Journal articles on the topic 'Geospatial data fusion'
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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.
Abstract:
<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.
Abstract:
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.
Abstract:
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.
5
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
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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.
Abstract:
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.
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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.
Abstract:
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.
Abstract:
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.
11
Yokoya, Naoto, Pedram Ghamisi, Ronny Hansch, Colin Prieur, Hana Malha, Jocelyn Chanussot, Caleb Robinson, Kolya Malkin, and Nebojsa Jojic. "2021 Data Fusion Contest: Geospatial Artificial Intelligence for Social Good [Technical Committees]." IEEE Geoscience and Remote Sensing Magazine 9, no. 1 (March 2021): 287—C3. http://dx.doi.org/10.1109/mgrs.2021.3055633.
12
Zhao, Wei, and Shangwei Shi. "Integration Method of Monitoring Video and Geospatial Data Based on 3D Modeling." International Journal of Computer Science and Information Technology 1, no. 1 (December 30, 2023): 182–93. http://dx.doi.org/10.62051/ijcsit.v1n1.23.
Abstract:
The fusion technology of videos has been increasingly applied in various fields such as smart cities and smart agriculture, playing a significant role in enabling real-time scene monitoring and decision-making for surveillance personnel. Currently, conventional methods for integrating geographic data with monitoring videos involve mapping two-dimensional geographic information data onto surveillance videos. However, this method exhibits significant mapping errors in scenarios with large terrain variations and 360 degrees multi-angle real-time previews. To address this issue, this paper proposes a fusion method of monitoring videos and geospatial data based on three-dimensional modeling. This method constructs a virtual scene using digital elevation models and vector geographic data and overlays the images captured under the camera viewport in the virtual scene with each frame of the video images in the monitoring video stream, thereby enhancing the video scene based on geographic data. In practical application scenarios, a system for integrating monitoring videos with geospatial data is designed and implemented. Experimental results demonstrate that this method effectively addresses issues such as unfamiliarity with the geographical environment, ambiguity of location information, inaccuracies in mapping due to terrain variations and changes in camera intrinsic parameters, thus showing superior applicability.
13
Hoffman-Hall, Amanda, Tatiana V. Loboda, Joanne V. Hall, Mark L. Carroll, and Dong Chen. "Mapping remote rural settlements at 30 m spatial resolution using geospatial data-fusion." Remote Sensing of Environment 233 (November 2019): 111386. http://dx.doi.org/10.1016/j.rse.2019.111386.
14
Parvaz, Shahoriar, Felicia Teferle, and Abdul Nurunnabi. "Airborne Cross-Source Point Clouds Fusion by Slice-to-Slice Adjustment." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W4-2024 (May 31, 2024): 161–68. http://dx.doi.org/10.5194/isprs-annals-x-4-w4-2024-161-2024.
Abstract:
Abstract. Point cloud fusion is a process plays pivotal role in geospatial data analysis that aims to integrate data from multiple sources to create a comprehensive and precise representation of the environment. Integrating point clouds acquired from cross-source or hybrid sensors presents unique challenges due to differences in geometric accuracy, precision, and the size of data gaps, along with variations in available attributes. Significant progress has been made in developing algorithms and methods to address these challenges, but the problems are not sufficiently resolved and remain one of the most challenging aspects of geospatial data processing. In this paper, we present a new approach for airborne cross-source point cloud fusion through a slice-to-slice adjustment. Our method generates cross-sectional slices and aligns them following some sequential steps. This approach enhances the accuracy and completeness of the fused point cloud, overcoming issues related to geometric disparities and data gaps. Experimental results demonstrate the effectiveness of our approach in improving registration accuracy, preserving geometric detail, and providing valuable insights for utilizing the potentials of both data sources.
