Relevant bibliographies by topics / Geospatial data fusion

Academic literature on the topic 'Geospatial data fusion'

Author: Grafiati
Published: 7 July 2024
Last updated: 7 July 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Geospatial data fusion":

1

Ahn, D. S., J. H. Park, and 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 (October 30, 2018): 317–19. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w9-317-2018.

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<p><strong>Abstract.</strong> Currently, geospatial datasets are produced in various models and formats in accordance with the spatial scale of the real world such as ground/ surface/underground or indoor/outdoor. The location-based services application also uses the optimal data model and format for each purpose. Therefore, there are various geospatial dataset for representing features of the same space. Various geospatial data on same object cause problems with the financial problems and the suitability of the data. In the paper, we reviewed how to integrate existing geospatial data to utilize geospatial data constructed in different models and formats. There are four main ways to fuse existing geospatial information. The existing geospatial data fusion methods consist of a method through geometry data conversion, a method through the aspect of visualization, a method based on attribute data, and a method using topological relationships. Based on this review, we defined a geospatial data fusion method on topological relationships, which is a method considering topological relationship between geospatial objects. In this method, the topological relationship of objects uses the basic concept of IndoorGML.</p>
2

Park, Junho, Dasol Ahn, and 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.

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Geospatial datasets are currently constructed, managed, and utilized individually according to the spatial scale of the real world, such as the ground/surface/underground or indoor/outdoor, as well the particular purpose of the geospatial data used for location-based services. In addition, LBS applications use an optimal data model and data format according to their particular purpose, and thus, various datasets exist to represent the same spatial features. Such duplicated geospatial datasets and geographical feature-based GIS data cause serious problems in the financial area, compatibility issues among LBS systems, and data integration problems among the various geospatial datasets generated independently for different systems. We propose a geospatial data fusion model called the topological relation-based data fusion model (TRDFM) using topological relations among spatial objects in order to integrate different geospatial datasets and different data formats. The proposed model is a geospatial data fusion model implemented in a spatial information application and is used to directly provide spatial information-based services without data conversion or exchange of geometric data generated by different data models. The proposed method was developed based on an extension of the AnchorNode concept of IndoorGML. The topological relationships among spatial objects are defined and described based upon the basic concept of IndoorGML. This paper describes the concept of the proposed TRDFM and shows an experimental implementation of the proposed data fusion model using commercial 3D GIS software. Finally, the limitations of this study and areas of future research are summarized.
3

Jia, Wei Jie, Hong Rui Zhang, Jian Lin, and 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 (September 2013): 1588–93. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1588.

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Based on west of Qimantage of East Kunlun mountain area, takes advantage of ASTER data, according to the altered mineral spectral characteristics, remote sensing alteration information is extracted. Incorporation the anomaly extraction results with high-precision aero geophysical data processing results, a multiple resource information fusion model is proposed. The fusion model of two totally different type of data which is a special attention in geospatial academia now, which can improve the accuracy of geospatial data application. our fusion result analysis show that it provides information more accurately and sufficiently than separate geospatial data application. The fusion can provide decision-making support for mineral resources prediction.
4

Zhang, Yuhang, and Saurabh Prasad. "Multisource Geospatial Data Fusion via Local Joint Sparse Representation." IEEE Transactions on Geoscience and Remote Sensing 54, no. 6 (June 2016): 3265–76. http://dx.doi.org/10.1109/tgrs.2016.2514481.

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Lewicka, Oktawia, Mariusz Specht, Andrzej Stateczny, Cezary Specht, David Brčić, Alen Jugović, Szymon Widźgowski, and Marta Wiśniewska. "Analysis of GNSS, Hydroacoustic and Optoelectronic Data Integration Methods Used in Hydrography." Sensors 21, no. 23 (November 25, 2021): 7831. http://dx.doi.org/10.3390/s21237831.

