Vempala, Alakananda. "Extracting Temporally-Anchored Spatial Knowledge." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1505146/.

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In my dissertation, I elaborate on the work that I have done to extract temporally-anchored spatial knowledge from text, including both intra- and inter-sentential knowledge. I also detail multiple approaches to infer spatial timeline of a person from biographies and social media. I present and analyze two strategies to annotate information regarding whether a given entity is or is not located at some location, and for how long with respect to an event. Specifically, I leverage semantic roles or syntactic dependencies to generate potential spatial knowledge and then crowdsource annotations to validate the potential knowledge. The resulting annotations indicate how long entities are or are not located somewhere, and temporally anchor this spatial information. I present an in-depth corpus analysis and experiments comparing the spatial knowledge generated by manipulating roles or dependencies. In my work, I also explore research methodologies that go beyond single sentences and extract spatio-temporal information from text. Spatial timelines refer to a chronological order of locations where a target person is or is not located. I present corpus and experiments to extract spatial timelines from Wikipedia biographies. I present my work on determining locations and the order in which they are actually visited by a person from their travel experiences. Specifically, I extract spatio-temporal graphs that capture the order (edges) of locations (nodes) visited by a person. Further, I detail my experiments that leverage both text and images to extract spatial timeline of a person from Twitter.
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Mackay, Jane Louise. "The extraction of urban land cover information from fine spatial scale earth observation data." Thesis, University of Leeds, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410960.

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Devine, Jon. "Support Vector Methods for Higher-Level Event Extraction in Point Data." Fogler Library, University of Maine, 2009. http://www.library.umaine.edu/theses/pdf/DevineJ2009.pdf.

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Dittrich, André [Verfasser], and S. [Akademischer Betreuer] Hinz. "Real-Time Event Analysis and Spatial Information Extraction From Text Using Social Media Data / André Dittrich. Betreuer: S. Hinz." Karlsruhe : KIT-Bibliothek, 2016. http://d-nb.info/1108453295/34.

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Basnet, Shiva. "Spatial Analysis of Rock Textures." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1349988757.

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Zenasni, Sarah. "Extraction d'information spatiale à partir de données textuelles non-standards." Thesis, Montpellier, 2018. http://www.theses.fr/2018MONTS076/document.

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L’extraction d’information spatiale à partir de données textuelles est désormais un sujet de recherche important dans le domaine du Traitement Automatique du Langage Naturel (TALN). Elle répond à un besoin devenu incontournable dans la société de l’information, en particulier pour améliorer l’efficacité des systèmes de Recherche d’Information (RI) pour différentes applications (tourisme, aménagement du territoire, analyse d’opinion, etc.). De tels systèmes demandent une analyse fine des informations spatiales contenues dans les données textuelles disponibles (pages web, courriels, tweets, SMS, etc.). Cependant, la multitude et la variété de ces données ainsi que l’émergence régulière de nouvelles formes d’écriture rendent difficile l’extraction automatique d’information à partir de corpus souvent peu standards d’un point de vue lexical voire syntaxique.Afin de relever ces défis, nous proposons, dans cette thèse, des approches originales de fouille de textes permettant l’identification automatique de nouvelles variantes d’entités et relations spatiales à partir de données textuelles issues de la communication médiée. Ces approches sont fondées sur trois principales contributions qui sont cruciales pour fournir des méthodes de navigation intelligente. Notre première contribution se concentre sur la problématique de reconnaissance et d’extraction des entités spatiales à partir de corpus de messages courts (SMS, tweets) marqués par une écriture peu standard. La deuxième contribution est dédiée à l’identification de nouvelles formes/variantes de relations spatiales à partir de ces corpus spécifiques. Enfin, la troisième contribution concerne l’identification des relations sémantiques associées à l’information spatiale contenue dans les textes. Les évaluations menées sur des corpus réels, principalement en français (SMS, tweets, presse), soulignent l’intérêt de ces contributions. Ces dernières permettent d’enrichir la typologie des relations spatiales définies dans la communauté scientifique et, plus largement, de décrire finement l’information spatiale véhiculée dans les données textuelles non standards issues d’une communication médiée aujourd’hui foisonnante
The extraction of spatial information from textual data has become an important research topic in the field of Natural Language Processing (NLP). It meets a crucial need in the information society, in particular, to improve the efficiency of Information Retrieval (IR) systems for different applications (tourism, spatial planning, opinion analysis, etc.). Such systems require a detailed analysis of the spatial information contained in the available textual data (web pages, e-mails, tweets, SMS, etc.). However, the multitude and the variety of these data, as well as the regular emergence of new forms of writing, make difficult the automatic extraction of information from such corpora.To meet these challenges, we propose, in this thesis, new text mining approaches allowing the automatic identification of variants of spatial entities and relations from textual data of the mediated communication. These approaches are based on three main contributions that provide intelligent navigation methods. Our first contribution focuses on the problem of recognition and identification of spatial entities from short messages corpora (SMS, tweets) characterized by weakly standardized modes of writing. The second contribution is dedicated to the identification of new forms/variants of spatial relations from these specific corpora. Finally, the third contribution concerns the identification of the semantic relations associated withthe textual spatial information
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Wallace, Cynthia S. A. "Extracting temporal and spatial information from remotely sensed data for mapping wildlife habitat." Diss., The University of Arizona, 2002. http://hdl.handle.net/10150/280220.

