Literatura académica sobre el tema "Geospatial data fusion"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Geospatial data fusion".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Geospatial data fusion"
Ahn, D. S., J. H. Park y J. Y. Lee. "DEFINING GEOSPATIAL DATA FUSION METHODS BASED ON TOPOLOGICAL RELATIONSHIPS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W9 (30 de octubre de 2018): 317–19. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w9-317-2018.
Texto completoPark, Junho, Dasol Ahn y Jiyeong Lee. "Development of Data Fusion Method Based on Topological Relationships Using IndoorGML Core Module". Journal of Sensors 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/4094235.
Texto completoJia, Wei Jie, Hong Rui Zhang, Jian Lin y Hong Lei Zhao. "The Application of Remote Sensing and Aero-Geophysics Data Fusion on Metallogenic Prognosis in Qimantage of East Kunlun Montain Area". Applied Mechanics and Materials 411-414 (septiembre de 2013): 1588–93. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1588.
Texto completoZhang, Yuhang y Saurabh Prasad. "Multisource Geospatial Data Fusion via Local Joint Sparse Representation". IEEE Transactions on Geoscience and Remote Sensing 54, n.º 6 (junio de 2016): 3265–76. http://dx.doi.org/10.1109/tgrs.2016.2514481.
Texto completoLewicka, Oktawia, Mariusz Specht, Andrzej Stateczny, Cezary Specht, David Brčić, Alen Jugović, Szymon Widźgowski y Marta Wiśniewska. "Analysis of GNSS, Hydroacoustic and Optoelectronic Data Integration Methods Used in Hydrography". Sensors 21, n.º 23 (25 de noviembre de 2021): 7831. http://dx.doi.org/10.3390/s21237831.
Texto completoMa, Wenping, Qiongqiong Guo, Yue Wu, Wei Zhao, Xiangrong Zhang y Licheng Jiao. "A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images". Remote Sensing 11, n.º 7 (27 de marzo de 2019): 737. http://dx.doi.org/10.3390/rs11070737.
Texto completoWang, Haiqi, Liuke Li, Lei Che, Haoran Kong, Qiong Wang, Zhihai Wang y Jianbo Xu. "Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix". ISPRS International Journal of Geo-Information 10, n.º 11 (20 de octubre de 2021): 714. http://dx.doi.org/10.3390/ijgi10110714.
Texto completoPriyashani, Nelunika, Nayomi Kankanamge y Tan Yigitcanlar. "Multisource Open Geospatial Big Data Fusion: Application of the Method to Demarcate Urban Agglomeration Footprints". Land 12, n.º 2 (2 de febrero de 2023): 407. http://dx.doi.org/10.3390/land12020407.
Texto completoCherif, Mohamed Abderrazak, Sebastien Tripodi, Yuliya Tarabalka, Isabelle Manighetti y Lionel Laurore. "Novel Approaches for Aligning Geospatial Vector Maps". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (11 de junio de 2024): 55–64. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-55-2024.
Texto completoHuang, W., J. Jiang, Z. Zha, H. Zhang, C. Wang y J. Zhang. "A Practice Approach of Multi-source Geospatial Data Integration for Web-based Geoinformation Services". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4 (23 de abril de 2014): 97–100. http://dx.doi.org/10.5194/isprsarchives-xl-4-97-2014.
Texto completoTesis sobre el tema "Geospatial data fusion"
Foy, Andrew Scott. "Making Sense Out of Uncertainty in Geospatial Data". Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/39175.
Texto completoPh. D.
Martin-Lac, Victor. "Aerial navigation based on SAR imaging and reference geospatial data". Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0400.
Texto completoWe seek the algorithmic means of determining the kinematic state of an aerial device from an observation SAR image and reference geospatial data that may be SAR, optical or vector. We determine a transform that relates the observation and reference coordinates and whose parameters are the kinematic state. We follow three approaches. The first one is based on detecting and matching structures such as contours. We propose an iterative closest point algorithm and demonstrate how it can serve to estimate the full kinematic state. We then propose a complete pipeline that includes a learned multimodal contour detector. The second approach is based on a multimodal similarity metric, which is the means of measuring the likelihood that two local patches of geospatial data represent the same geographic point. We determine the kinematic state under the hypothesis of which the SAR image is most similar to the reference geospatial data. The third approach is based on scene coordinates regression. We predict the geographic coordinates of random image patches and infer the kinematic state from these predicted correspondences. However, in this approach, we do not address the fact that the modality of the observation and the reference are different
Cherif, Mohamed Abderrazak. "Alignement et fusion de cartes géospatiales multimodales hétérogènes". Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5002.