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The integration of geospatial data in hydrography, performed using different measurement systems, involves combining several study results to provide a comprehensive analysis. Each of the hydroacoustic and optoelectronic systems is characterised by a different spatial reference system and the method for technical implementation of the measurement. Therefore, the integration of hydrographic data requires that problems in selected fields of electronics, geodesy and physics (acoustics and optics) be solved. The aim of this review is to present selected fusion methods applying the data derived from Global Navigation Satellite System (GNSS), Real Time Kinematic (RTK) measurements, hydrographic surveys, a photogrammetric pass using unmanned vehicles and Terrestrial Laser Scanning (TLS) and compare their accuracy. An additional goal is the evalution of data integration methods according to the International Hydrographic Organization (IHO) S-44 standard. The publication is supplemented by implementation examples of the integration of geospatial data in the Geographic Information System (GIS). The methods described indicate the lack of a uniform methodology for data fusion due to differences in both the spatial reference systems and the techniques used. However, the integration of hydroacoustic and optoelectronic data allows for high accuracy geospatial data to be obtained. This is confirmed by the methods cited, in which the accuracy of integrated geospatial data was in the order of several centimetres.
6

Ma, Wenping, Qiongqiong Guo, Yue Wu, Wei Zhao, Xiangrong Zhang, and Licheng Jiao. "A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images." Remote Sensing 11, no. 7 (March 27, 2019): 737. http://dx.doi.org/10.3390/rs11070737.

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Object detection in optical remote sensing images is still a challenging task because of the complexity of the images. The diversity and complexity of geospatial object appearance and the insufficient understanding of geospatial object spatial structure information are still the existing problems. In this paper, we propose a novel multi-model decision fusion framework which takes contextual information and multi-region features into account for addressing those problems. First, a contextual information fusion sub-network is designed to fuse both local contextual features and object-object relationship contextual features so as to deal with the problem of the diversity and complexity of geospatial object appearance. Second, a part-based multi-region fusion sub-network is constructed to merge multiple parts of an object for obtaining more spatial structure information about the object, which helps to handle the problem of the insufficient understanding of geospatial object spatial structure information. Finally, a decision fusion is made on all sub-networks to improve the stability and robustness of the model and achieve better detection performance. The experimental results on a publicly available ten class data set show that the proposed method is effective for geospatial object detection.
7

Wang, Haiqi, Liuke Li, Lei Che, Haoran Kong, Qiong Wang, Zhihai Wang, and Jianbo Xu. "Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix." ISPRS International Journal of Geo-Information 10, no. 11 (October 20, 2021): 714. http://dx.doi.org/10.3390/ijgi10110714.

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Due to the increasingly complex objects and massive information involved in spatial statistics analysis, least squares support vector regression (LS-SVR) with a good stability and high calculation speed is widely applied in regression problems of geospatial objects. According to Tobler’s First Law of Geography, near things are more related than distant things. However, very few studies have focused on the spatial dependence between geospatial objects via SVR. To comprehensively consider the spatial and attribute characteristics of geospatial objects, a geospatial LS-SVR model for geospatial data regression prediction is proposed in this paper. The 0–1 type and numeric-type spatial weight matrices are introduced as dependence measures between geospatial objects and fused into a single regression function of the LS-SVR model. Comparisons of the results obtained with the proposed and conventional models and other traditional models indicate that fusion of the spatial weight matrix can improve the prediction accuracy. The proposed model is more suitable for geospatial data regression prediction and enhances the ability of geospatial phenomena to explain geospatial data.
8

Priyashani, Nelunika, Nayomi Kankanamge, and Tan Yigitcanlar. "Multisource Open Geospatial Big Data Fusion: Application of the Method to Demarcate Urban Agglomeration Footprints." Land 12, no. 2 (February 2, 2023): 407. http://dx.doi.org/10.3390/land12020407.