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The research accomplished in this dissertation used both mathematical and statistical techniques to extract and evaluate measures of landscape temporal dynamics and spatial structure from remotely sensed data for the purpose of mapping wildlife habitat. By coupling the landscape measures gleaned from the remotely sensed data with various sets of animal sightings and population data, effective models of habitat preference were created. Measures of temporal dynamics of vegetation greenness as measured by National Oceanographic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite were used to effectively characterize and map season specific habitat of the Sonoran pronghorn antelope, as well as produce preliminary models of potential yellow-billed cuckoo habitat in Arizona. Various measures that capture different aspects of the temporal dynamics of the landscape were derived from AVHRR Normalized Difference Vegetation Index composite data using three main classes of calculations: basic statistics, standardized principal components analysis, and Fourier analysis. Pronghorn habitat models based on the AVHRR measures correspond visually and statistically to GIS-based models produced using data that represent detailed knowledge of ground-condition. Measures of temporal dynamics also revealed statistically significant correlations with annual estimates of elk population in selected Arizona Game Management Units, suggesting elk respond to regional environmental changes that can be measured using satellite data. Such relationships, once verified and established, can be used to help indirectly monitor the population. Measures of landscape spatial structure derived from IKONOS high spatial resolution (1-m) satellite data using geostatistics effectively map details of Sonoran pronghorn antelope habitat. Local estimates of the nugget, sill, and range variogram parameters calculated within 25 x 25-meter image windows describe the spatial autocorrelation of the image, permitting classification of all pixels into coherent units whose signature graphs exhibit a classic variogram shape. The variogram parameters captured in these signatures have been shown in previous studies to discriminate between different species-specific vegetation associations. The synoptic view of the landscape provided by satellite data can inform resource management efforts. The ability to characterize the spatial structure and temporal dynamics of habitat using repeatable remote sensing data allows closer monitoring of the relationship between a species and its landscape.
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Alatrista-Salas, Hugo. "Extraction de relations spatio-temporelles à partir des données environnementales et de la santé." Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2013. http://tel.archives-ouvertes.fr/tel-00997539.