15
Chen, D., X. Zhang, N. Chen, J. Yang, and J. Gong. "GEOSPATIAL SENSOR WEB ADAPTOR FOR INTEGRATING DIVERSE INTERNET OF THINGS PROTOCOLS WITHIN SMART CITY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2020 (August 3, 2020): 115–21. http://dx.doi.org/10.5194/isprs-annals-v-4-2020-115-2020.
Abstract:
Abstract. In recent years, the multi-scale comprehensive perception is central to smart city development. We propose an "adaptor" for geospatial sensor web as an integrated sensory system that can integrate access to geodetic equipment based on the Internet of Things technology with multiple platforms and protocols. At the same time, the acquisition, fusion, and processing of sensory resources can perform. The geospatial adaptor can access and process sensors of different IoT protocols to different conditions simultaneously. Grace to this geospatial adaptor, a considerable number of the sensor based on IoT in the community, can achieve distributed access, ensuring the better robustness of the geospatial sensor web. This paper describes the system architecture of the geospatial sensor web adapter. Furthermore, from the perspective of protocol access, it introduces the access capabilities of geospatial sensor web adapter to the standard IoT interface protocols. By comparing the geospatial sensor web adapter with traditional observation methods by experiments and acquisition of test data. The results show that the geospatial sensor web adapter can achieve powerful access capabilities and network stability, and it is a better solution for heterogeneous sensing platform access in smart cities.
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La Guardia, Marcello, and Mila Koeva. "Towards Digital Twinning on the Web: Heterogeneous 3D Data Fusion Based on Open-Source Structure." Remote Sensing 15, no. 3 (January 26, 2023): 721. http://dx.doi.org/10.3390/rs15030721.
Abstract:
Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente—Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks.
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Chaabane, Ferdaous, Safa Réjichi, Houssem Ben Salem, Hassen Elmabrouk, and Florence Tupin. "Strategic Planning of Rural Telecommunication Infrastructure: A Multi-Source Data Fusion and Optimization Model." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 73–78. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-73-2024.
Abstract:
Abstract. The United Nations has underscored the critical role of digital connectivity by integrating it into several sustainable development goals, with the ambition for nations worldwide to achieve comprehensive access by 2030. Over thousand million people, primarily in rural areas, are disconnected from the digital world, highlighting the urgent need for viable and sustainable telecommunications solutions. These areas are characterized by sparse populations and lower economic levels, presenting great challenges for connectivity. This work introduces a strategy for enhancing rural telecommunication planning using geospatial and remote sensing data, deep learning-based clustering techniques, network graphs, and terrain analysis. The objective is to develop an optimal network topology and identify prime locations for telecommunications infrastructure, such as towers or relay stations. The methodology begins with the application of an adapted Deep Embedded Clustering (DEC) technique to identify community boundaries accurately. Then, it combines geospatial data (such as roads, terrain slope, flatness, etc.) and remote sensing data (vegetation, waterways, etc.) through an optimization algorithm. This process aims to determine the most suitable sites for infrastructure placement and the best network topology for connecting these areas. The study focuses on the region of Congo, offering a detailed case study on the application of this approach. Experimental results are presented to demonstrate the effectiveness of the proposed telecommunications expansion strategy.
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Azmil, Rabi'atul'Adawiyah, Mohd Farid Mohd Ariff, Ahmad Firdaus Razali, Suzanna Noor Azmy, Norhadija Darwin, and Khairulnizam M. Idris. "Transforming Physical Crime Scene into Geospatial-based Point Cloud Data." Engineering, Technology & Applied Science Research 14, no. 3 (June 1, 2024): 13974–81. http://dx.doi.org/10.48084/etasr.6888.