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Urban agglomeration is a continuous urban spread and generally comprises a main city at the core and its adjoining growth areas. These agglomerations are studied using different concepts, theories, models, criteria, indices, and approaches, where population distribution and its associated characteristics are mainly used as the main parameters. Given the difficulties in accurately demarcating these agglomerations, novel methods and approaches have emerged in recent years. The use of geospatial big data sources to demarcate urban agglomeration is one of them. This promising method, however, has not yet been studied widely and hence remains an understudied area of research. This study explores using a multisource open geospatial big data fusion approach to demarcate urban agglomeration footprint. The paper uses the Southern Coastal Belt of Sri Lanka as the testbed to demonstrate the capabilities of this novel approach. The methodological approach considers both the urban form and functions related to the parameters of cities in defining urban agglomeration footprint. It employs near-real-time data in defining the urban function-related parameters. The results disclosed that employing urban form and function-related parameters delivers more accurate demarcation outcomes than single parameter use. Hence, the utilization of a multisource geospatial big data fusion approach for the demarcation of urban agglomeration footprint informs urban authorities in developing appropriate policies for managing urban growth.
9

Cherif, Mohamed Abderrazak, Sebastien Tripodi, Yuliya Tarabalka, Isabelle Manighetti, and Lionel Laurore. "Novel Approaches for Aligning Geospatial Vector Maps." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 55–64. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-55-2024.

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Abstract. The 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, 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. In practice, the proposed approaches serve as tools to benefit from as much as possible from existing datasets while respecting a spatial reference source. It also serves as a paramount step for the data fusion task to reduce its complexity.
10

Huang, W., J. Jiang, Z. Zha, H. Zhang, C. Wang, and 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 (April 23, 2014): 97–100. http://dx.doi.org/10.5194/isprsarchives-xl-4-97-2014.

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Geospatial data resources are the foundation of the construction of geo portal which is designed to provide online geoinformation services for the government, enterprise and public. It is vital to keep geospatial data fresh, accurate and comprehensive in order to satisfy the requirements of application and development of geographic location, route navigation, geo search and so on. One of the major problems we are facing is data acquisition. For us, integrating multi-sources geospatial data is the mainly means of data acquisition. <br><br> This paper introduced a practice integration approach of multi-source geospatial data with different data model, structure and format, which provided the construction of National Geospatial Information Service Platform of China (NGISP) with effective technical supports. NGISP is the China's official geo portal which provides online geoinformation services based on internet, e-government network and classified network. Within the NGISP architecture, there are three kinds of nodes: national, provincial and municipal. Therefore, the geospatial data is from these nodes and the different datasets are heterogeneous. According to the results of analysis of the heterogeneous datasets, the first thing we do is to define the basic principles of data fusion, including following aspects: 1. location precision; 2.geometric representation; 3. up-to-date state; 4. attribute values; and 5. spatial relationship. Then the technical procedure is researched and the method that used to process different categories of features such as road, railway, boundary, river, settlement and building is proposed based on the principles. A case study in Jiangsu province demonstrated the applicability of the principle, procedure and method of multi-source geospatial data integration.
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Journal articles

Dissertations / Theses on the topic "Geospatial data fusion":

1

Foy, Andrew Scott. "Making Sense Out of Uncertainty in Geospatial Data." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/39175.

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Uncertainty in geospatial data fusion is a major concern for scientists because society is increasing its use of geospatial technology and generalization is inherent to geographic representations. Limited research exists on the quality of results that come from the fusion of geographic data, yet there is extensive literature on uncertainty in cartography, GIS, and geospatial data. The uncertainties exist and are difficult to understand because data are overlaid which have different scopes, times, classes, accuracies, and precisions. There is a need for a set of tools that can manage uncertainty and incorporate it into the overlay process. This research explores uncertainty in spatial data, GIS and GIScience via three papers. The first paper introduces a framework for classifying and modeling error-bands in a GIS. Paper two tests GIS usersâ ability to estimate spatial confidence intervals and the third paper looks at the practical application of a set of tools for incorporating uncertainty into overlays. The results from this research indicate that it is hard for people to agree on an error-band classification based on their interpretation of metadata. However, people are good estimators of data quality and uncertainty if they follow a systematic approach and use their average estimate to define spatial confidence intervals. The framework and the toolset presented in this dissertation have the potential to alter how people interpret and use geospatial data. The hope is that the results from this paper prompt inquiry and question the reliability of all simple overlays. Many situations exist in which this research has relevance, making the framework, the tools, and the methods important to a wide variety of disciplines that use spatial analysis and GIS.
Ph. D.
2

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.