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Face à l'explosion des nouvelles technologies (mobiles, capteurs, etc.), de grandes quantités de données localisées dans l'espace et dans le temps sont désormais disponibles. Les bases de données associées peuvent être qualifiées de bases de données spatio-temporelles car chaque donnée est décrite par une information spatiale (e.g. une ville, un quartier, une rivière, etc.) et temporelle (p. ex. la date d'un événement). Cette masse de données souvent hétérogènes et complexes génère ainsi de nouveaux besoins auxquels les méthodes d'extraction de connaissances doivent pouvoir répondre (e.g. suivre des phénomènes dans le temps et l'espace). De nombreux phénomènes avec des dynamiques complexes sont ainsi associés à des données spatio-temporelles. Par exemple, la dynamique d'une maladie infectieuse peut être décrite par les interactions entre les humains et le vecteur de transmission associé ainsi que par certains mécanismes spatio-temporels qui participent à son évolution. La modification de l'un des composants de ce système peut déclencher des variations dans les interactions entre les composants et finalement, faire évoluer le comportement global du système.Pour faire face à ces nouveaux enjeux, de nouveaux processus et méthodes doivent être développés afin d'exploiter au mieux l'ensemble des données disponibles. Tel est l'objectif de la fouille de données spatio-temporelles qui correspond à l'ensemble de techniques et méthodes qui permettent d'obtenir des connaissances utiles à partir de gros volumes de données spatio-temporelles. Cette thèse s'inscrit dans le cadre général de la fouille de données spatio-temporelles et l'extraction de motifs séquentiels. Plus précisément, deux méthodes génériques d'extraction de motifs sont proposées. La première permet d'extraire des motifs séquentiels incluant des caractéristiques spatiales. Dans la deuxième, nous proposons un nouveau type de motifs appelé "motifs spatio-séquentiels". Ce type de motifs permet d'étudier l'évolution d'un ensemble d'événements décrivant une zone et son entourage proche. Ces deux approches ont été testées sur deux jeux de données associées à des phénomènes spatio-temporels : la pollution des rivières en France et le suivi épidémiologique de la dengue en Nouvelle Calédonie. Par ailleurs, deux mesures de qualité ainsi qu'un prototype de visualisation de motifs sont été également proposés pour accompagner les experts dans la sélection des motifs d'intérêts.
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Budig, Benedikt [Verfasser], Dijk Thomas C. [Gutachter] van, Alexander [Gutachter] Wolff, and Yao-Yi [Gutachter] Chiang. "Extracting Spatial Information from Historical Maps: Algorithms and Interaction / Benedikt Budig ; Gutachter: Thomas C. van Dijk, Alexander Wolff, Yao-Yi Chiang." Würzburg : Würzburg University Press, 2018. http://d-nb.info/1174143495/34.

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Книги з теми "Spatial information extraction"

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Gougeon, François A. Forest information extraction from high spatial resolution images using an individual tree crown approach. Victoria: Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, 2003.

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ISPRS Commission II/VII International Workshop (1990 University of Maine, Orono). Advances in spatial information extraction and analysis for remote sensing: Proceedings, 13-17 January 1990, University of Maine, Orono, Maine. Bethesda, Md: The Society, 1990.

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Budig, Benedikt. Extracting Spatial Information from Historical Maps. Wurzburg University Press, 2018.

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4

Schelbert, Heinrich R. Image-Based Measurements of Myocardial Blood Flow. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0024.

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Image-based measurements of myocardial blood flow afford the assessment of coronary circulatory function. They reflect functional consequences of coronary stenoses, diffuse epicardial vessel disease and microvascular dysfunction and structural changes and thus provide a measure of the total ischemic burden. Measured flows contain therefore clinically important predictive information. Fundamental to flow measurements are the tissue tracer kinetics, their description through tracer kinetic models, high spatial and temporal resolution imaging devices and accurate extraction of radiotracer tissue concentrations from dynamically acquired images for estimating true flows from the tissue time activity curves. A large body of literature on measurements of myocardial blood flow exists for defining in humans normal values for flow at baseline and during hyperemic stress as well as for the myocardial flow reserve. The role of PET for flow measurements has been well established; initial results with modern SPECT devices are encouraging. Responses of myocardial blood flow to specific challenges like pharmacologic vasodilation and to sympathetic stimulation can uncover functional consequences of focal epicardial coronary stenoses, of conduit vessel disturbances and disease and impairments of microvascular function. Apart from risk stratification, flow measurements may allow detection of early preclinical disease, influence treatment strategies and identify therapy responses.
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Частини книг з теми "Spatial information extraction"

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Tezuka, Taro, and Katsumi Tanaka. "Landmark Extraction: A Web Mining Approach." In Spatial Information Theory, 379–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11556114_24.

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Zheng, Suiwu, Linshan Liu, and Hong Qiao. "Spatial-Temporal Saliency Feature Extraction for Robust Mean-Shift Tracker." In Neural Information Processing, 191–98. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12637-1_24.

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Park, Kyung-Je, Min-Soo Moon, and Ki-Jung Lee. "The Extraction of Spatial Information and Object Location Information from Video." In Lecture Notes in Electrical Engineering, 395–402. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41671-2_50.

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Fujita, Kazuhisa. "Spatial Feature Extraction by Spike Timing Dependent Synaptic Modification." In Neural Information Processing. Theory and Algorithms, 148–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17537-4_19.

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Wang, Shuang, Yecheng Yuan, Tao Pei, and Yufen Chen. "A Framework for Event Information Extraction from Chinese News Online." In Spatial Data Handling in Big Data Era, 53–73. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4424-3_5.