Abstract:
Terrestrial Laser Scanning (TLS) and Close-Range Photogrammetry (CRP) are advanced techniques for capturing 3D data in crime scene reconstruction, offering complementary information. Despite taking multiple scans and images from different angles to ensure a comprehensive model, limitations, such as device positioning, shadows, object distance, and laser beam angles prevent the creation of a complete crime scene model. Therefore, combining TLS and CRP data is crucial for achieving a comprehensive reconstruction. This study aims to transform a physical crime scene into a geospatial-based reconstructed model known as point clouds. The technique used was highly rich in realistic features, digitally reconstructed from TLS and CRP. The data sources were then fused via a rigid body transformation, creating a comprehensive crime scene model. The combined point cloud measurements were compared with measurements obtained from a high-precision Vernier caliper to ascertain their accuracy. The resulting Root Mean Square (RMSE) difference between the fused point cloud data and the high-precision caliper measurements was approximately ±4mm. The fusion of TLS and CRP data provides reliable and highly accurate 3D model point clouds, making it suitable for forensic applications.
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Popov, M., O. Fedorovsky, S. Stankevich, V. Filipovich, A. Khyzhniak, I. Piestova, M. Lubskyi, and M. Svideniuk. "REMOTE SENSING TECHNOLOGIES AND GEOSPATIAL MODELLING HIERARCHY FOR SMART CITY SUPPORT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5/W1 (December 13, 2017): 51–56. http://dx.doi.org/10.5194/isprs-annals-iv-5-w1-51-2017.
Abstract:
The approach to implementing the remote sensing technologies and geospatial modelling for smart city support is presented. The hierarchical structure and basic components of the smart city information support subsystem are considered. Some of the already available useful practical developments are described. These include city land use planning, urban vegetation analysis, thermal condition forecasting, geohazard detection, flooding risk assessment. Remote sensing data fusion approach for comprehensive geospatial analysis is discussed. Long-term city development forecasting by Forrester – Graham system dynamics model is provided over Kiev urban area.
20
Andronie, Mihai, George Lăzăroiu, Mariana Iatagan, Iulian Hurloiu, Roxana Ștefănescu, Adrian Dijmărescu, and Irina Dijmărescu. "Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things." ISPRS International Journal of Geo-Information 12, no. 2 (January 21, 2023): 35. http://dx.doi.org/10.3390/ijgi12020035.
Abstract:
The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms.
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Su, Chen, Xinli Hu, Qingyan Meng, Linlin Zhang, Wenxu Shi, and Maofan Zhao. "A multimodal fusion framework for urban scene understanding and functional identification using geospatial data." International Journal of Applied Earth Observation and Geoinformation 127 (March 2024): 103696. http://dx.doi.org/10.1016/j.jag.2024.103696.
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Li, Ke, Han Lin Zhang, and Lin Du. "Research on Technology of Semantic Fusion in Geospatial Information Based on Ontology." Advanced Materials Research 403-408 (November 2011): 3039–43. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3039.
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Because of lacking detailed metadata information in the traditional geospatial data, it would lead to being short of part semantic information. Facing the demand of interoperability in growing application of geographic information service, Ontology technology is regarded as one of Effective approach to solve the question of data heterogeneous and interoperability. So in this paper, it would use some new theory and methods of ontology into geographic Information Services, which would solve the difficulties of the geographical spatial data integration. Facing the demand of interoperability in growing application of geographic information service, Ontology techology is widely noticed in the past few years, and is regarded as one of Effective approach to solve the question of data heterogeneous and interoperability. Using the mapping of concepts and attributes, ontology expresses practical geographic space in Semantic level, analyzing the structure and content of geographical spatial database and setting up corresponding domain ontology. At last , it use some logical operations to solve the question of data heterogeneous and interoperability. So in this paper, it would use some new theory and methods of ontology into geographic Information Services, which would solve the difficulties of the geographical spatial data integration.
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Srivastava, Prashant K., George P. Petropoulos, Manika Gupta, Sudhir K. Singh, Tanvir Islam, and Dimitra Loka. "Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining." Modeling Earth Systems and Environment 5, no. 2 (December 10, 2018): 627–43. http://dx.doi.org/10.1007/s40808-018-0555-5.