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Nous cherchons les moyens algorithmiques de déterminer l’état cinématique d’un appareil aérien à partir d’une image RSO observée et de données géospatiales de référence qui peuvent être RSO, optiques ou vectorielles. Nous déterminons la transformation qui associe les coordonnées de l’observation et les coordonnées de la référence et dont les paramètres sont l’état cinématique. Nous poursuivons trois approches. La première repose sur la détection et l’appariement de structures telles que des contours. Nous proposons un algorithme de type Iterative Closest Point (ICP) et démontrons comment il peut servir à estimer l’état cinématique complet. Nous proposons ensuite un système complet qui inclue un détecteur de contours multimodal appris. La seconde approche repose sur une métrique de similarité multimodale, ce qui est un moyen de mesurer la vraisemblance que deux restrictions locales de données géospatiales représentent le même point géographique. Nous déterminons l’état cinématique sous l’hypothèse duquel l’image SAR est la plus similaire aux données géospatiales de référence. La troisième approche repose sur la régression de coordonnées de scène. Nous prédisons les coordonnées géographiques de morceaux d’images et déduisons l’état cinématique à partir des correspondances ainsi prédites. Cependant, dans cette approche, nous ne satisfaisons pas l’hypothèse de multimodalité
We 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
3

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.

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L'augmentation des données dans divers domaines présente un besoin essentiel de techniques avancées pour fusionner et interpréter ces informations. Avec une emphase particulière sur la compilation de données géospatiales, cette intégration est cruciale pour débloquer de nouvelles perspectives à partir des données géographiques, améliorant notre capacité à cartographier et analyser les tendances qui s'étendent à travers différents lieux et environnements avec plus d'authenticité et de fiabilité. Les techniques existantes ont progressé dans l'adresse de la fusion des données ; cependant, des défis persistent dans la fusion et l'harmonisation des données de différentes sources, échelles et modalités. Cette recherche présente une enquête complète sur les défis et les solutions dans l'alignement et la fusion des cartes vectorielles, se concentrant sur le développement de méthodes qui améliorent la précision et l'utilisabilité des données géospatiales. Nous avons exploré et développé trois méthodologies distinctes pour l'alignement des cartes vectorielles polygonales : ProximityAlign, qui excelle en précision dans les agencements urbains; l'Alignement Basé sur l'Apprentissage Profond du Flux Optique, remarquable pour son efficacité ; et l'Alignement Basé sur la Géométrie Épipolaire, efficace dans les contextes riches en données. De plus, notre étude s'est penchée sur l'alignement des cartes de géometries linéaires, soulignant l'importance d'un alignement précis et du transfert d'attributs des éléments, pointant vers le développement de bases de données géospatiales plus riches et plus informatives en adaptant l'approche ProximityAlign pour des géometries linéaires telles que les traces de failles et les réseaux routiers. L'aspect fusion de notre recherche a introduit un pipeline sophistiqué pour fusionner des géométries polygonales en se basant sur le partitionnement d'espace, l'optimisation non convexe de la structure de données de graphes et les opérations géométriques pour produire une carte fusionnée fiable qui harmonise les cartes vectorielles en entrée, en maintenant leur intégrité géométrique et topologique. En pratique, le cadre développé a le potentiel d'améliorer la qualité et l'utilisabilité des données géospatiales intégrées, bénéficiant à diverses applications telles que la planification urbaine, la surveillance environnementale et la gestion des catastrophes. Cette étude avance non seulement la compréhension théorique dans le domaine mais fournit également une base solide pour des applications pratiques dans la gestion et l'interprétation de grands ensembles de données géospatiales
The 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
4

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.