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Lu, Kui, Min Zhou, and Shunxiang Zhang. "A Hyperspectral Image Feature Extraction Algorithm Combining Spatial-Spectral Information." In Advances in Intelligent Systems and Computing, 1356–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25128-4_167.

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Chenthamarakshan, Vijil, Ramakrishna Varadarajan, Prasad M. Deshpande, Raghuram Krishnapuram, and Knut Stolze. "WYSIWYE: An Algebra for Expressing Spatial and Textual Rules for Information Extraction." In Web-Age Information Management, 419–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32281-5_41.

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Qi, Feifei, Yuanqing Li, Zhenfu Wen, and Wei Wu. "An Algorithm Combining Spatial Filtering and Temporal Down-Sampling with Applications to ERP Feature Extraction." In Neural Information Processing, 739–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70096-0_75.

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Baran, Remigiusz, Andrzej Dziech, and Jakob Wassermann. "Contour Extraction and Compression Scheme Utilizing Both the Transform and Spatial Image Domains." In Communications in Computer and Information Science, 1–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69911-0_1.

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Tezuka, Taro, and Katsumi Tanaka. "Temporal and Spatial Attribute Extraction from Web Documents and Time-Specific Regional Web Search System." In Web and Wireless Geographical Information Systems, 14–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427865_2.

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Тези доповідей конференцій з теми "Spatial information extraction"

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Shin, Hyeong Jin, Jeong Yeon Park, Dae Bum Yuk, and Jae Sung Lee. "BERT-based Spatial Information Extraction." In Proceedings of the Third International Workshop on Spatial Language Understanding. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.splu-1.2.

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Zhu, Yi, Xiaodong Liu, and Lijian Sun. "Extraction and mining for layered natural disaster information based on GIS." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837658.

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Lo, Chun-Chih, Kuo-Hsuan Hsu, Mong-Fong Horng, and Yau-Hwang Kuo. "Spatial Information Extraction using Hidden Correlations." In 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2018. http://dx.doi.org/10.1109/pimrc.2018.8580676.

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Xu, Chao, Emmanuelle-Anna Dietz Saldanha, Dagmar Gromann, and Beihai Zhou. "A Cognitively Motivated Approach to Spatial Information Extraction." In Proceedings of the Third International Workshop on Spatial Language Understanding. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.splu-1.3.

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Badia, Antonio, Jothi Ravishankar, and Tulay Muezzinoglu. "Text Extraction of Spatial and Temporal Information." In 2007 IEEE Intelligence and Security Informatics. IEEE, 2007. http://dx.doi.org/10.1109/isi.2007.379527.

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Zhu, Shiping, and Jie Gao. "Video Object Extraction Integrating Temporal-Spatial Information." In 2nd International Conference on Electronic and Mechanical Engineering and Information Technology. Paris, France: Atlantis Press, 2012. http://dx.doi.org/10.2991/emeit.2012.455.

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Tian, Balin, Jianping Yuan, Xiaokui Yue, and Xin Ning. "Feature extraction algorithm for space targets based on fractal theory." In Second International Conference on Spatial Information Technology, edited by Cheng Wang, Shan Zhong, and Jiaolong Wei. SPIE, 2007. http://dx.doi.org/10.1117/12.773739.

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Yang, Jin, Dong-mei Yan, Chao Wang, and Hong Zhang. "Feature extraction of attributed scattering centers on high resolution SAR imagery." In Second International Conference on Spatial Information Technology, edited by Cheng Wang, Shan Zhong, and Jiaolong Wei. SPIE, 2007. http://dx.doi.org/10.1117/12.773984.

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Li, Deren, and Juliang Shao. "House extraction with multiresolution analysis and information fusion." In Spatial Information from Digital Photogrammetry and Computer Vision: ISPRS Commission III Symposium, edited by Heinrich Ebner, Christian Heipke, and Konrad Eder. SPIE, 1994. http://dx.doi.org/10.1117/12.182891.

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Qi, Ji, Chao Tao, Hao Wang, Yuqi Tang, and Zhenqi Cui. "Spatial Information Inference Net: Road Extraction Using Road-Specific Contextual Information." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8900507.

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Звіти організацій з теми "Spatial information extraction"

1

Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, May 2021. http://dx.doi.org/10.17760/d20410114.

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Анотація:
Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.
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