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Patrucco, G., G. Cortese, F. Giulio Tonolo, and A. Spanò. "THERMAL AND OPTICAL DATA FUSION SUPPORTING BUILT HERITAGE ANALYSES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 619–26. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-619-2020.
Abstract:
Abstract. The recent developments of passive sensors techniques, that have been able to take advantage of the technological innovations related to sensors technical features, sensor calibration, the use of UAV systems (Unmanned Aerial Vehicle), the integration of image matching techniques and SfM (Structure from Motion) algorithms, enable to exploit both thermal and optical data in multi-disciplinary projects. This synergy boost the application of Infrared Thermography (IRT) to new application domains, since the capability to provide thematic information of the analysed objects benefits from the typical advantages of data georeferencing and metric accuracy, being able to compare results investigating different phenomena.This paper presents a research activity in terrestrial and aerial (UAV) applications, aimed at generating photogrammetric products with certified and controlled geometric and thematic accuracy even when the acquisitions of thermal data were not initially designed for the photogrammetric process. The basic principle investigated and pursued is the processing of a photogrammetric block of images, including thermal IR and optical imagery, using the same reference system, which allows the use of co-registration algorithms. Such approach enabled the generation of radiance maps, orthoimagery and 3D models embedding the thermal information of the investigated surfaces, also known as texture mapping; these geospatial dataset are particularly useful in the context of the built Heritage documentation, characterised by complex analyses challenges that a perfect fit for investigations based on interdisciplinary approaches.
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Huang, Y. S., G. Q. Zhou, T. Yue, H. B. Yan, W. X. Zhang, X. Bao, Q. Y. Pan, and J. S. Ni. "VECTOR AND RASTER DATA LAYERED FUSION AND 3D VISUALIZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1127–34. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1127-2020.
Abstract:
Abstract. Although contemporary geospatial science has made great progress, spatial data fusion of vector and raster data is still a problem in the geoinformation science environment. In order to solve the problem, this paper proposes a method which merges vector and raster data. Firstly, the row and column numbers of the raster data, and the X, Y values of the vector data are represented by Morton code in the C++ environment, respectively. Secondly, we establish the the raster data table and the vector data table in the Oracle database to store the vector data and the raster data. Third, this paper uses the minimum selection bounding box method to extract the top data of the building model. Finally, we divide the vector and raster data into four steps to obtain the fusion data table, and we call the fusion data in the database for 3D visualization. This method compresses the size of data of the original data, and simultaneously divides the data into three levels, which not only solves the problem of data duplication storage and unorganized storage, but also can realize vector data storage and the raster data storage in the same database at the same time. Thus, the fusion original orthophoto data contains the gray values of building roofs and the elevation data, which can improve the availability of vector data and the raster data in the 3D Visualization application.
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McGrath, H., E. Stefanakis, and M. Nastev. "DEM Fusion of Elevation REST API Data in Support of Rapid Flood Modelling." GEOMATICA 70, no. 4 (December 2016): 283–97. http://dx.doi.org/10.5623/cig2016-402.
Abstract:
Digital elevation models (DEM) are an integral part of flood modelling. High resolution DEM data are not always available or affordable for communities, thus other elevation data sources are explored. While the accuracy of some of these sources has been rigorously tested (e.g., SRTM, ASTER), others, such as Natural Resources Canada’s Canadian Digital Elevation Model (CDEM) and Google and Bings’ Elevation REST APIs, have not yet been properly evaluated. Details pertaining to acquisition source and accuracy are often unreported for APIs. To include these data in geospatial applications and test and reduce uncertainty, data fusion is explored. Thus, this paper introduces a new method of elevation data fusion. The novel method incorporates clustering and inverse distance weighting (IDW) concepts in the computation of a new fusion elevation surface. The results of the individual DEMs and fusion DEMs are compared to high-resolution Light Detection and Ranging (LiDAR) surface and flood inundation maps for two study areas in New Brunswick. Comparison of individual surfaces to LiDAR find that the results meet their posted accuracy specifications, with the Bing data computing the smallest mean bias and the CDEM the smallest RMSE. Fusion of all three surfaces via the proposed method increases the correlation and minimizes both RMSE and mean bias when compared to LiDAR, independent of the terrain, thus producing a more accurate DEM.