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De nos jours, la préservation de l’environnement constitue un enjeu prioritaire. La compréhension des phénomènes environnementaux passe par l’étude et la combinaison d’un nombre croissant de données hétérogènes. De nombreuses initiatives internationales (INSPIRE, GEOSS) visent à encourager le partage et l’échange de ces données. Dans ce sujet de recherche, nous traitons de l’intérêt de mettre à disposition des modèles scientifiques sur le web. Nous montrons l’intérêt d’utiliser des applications s’appuyant sur des données géoréférencées et présentons des méthodes et des moyens répondant aux exigences d’interopérabilité. Nous illustrons notre approche par l’implémentation de modèles d’estimation des besoins en eau agricoles et domestiques fonctionnant à diverses échelles spatiales et temporelles. Un prototype basé sur une architecture entièrement orientée services web a été développé. L’outil s’appuie sur les standards Web Feature Service (WFS), Sensor Observation Service (SOS) et Web Processing Service (WPS) de l’OGC. Enfin, la prise en compte des imperfections des données est également abordée avec l’intégration de méthodes d’analyse de sensibilité et de propagation de l’incertitude
Nowadays, 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
5

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.

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This thesis presents the implementation of data fusion techniques for sustainable urban development. Recently, increasingly more geospatial data have been made easily available for no cost. The immeasurable quantities of geospatial data are mainly from four kinds of sources: remote sensing satellites, geographic information systems (GIS) data, citizen science, and sensor web. Among them, satellite images have been mostly used, due to the frequent and repetitive coverage, as well as the data acquisition over a long time period. However, the rather coarse spatial resolution of e.g. 30 m for Landsat 8 multispectral images impairs the application of satellite images in urban areas. Even though image fusion techniques have been used to improve the spatial resolution, the existing image fusion methods are neither suitable for sharpening one band thermal images nor for hyperspectral images with hundreds of bands. Therefore, simplified Ehlers fusion was developed. It adds the spatial information of a high-resolution image into a low-resolution image in the frequency domain through fast Fourier transform (FFT) and filter techniques. The developed algorithm successfully improved the spatial resolution of both one band thermal images as well as hyperspectral images. It can enhance various images, regardless of the number of bands and the spectral coverage, providing more precise measurement and richer information. To investigate the performance of simplified Ehlers fusion in practical use, it was applied for urban heat island (UHI) analysis. This was done by sharpening daytime and nighttime thermal images from Landsat 8, Landsat 7, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The developed algorithm effectively improved the spatial details of the original images so that the temperature differences between agricultural, forest, industrial, transportation, and residential areas could be distinguished from each other. Based on that, it was found that in the study city the causes of UHI are mainly anthropogenic heat from industrial areas as well as high temperatures from the road surface and dense urban fabric. Based on this analysis, corresponding mitigation strategies were tailored. Remote sensing images are useful yet not sufficient to retrieve land use related information, despite high spatial resolution. For sustainable urban development research, remote sensing images need to be incorporated with data from other sources. Accordingly, image fusion needs to be extended to broader data fusion. Extraction of urban vacant land was therefore taken as a second application case. Much effort was spent on the definition of vacant land as unclear definitions lead to ineffective data fusion and incorrect site extraction results. Through an intensive study of the current research and the available open data sources, a vacant land typology is proposed. It includes four categories: transportation-associated land, natural sites, unattended areas or remnant parcels, and brownfields. Based on this typology, a two-level data fusion framework was developed. On the feature level, sites are identified. For each type of vacant land, an individual site extraction rule and data fusion procedure is implemented. The overall data fusion involves satellite images, GIS data, citizen science, and social media data. In the end, four types of vacant land features were extracted from the study area. On the decision level, these extracted sites could be conserved or further developed to support sustainable urban development.
6

Uttam, Kumar *. "Algorithms For Geospatial Analysis Using Multi-Resolution Remote Sensing Data." Thesis, 2012. https://etd.iisc.ac.in/handle/2005/2280.