Texto completoThe surge in data across diverse fields presents an essential need for advanced techniques to merge and interpret this information. With a special emphasis on compiling geospatial data, this integration is crucial for unlocking new insights from geographic data, enhancing our ability to map and analyze trends that span across different locations and environments with more authenticity and reliability. Existing techniques have made progress in addressing data fusion; however, challenges persist in fusing and harmonizing data from different sources, scales, and modalities.This research presents a comprehensive investigation into the challenges and solutions in vector map alignment and fusion, focusing on developing methods that enhance the precision and usability of geospatial data. We explored and developed three distinct methodologies for polygonal vector map alignment: ProximityAlign, which excels in precision within urban layouts but faces computational challenges; the Optical Flow Deep Learning-Based Alignment, noted for its efficiency and adaptability; and the Epipolar Geometry-Based Alignment, effective in data-rich contexts but sensitive to data quality. Additionally, our study delved into linear feature map alignment, emphasizing the importance of precise alignment and feature attribute transfer, pointing towards the development of richer, more informative geospatial databases by adapting the ProximityAlign approach for linear features like fault traces and road networks. The fusion aspect of our research introduced a sophisticated pipeline to merge polygonal geometries relying on space partitioning, non-convex optimization of graph data structure, and geometrical operations to produce a reliable fused map that harmonizes input vector maps, maintaining their geometric and topological integrity.In practice, the developed framework has the potential to improve the quality and usability of integrated geospatial data, benefiting various applications such as urban planning, environmental monitoring, and disaster management. This study not only advances theoretical understanding in the field but also provides a solid foundation for practical applications in managing and interpreting large-scale geospatial datasets
Beaufils, Mickaël. "Fusion de données géoréférencées et développement de services interopérables pour l’estimation des besoins en eau à l’échelle des bassins versants". Thesis, Paris, CNAM, 2012. http://www.theses.fr/2012CNAM0847/document.
Texto completoNowadays, preservation of the environment is a main priority. Understanding of environmental phenomena requires the study and the combination of an increasing number of heterogeneous data. Several international initiatives (INSPIRE, GEOSS) aims to encourage the sharing and exchange of those data.In this thesis, the interest of making scientific models available on the web is discussed. The value of using applications based on geospatial data is demonstrated. Several methods and means that satisfy the requirements of interoperability are also purposed.Our approach is illustrated by the implementation of models for estimating agricultural and domestic water requirements. Those models can be used at different spatial scales and temporal granularities. A prototype based on a complete web service oriented architecture was developed. The tool is based on the OGC standards Web Feature Service (WFS), Sensor Observation Service (SOS) and Web Processing Service (WPS).Finally, taking into account the imperfections of the data is also discussed with the integration of methods for sensitivity analysis and uncertainty propagation
Xu, Shaojuan. "Open geospatial data fusion and its application in sustainable urban development". Doctoral thesis, 2020. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-202007173335.
Texto completoUttam, Kumar *. "Algorithms For Geospatial Analysis Using Multi-Resolution Remote Sensing Data". Thesis, 2012. https://etd.iisc.ac.in/handle/2005/2280.
Texto completoUttam, Kumar *. "Algorithms For Geospatial Analysis Using Multi-Resolution Remote Sensing Data". Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2280.
Texto completoLibros sobre el tema "Geospatial data fusion"
Wang, Jiaqiu. Shi kong xu lie shu ju fen xi he jian mo. 8a ed. Beijing: Ke xue chu ban she, 2012.
Buscar texto completoInternational Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining (2009 Wuhan, China). International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining: 13-14 October 2009, Wuhan, China. Editado por Liu Yaolin 1960-, Tang Xinming, Wuhan da xue. School of Resource and Environmental Science, China Jiao yu bu y SPIE (Society). Bellingham, Wash: SPIE, 2009.
Buscar texto completoCapítulos de libros sobre el tema "Geospatial data fusion"
Ochodnicky, Jan. "Data Filtering and Data Fusion in Remote Sensing Systems". En GeoSpatial Visual Analytics, 155–65. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2899-0_12.
Texto completoStankutė, Silvija y Hartmut Asche. "An Integrative Approach to Geospatial Data Fusion". En Computational Science and Its Applications – ICCSA 2009, 490–504. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02454-2_35.
Texto completoMoshou, Dimitrios, Ioannis Gravalos, Dimitrios Kateris Cedric Bravo, Roberto Oberti, Jon S. West y Herman Ramon. "Multisensor Fusion of Remote Sensing Data for Crop Disease Detection". En Geospatial Techniques for Managing Environmental Resources, 201–19. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1858-6_13.
Texto completoYang, Huadong y Hongping Tuo. "Multi-source Geospatial Vector Data Fusion Technology and Software Design". En Advances in Intelligent Systems and Computing, 489–96. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02116-0_57.
Texto completoKermarrec, Gaël, Vibeke Skytt y Tor Dokken. "LR B-Splines for Representation of Terrain and Seabed: Data Fusion, Outliers, and Voids". En Optimal Surface Fitting of Point Clouds Using Local Refinement, 57–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16954-0_5.
Texto completoSalleh, Siti Aekbal, Zulkiflee Abd. Latif, Faezah Pardi, Emad Mushtaha y Yarina Ahmad. "Conceptualising the Citizen-Driven Urban Forest Framework to Improve Local Climate Condition: Geospatial Data Fusion and Numerical Simulation". En Concepts and Applications of Remote Sensing in Forestry, 337–53. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4200-6_17.