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Inoue, Ryo, and Koichiro Den. "Extraction of Continuous and Discrete Spatial Heterogeneities: Fusion Model of Spatially Varying Coefficient Model and Sparse Modelling." ISPRS International Journal of Geo-Information 11, no. 7 (June 23, 2022): 358. http://dx.doi.org/10.3390/ijgi11070358.
Abstract:
Geospatial phenomena often have spatial heterogeneity, which is caused by differences in the data generation process from place to place. There are two types of spatial heterogeneity: continuous and discrete, and there has been much discussion about how to analyze one type of spatial heterogeneity. Although geospatial phenomena can have both types of spatial heterogeneities, previous studies have not sufficiently discussed how to consider these two different types of spatial heterogeneity simultaneously and how to detect them separately, which may lead to biased estimates and the wrong interpretation of geospatial phenomena. This study proposes a new approach for the analysis of spatial data with both heterogeneities by combining the eigenvector spatial filtering-based spatially varying coefficient (ESF-SVC) model, which assumes the continuous spatial heterogeneity and generalized lasso (GL) estimation, which assumes discrete spatial heterogeneity and proposes the ESF-GL-SVC model. The performance of ESF-GL-SVC was evaluated through experiments based on a Monte Carlo simulation and confirms that the ESF-GL-SVC showed better performance in estimating coefficients with both types of spatial heterogeneity than the previous two models. The application of the apartment rent data showed that the ESF-GL-SVC outputs the result with the smallest BIC value, and the estimated coefficients depict continuous and discrete spatial heterogeneity in the dataset. Reasonable coefficients were estimated using the ESF-GL-SVC, although some coefficients by ESF-SVC were not.
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Werner, Piotr A. "Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies." Algorithms 13, no. 8 (August 3, 2020): 188. http://dx.doi.org/10.3390/a13080188.
Abstract:
The Reed-Solomon algorithm is well known in different fields of computer science. The novelty of this study lies in the different interpretation of the algorithm itself and its scope of application for remote sensing, especially at the preparatory stage, i.e., data fusion. A short review of the attempts to use different data fusion approaches in geospatial technologies explains the possible usage of the algorithm. The rationale behind its application for data fusion is to include all possible information from all acquired spectral bands, assuming that complete composite information in the form of one compound image will improve both the quality of visualization and some aspects of further quantitative and qualitative analyses. The concept arose from an empirical, heuristic combination of geographic information systems (GIS), map algebra, and two-dimensional cellular automata. The challenges are related to handling big quantitative data sets and the awareness that these numbers are in fact descriptors of a real-world multidimensional view. An empirical case study makes it easier to understand the operationalization of the Reed-Solomon algorithm for land use studies.
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Zhang, Jia, Xiulian Wang, Xiaotong Zhang, Xiaofei Bai, and Qiang Chen. "Construction of multi-scale grid for massive land survey data." E3S Web of Conferences 206 (2020): 03018. http://dx.doi.org/10.1051/e3sconf/202020603018.
Abstract:
In the face of ever-growing and complex massive multi-source spatiotemporal data, the traditional vector data model is increasingly difficult to meet the needs of efficient data organization, management, calculation and analysis. Based on the simple and widely used geographic grid data organization model, this paper designs a technical method to convert vector data into multi-scale grid data, establishes a unified, standardized and seamless land spatial grid data model, and analyses the area accuracy of multi-scale grid data. Practice shows that the model can better meet the needs of multi-scale geospatial information integration and analysis, and it is easy to carry out distributed data processing, which provides technical support for the efficient organization, fusion and analysis of spatiotemporal data.