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Geospatial analysis involves application of statistical methods, algorithms and information retrieval techniques to geospatial data. It incorporates time into spatial databases and facilitates investigation of land cover (LC) dynamics through data, model, and analytics. LC dynamics induced by human and natural processes play a major role in global as well as regional scale patterns, which in turn influence weather and climate. Hence, understanding LC dynamics at the local / regional as well as at global levels is essential to evolve appropriate management strategies to mitigate the impacts of LC changes. This can be captured through the multi-resolution remote sensing (RS) data. However, with the advancements in sensor technologies, suitable algorithms and techniques are required for optimal integration of information from multi-resolution sensors which are cost effective while overcoming the possible data and methodological constraints. In this work, several per-pixel traditional and advanced classification techniques have been evaluated with the multi-resolution data along with the role of ancillary geographical data on the performance of classifiers. Techniques for linear and non-linear un-mixing, endmember variability and determination of spatial distribution of class components within a pixel have been applied and validated on multi-resolution data. Endmember estimation method is proposed and its performance is compared with manual, semi-automatic and fully automatic methods of endmember extraction. A novel technique - Hybrid Bayesian Classifier is developed for per pixel classification where the class prior probabilities are determined by un-mixing a low spatial-high spectral resolution multi-spectral data while posterior probabilities are determined from the training data obtained from ground, that are assigned to every pixel in a high spatial-low spectral resolution multi-spectral data in Bayesian classification. These techniques have been validated with multi-resolution data for various landscapes with varying altitudes. As a case study, spatial metrics and cellular automata based models applied for rapidly urbanising landscape with moderate altitude has been carried out.
7

Uttam, Kumar *. "Algorithms For Geospatial Analysis Using Multi-Resolution Remote Sensing Data." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2280.

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Abstract:
Geospatial analysis involves application of statistical methods, algorithms and information retrieval techniques to geospatial data. It incorporates time into spatial databases and facilitates investigation of land cover (LC) dynamics through data, model, and analytics. LC dynamics induced by human and natural processes play a major role in global as well as regional scale patterns, which in turn influence weather and climate. Hence, understanding LC dynamics at the local / regional as well as at global levels is essential to evolve appropriate management strategies to mitigate the impacts of LC changes. This can be captured through the multi-resolution remote sensing (RS) data. However, with the advancements in sensor technologies, suitable algorithms and techniques are required for optimal integration of information from multi-resolution sensors which are cost effective while overcoming the possible data and methodological constraints. In this work, several per-pixel traditional and advanced classification techniques have been evaluated with the multi-resolution data along with the role of ancillary geographical data on the performance of classifiers. Techniques for linear and non-linear un-mixing, endmember variability and determination of spatial distribution of class components within a pixel have been applied and validated on multi-resolution data. Endmember estimation method is proposed and its performance is compared with manual, semi-automatic and fully automatic methods of endmember extraction. A novel technique - Hybrid Bayesian Classifier is developed for per pixel classification where the class prior probabilities are determined by un-mixing a low spatial-high spectral resolution multi-spectral data while posterior probabilities are determined from the training data obtained from ground, that are assigned to every pixel in a high spatial-low spectral resolution multi-spectral data in Bayesian classification. These techniques have been validated with multi-resolution data for various landscapes with varying altitudes. As a case study, spatial metrics and cellular automata based models applied for rapidly urbanising landscape with moderate altitude has been carried out.

Books on the topic "Geospatial data fusion":

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Wang, Jiaqiu. Shi kong xu lie shu ju fen xi he jian mo. 8th ed. Beijing: Ke xue chu ban she, 2012.

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International 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. Edited by Liu Yaolin 1960-, Tang Xinming, Wuhan da xue. School of Resource and Environmental Science, China Jiao yu bu, and SPIE (Society). Bellingham, Wash: SPIE, 2009.

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Book chapters on the topic "Geospatial data fusion":

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Ochodnicky, Jan. "Data Filtering and Data Fusion in Remote Sensing Systems." In GeoSpatial Visual Analytics, 155–65. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2899-0_12.