Texto completoGiannopoulos, Giorgos, Dimitrios Skoutas, Thomas Maroulis, Nikos Karagiannakis y Spiros Athanasiou. "FAGI: A Framework for Fusing Geospatial RDF Data". En On the Move to Meaningful Internet Systems: OTM 2014 Conferences, 553–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45563-0_33.
Texto completoGiannopoulos, Giorgos, Nick Vitsas, Nikos Karagiannakis, Dimitrios Skoutas y Spiros Athanasiou. "FAGI-gis: A Tool for Fusing Geospatial RDF Data". En The Semantic Web: ESWC 2015 Satellite Events, 51–57. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25639-9_10.
Texto completoMbuh, Mbongowo Joseph. "Application of Data Fusion for Uncertainty and Sensitivity Analysis of Water Quality in the Shenandoah River". En Geospatial Intelligence, 1383–410. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8054-6.ch061.
Texto completoSharma, Arpita y Samiksha Goel. "Cuckoo Search Based Decision Fusion Techniques for Natural Terrain Understanding". En Geospatial Intelligence, 813–36. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8054-6.ch036.
Texto completoActas de conferencias sobre el tema "Geospatial data fusion"
Meng, Xiaolin, Alan Dodson, Jixian Zhang, Yanhui Cai, Chun Liu y Keith Geary. "Geospatial Data Fusion for Precision Agriculture". En 2011 International Symposium on Image and Data Fusion (ISIDF). IEEE, 2011. http://dx.doi.org/10.1109/isidf.2011.6024218.
Texto completoPercivall, George y Trevor Taylor. "Advances in fusion of big geospatial data". En 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8126975.
Texto completoWest, R. Derek, Brian J. Redman, David A. Yocky, John D. van der Laan y Dylan Z. Anderson. "Robust terrain classification of high spatial resolution remote sensing data employing probabilistic feature fusion and pixelwise voting". En Geospatial Informatics X, editado por Kannappan Palaniappan, Gunasekaran Seetharaman, Peter J. Doucette y Joshua D. Harguess. SPIE, 2020. http://dx.doi.org/10.1117/12.2558196.
Texto completoSzekely, Pedro, Craig A. Knoblock, Shubham Gupta, Mohsen Taheriyan y Bo Wu. "Exploiting semantics of web services for geospatial data fusion". En the 1st ACM SIGSPATIAL International Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2068976.2068981.
Texto completoKovalerchuk, Boris, Leonid Perlovsky y Michael Kovalerchuk. "Modeling spatial uncertainties in geospatial data fusion and mining". En SPIE Defense, Security, and Sensing. SPIE, 2012. http://dx.doi.org/10.1117/12.920878.
Texto completoAn Xiaoya, Sun Qun, Zhu Rui, Yan Wei y Wen Chengjie. "The application of data fusion in updating geospatial database actively". En 2010 2nd International Conference on Advanced Computer Control. IEEE, 2010. http://dx.doi.org/10.1109/icacc.2010.5487256.
Texto completoGiannecchini, Simone, Francesco Spina, Bryce Nordgren y Martin Desruisseaux. "Supporting Interoperable Geospatial Data Fusion by adopting OGC and ISO TC 211 standards". En 2006 9th International Conference on Information Fusion. IEEE, 2006. http://dx.doi.org/10.1109/icif.2006.301751.
Texto completoCai, Bofeng, Rong Yu y Zengxiang Zhang. "Utility of neural net classification for remote sensing data based on an improved image fusion algorithm". En Geoinformatics 2006: GNSS and Integrated Geospatial Applications, editado por Deren Li y Linyuan Xia. SPIE, 2006. http://dx.doi.org/10.1117/12.712584.
Texto completoNgan, Chun-Kit. "Geo-Data Fusion Integrator for Object-Oriented Spatiotemporal OLAP Cubes". En 2014 5th International Conference on Computing for Geospatial Research and Application (COM.Geo). IEEE, 2014. http://dx.doi.org/10.1109/com.geo.2014.5.
Texto completoSacharny, D., T. C. Henderson, R. Simmons, A. Mitiche, T. Welker y X. Fan. "BRECCIA: A novel multi-source fusion framework for dynamic geospatial data analysis". En 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2017. http://dx.doi.org/10.1109/mfi.2017.8170352.
Texto completoInformes sobre el tema "Geospatial data fusion"
Bissett, W. P. y David D. Kohler. High Resolution Multispectral and Hyperspectral Data Fusion for Advanced Geospatial Information Products. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2006. http://dx.doi.org/10.21236/ada630662.
Texto completoBissett, W. P. y David D. Kohler. High Resolution Multispectral and Hyperspectral Data Fusion for Advanced Geospatial Information Products. Fort Belvoir, VA: Defense Technical Information Center, marzo de 2007. http://dx.doi.org/10.21236/ada465229.
Texto completo