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Somanath, Sanjay, Vasilis Naserentin, Orfeas Eleftheriou, Daniel Sjölie, Beata Stahre Wästberg, and Anders Logg. "Towards Urban Digital Twins: A Workflow for Procedural Visualization Using Geospatial Data." Remote Sensing 16, no. 11 (May 28, 2024): 1939. http://dx.doi.org/10.3390/rs16111939.
Abstract:
A key feature for urban digital twins (DTs) is an automatically generated detailed 3D representation of the built and unbuilt environment from aerial imagery, footprints, LiDAR, or a fusion of these. Such 3D models have applications in architecture, civil engineering, urban planning, construction, real estate, Geographical Information Systems (GIS), and many other areas. While the visualization of large-scale data in conjunction with the generated 3D models is often a recurring and resource-intensive task, an automated workflow is complex, requiring many steps to achieve a high-quality visualization. Methods for building reconstruction approaches have come a long way, from previously manual approaches to semi-automatic or automatic approaches. This paper aims to complement existing methods of 3D building generation. First, we present a literature review covering different options for procedural context generation and visualization methods, focusing on workflows and data pipelines. Next, we present a semi-automated workflow that extends the building reconstruction pipeline to include procedural context generation using Python and Unreal Engine. Finally, we propose a workflow for integrating various types of large-scale urban analysis data for visualization. We conclude with a series of challenges faced in achieving such pipelines and the limitations of the current approach. However, the steps for a complete, end-to-end solution involve further developing robust systems for building detection, rooftop recognition, and geometry generation and importing and visualizing data in the same 3D environment, highlighting a need for further research and development in this field.
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Zyelyk, Ya I., N. M. Kussul, S. V. Skakun, and A. Yu Shelestov. "Natural disaster risk assessment based on the ensemble processing and technology of heterogeneous geospatial data fusion." Kosmìčna nauka ì tehnologìâ 17, no. 1 (January 30, 2011): 60–64. http://dx.doi.org/10.15407/knit2011.01.060.
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Cvetek, Dominik, Mario Muštra, Niko Jelušić, and Leo Tišljarić. "A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion." Applied Sciences 11, no. 5 (March 5, 2021): 2306. http://dx.doi.org/10.3390/app11052306.
Abstract:
Traffic congestion occurs when traffic demand is greater than the available network capacity. It is characterized by lower vehicle speeds, increased travel times, arrival unreliability, and longer vehicular queueing. Congestion can also impose a negative impact on the society by decreasing the quality of life with increased pollution, especially in urban areas. To mitigate the congestion problem, traffic engineers and scientists need quality, comprehensive, and accurate data to estimate the state of traffic flow. Various types of data collection technologies have different advantages and disadvantages as well as data characteristics, such as accuracy, sampling frequency, and geospatial coverage. Multisource data fusion increases the accuracy and provides a comprehensive estimation of the performance of traffic flow on a road network. This paper presents a literature overview related to the estimation of congestion and prediction based on the data collected from multiple sources. An overview of data fusion methods and congestion indicators used in the literature for traffic state and congestion estimation is given. Results of these methods are analyzed, and a disseminative analysis of the advantages and disadvantages of surveyed methods is presented.
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Montenegro, Jose A., and Antonio Muñoz. "EventGeoScout: Fostering Citizen Empowerment and Augmenting Data Quality through Collaborative Geographic Information Governance and Optimization." ISPRS International Journal of Geo-Information 13, no. 2 (February 2, 2024): 46. http://dx.doi.org/10.3390/ijgi13020046.