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Stankutė, Silvija, and Hartmut Asche. "An Integrative Approach to Geospatial Data Fusion." In 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.

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Moshou, Dimitrios, Ioannis Gravalos, Dimitrios Kateris Cedric Bravo, Roberto Oberti, Jon S. West, and Herman Ramon. "Multisensor Fusion of Remote Sensing Data for Crop Disease Detection." In Geospatial Techniques for Managing Environmental Resources, 201–19. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1858-6_13.

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Yang, Huadong, and Hongping Tuo. "Multi-source Geospatial Vector Data Fusion Technology and Software Design." In 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.

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Kermarrec, Gaël, Vibeke Skytt, and Tor Dokken. "LR B-Splines for Representation of Terrain and Seabed: Data Fusion, Outliers, and Voids." In 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.

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AbstractPerforming surface approximation of geospatial point clouds with locally refined (LR) B-splines comes with several challenges: (i) Point clouds have varying data density, (ii) outliers should be eliminated without deleting features, (iii) voids, also called holes, or data gaps should be treated specifically to avoid the drop of the approximated surface in domains without points. These factors tend to be even more challenging when point clouds acquired from different sensors having different noise characteristics are fused together. The data set becomes non-uniform and the fusing process itself involves a risk of an increased noise level. In this chapter, we provide some tools to answer those specific challenges. We will use terrain and seabed data and show didactically how to perform adaptive surface approximation with local refinement and to select customized parameters. We will further address the problem of choosing an appropriate tolerance for performing an adaptive fitting, and discuss the refinement strategies within the context of LR B-splines. The latter is shown to provide a promising framework for surface fitting of heterogeneous point clouds from various sources.
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Salleh, Siti Aekbal, Zulkiflee Abd. Latif, Faezah Pardi, Emad Mushtaha, and Yarina Ahmad. "Conceptualising the Citizen-Driven Urban Forest Framework to Improve Local Climate Condition: Geospatial Data Fusion and Numerical Simulation." In 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.

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Giannopoulos, Giorgos, Dimitrios Skoutas, Thomas Maroulis, Nikos Karagiannakis, and Spiros Athanasiou. "FAGI: A Framework for Fusing Geospatial RDF Data." In 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.

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Giannopoulos, Giorgos, Nick Vitsas, Nikos Karagiannakis, Dimitrios Skoutas, and Spiros Athanasiou. "FAGI-gis: A Tool for Fusing Geospatial RDF Data." In 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.

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Mbuh, Mbongowo Joseph. "Application of Data Fusion for Uncertainty and Sensitivity Analysis of Water Quality in the Shenandoah River." In Geospatial Intelligence, 1383–410. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8054-6.ch061.

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This article is aimed at demonstrating the feasibility of combining water quality observations with modeling using data fusion techniques for efficient nutrients monitoring in the Shenandoah River (SR). It explores the hypothesis; “Sensitivity and uncertainty from water quality modeling and field observation can be improved through data fusion for a better prediction of water quality.” It models water quality using water quality simulation programs and combines the results with field observation, using a Kalman filter (KF). The results show that the analysis can be improved by using more observations in watersheds where minor variations to the analysis result in large differences in the subsequent forecast. Analyses also show that while data fusion was an invaluable tool to reduce uncertainty, an improvement in the temporal scales would also enhance results and reduce uncertainty. To examine how changes in the field observation affects the final KF analysis, the fusion and lab analysis cross-validation showed some improvement in the results with a very high coefficient of determination.
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Sharma, Arpita, and Samiksha Goel. "Cuckoo Search Based Decision Fusion Techniques for Natural Terrain Understanding." In Geospatial Intelligence, 813–36. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8054-6.ch036.