Abstract:
In this manuscript, we present EventGeoScout, an innovative framework for collaborative geographic information management, tailored to meet the needs of the dynamically changing landscape of geographic data integration and quality enhancement. EventGeoScout enables the seamless fusion of open data from different sources and provides users with the tools to refine and improve data quality. A distinctive feature of our framework is its commitment to platform-agnostic data management, ensuring that processed datasets are accessible via standard Geographic Information System (GIS) tools, reducing the maintenance burden on organizations while ensuring the continued relevance of the data. Our approach goes beyond the boundaries of traditional data integration, enabling users to fully harness the power of geospatial information by simplifying the data creation process and providing a versatile solution to the complex challenges posed by layered geospatial data. To demonstrate the versatility and robustness of EventGeoScout as an optimization tool, we present a case study centered on the Uncapacitated Facility Location Problem (UFLP), where a genetic algorithm was used to achieve outstanding performance on both traditional computing platforms and smartphone devices. As a concrete case study, we applied our solution in the context of the Málaga City Marathon, using the latest data from the last edition of the marathon.
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Zhang, Xusong, and Maria Rosario Rodavia. "Population Spatialization based on Random Forest Model and Multi-source Geospatial big data." Frontiers in Computing and Intelligent Systems 5, no. 1 (August 28, 2023): 107–10. http://dx.doi.org/10.54097/fcis.v5i1.12005.
Abstract:
Population spatialization research is an important approach to achieve fine-grained management of urban space and coordinated development of rural resources and the environment. By converting administrative-level population data into a finer grid scale, it allows for in-depth analysis of the spatial distribution characteristics of population density and geographic heterogeneity within a region. Currently, in China, a population census is conducted every ten years, with the township as the smallest statistical unit. However, due to advancements in computer science and geography, the level of precision in data can no longer meet the requirements of modern geographical research. Population spatialization, based on national population statistics, utilizes techniques such as multi-source data fusion and data mining to decompose large-scale population data into corresponding grid-based data, enabling more accurate spatial representation of national population statistics and facilitating the understanding of population distribution patterns. This study used administrative boundary data for 88 counties in Guizhou Province in 2021, county-level population data from the 2021 China County Statistical Yearbook, and diverse geospatial data from Guizhou in 2017. Nine spatial variables that impact the spatial distribution of the study area's population, such as points of interest and nighttime light indices, were extracted. A random forest method was used to construct a population spatialization model and simulate population distribution.
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Wang, Yaming, Yiyang Liu, Wenqing Huang, Xiaoping Ye, and Mingfeng Jiang. "Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification." Applied Sciences 13, no. 21 (October 30, 2023): 11890. http://dx.doi.org/10.3390/app132111890.
Abstract:
Scene classification in remote sensing is a pivotal research area, traditionally relying on visual information from aerial images for labeling. The introduction of ground environment audio as a novel geospatial data source adds valuable information for scene classification. However, bridging the structural gap between aerial images and ground environment audio is challenging, rendering popular two-branch networks ineffective for direct data fusion. To address this issue, the study in this research presents the Two-stage Fusion-based Audiovisual Classification Network (TFAVCNet). TFAVCNet leverages both audio and visual modules to extract deep semantic features from ground environmental audio and remote sensing images, respectively. The audiovisual fusion module combines and fuses information from both modalities at the feature and decision levels, facilitating joint training and yielding a more-robust solution. The proposed method outperforms existing approaches, as demonstrated by the experimental results on the ADVANCE dataset for remote sensing audiovisual scene classification, offering an innovative approach to enhanced scene classification.
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Yokoya, Naoto, Pedram Ghamisi, Ronny Hansch, Colin Prieur, Hana Malha, Jocelyn Chanussot, Caleb Robinson, Kolya Malkin, and Nebojsa Jojic. "Report on the 2021 IEEE GRSS Data Fusion Contest—Geospatial Artificial Intelligence for Social Good [Technical Committees]." IEEE Geoscience and Remote Sensing Magazine 9, no. 4 (December 2021): 274–77. http://dx.doi.org/10.1109/mgrs.2021.3121628.