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This paper proposes two novel nature inspired decision level fusion techniques, Cuckoo Search Decision Fusion (CSDF) and Improved Cuckoo Search Decision Fusion (ICSDF) for enhanced and refined extraction of terrain features from remote sensing data. The developed techniques derive their basis from a recently introduced bio-inspired meta-heuristic Cuckoo Search and modify it suitably to be used as a fusion technique. The algorithms are validated on remote sensing satellite images acquired by multispectral sensors namely LISS3 Sensor image of Alwar region in Rajasthan, India and LANDSAT Sensor image of Delhi region, India. Overall accuracies obtained are substantially better than those of the four individual terrain classifiers used for fusion. Results are also compared with majority voting and average weighing policy fusion strategies. A notable achievement of the proposed fusion techniques is that the two difficult to identify terrains namely barren and urban are identified with similar high accuracies as other well identified land cover types, which was not possible by single analyzers.

Conference papers on the topic "Geospatial data fusion":

1

Meng, Xiaolin, Alan Dodson, Jixian Zhang, Yanhui Cai, Chun Liu, and Keith Geary. "Geospatial Data Fusion for Precision Agriculture." In 2011 International Symposium on Image and Data Fusion (ISIDF). IEEE, 2011. http://dx.doi.org/10.1109/isidf.2011.6024218.

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Percivall, George, and Trevor Taylor. "Advances in fusion of big geospatial data." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8126975.

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West, R. Derek, Brian J. Redman, David A. Yocky, John D. van der Laan, and Dylan Z. Anderson. "Robust terrain classification of high spatial resolution remote sensing data employing probabilistic feature fusion and pixelwise voting." In Geospatial Informatics X, edited by Kannappan Palaniappan, Gunasekaran Seetharaman, Peter J. Doucette, and Joshua D. Harguess. SPIE, 2020. http://dx.doi.org/10.1117/12.2558196.

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Szekely, Pedro, Craig A. Knoblock, Shubham Gupta, Mohsen Taheriyan, and Bo Wu. "Exploiting semantics of web services for geospatial data fusion." In the 1st ACM SIGSPATIAL International Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2068976.2068981.

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Kovalerchuk, Boris, Leonid Perlovsky, and Michael Kovalerchuk. "Modeling spatial uncertainties in geospatial data fusion and mining." In SPIE Defense, Security, and Sensing. SPIE, 2012. http://dx.doi.org/10.1117/12.920878.

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An Xiaoya, Sun Qun, Zhu Rui, Yan Wei, and Wen Chengjie. "The application of data fusion in updating geospatial database actively." In 2010 2nd International Conference on Advanced Computer Control. IEEE, 2010. http://dx.doi.org/10.1109/icacc.2010.5487256.

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Giannecchini, Simone, Francesco Spina, Bryce Nordgren, and Martin Desruisseaux. "Supporting Interoperable Geospatial Data Fusion by adopting OGC and ISO TC 211 standards." In 2006 9th International Conference on Information Fusion. IEEE, 2006. http://dx.doi.org/10.1109/icif.2006.301751.

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Cai, Bofeng, Rong Yu, and Zengxiang Zhang. "Utility of neural net classification for remote sensing data based on an improved image fusion algorithm." In Geoinformatics 2006: GNSS and Integrated Geospatial Applications, edited by Deren Li and Linyuan Xia. SPIE, 2006. http://dx.doi.org/10.1117/12.712584.

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Ngan, Chun-Kit. "Geo-Data Fusion Integrator for Object-Oriented Spatiotemporal OLAP Cubes." In 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.

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Sacharny, D., T. C. Henderson, R. Simmons, A. Mitiche, T. Welker, and X. Fan. "BRECCIA: A novel multi-source fusion framework for dynamic geospatial data analysis." In 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2017. http://dx.doi.org/10.1109/mfi.2017.8170352.

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Reports on the topic "Geospatial data fusion":

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Bissett, W. P., and David D. Kohler. High Resolution Multispectral and Hyperspectral Data Fusion for Advanced Geospatial Information Products. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada630662.

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Bissett, W. P., and David D. Kohler. High Resolution Multispectral and Hyperspectral Data Fusion for Advanced Geospatial Information Products. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada465229.

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