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Mishra, Vikash Kumar, Utsav Nareti, Raghvendra Kumar, Triloki Pant, Abdul Aleem, Ajeet Singh, and Seblewongel Esseynew Biable. "GDF: A Novel Image Fusion Approach for Compelling Depiction of Earthly Features." Journal of Sensors 2023 (October 7, 2023): 1–10. http://dx.doi.org/10.1155/2023/9429505.
Abstract:
One of the most challenging aspects of satellite remote sensing is image fusion. Image fusion increases the visual interpretation of the image and has many applications such as monitoring water bodies, land cover, urbanisation, agriculture, national defence, and so forth. Remote sensing applications require images with a high spatial and spectral resolution for accurately processing and distinguishing the land cover classes with fine texture and shape details. Due to technical limitations, most satellites cannot take high-resolution multi-spectral images but can get high-resolution panchromatic images and low-resolution multi-spectral satellite images separately. This article proposes a novel fusion method, geospatial data fusion (GDF), to obtain high-resolution multi-spectral images. GDF, along with three well-known fusion methods viz., Brovey Transform (BT), wavelet transform (WT), and Fourier transform (FT), have been comparatively implemented to fuse the Cartosat-2 and Sentinel-2 imageries of the Sangam area of Prayagraj, India. The fusion has been done to extract the earth’s surface features from the fused imagery. In this research, the fused image is utilised for river water mapping. Results confirm that the GDF outperforms other existing fusion methods and successfully maps the river water in the study area.
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Vosselman, G., S. J. Oude Elberink, and M. Y. Yang. "PREFACE – ISPRS GEOSPATIAL WEEK 2019." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 1. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1-2019.
Abstract:
<p><strong>Abstract.</strong> The ISPRS Geospatial Week 2019 is a combination of 13 workshops organised by 30 ISPRS Working Groups active in areas of interest of ISPRS. The Geospatial Week 2019 is held from 10–14 June 2019, and is convened by the University of Twente acting as local organiser. The Geospatial Week 2019 is the fourth edition, after Antalya Turkey in 2013, La Grande Motte France in 2015 and Wuhan China in 2017.</p><p>The following 13 workshops provide excellent opportunities to discuss the latest developments in the fields of sensors, photogrammetry, remote sensing, and spatial information sciences:</p> <ul> <li>C3M&amp;GBD – Collaborative Crowdsourced Cloud Mapping and Geospatial Big Data</li> <li>CHGCS – Cryosphere and Hydrosphere for Global Change Studies</li> <li>EuroCow-M3DMaN – Joint European Calibration and Orientation Workshop and Workshop onMulti-sensor systems for 3D Mapping and Navigation</li> <li>HyperMLPA – Hyperspectral Sensing meets Machine Learning and Pattern Analysis</li> <li>Indoor3D</li> <li>ISSDQ – International Symposium on Spatial Data Quality</li> <li>IWIDF – International Workshop on Image and Data Fusion</li> <li>Laser Scanning</li> <li>PRSM – Planetary Remote Sensing and Mapping</li> <li>SarCon – Advances in SAR: Constellations, Signal processing, and Applications</li> <li>Semantics3D – Semantic Scene Analysis and 3D Reconstruction from Images and ImageSequences</li> <li>SmartGeoApps – Advanced Geospatial Applications for Smart Cities and Regions</li> <li>UAV-g – Unmanned Aerial Vehicles in Geomatics</li> </ul> <p>Many of the workshops are part of well-established series of workshops convened in the past. They cover topics like UAV photogrammetry, laser scanning, spatial data quality, scene understanding, hyperspectral imaging, and crowd sourcing and collaborative mapping with applications ranging from indoor mapping and smart cities to global cryosphere and hydrosphere studies and planetary mapping.</p><p>In total 143 full papers and 357 extended abstracts were submitted by authors from 63 countries. 1250 reviews have been delivered by 295 reviewers. A total of 81 full papers have been accepted for the volume IV-2/W5 of the International Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Another 289 papers are published in volume XLII-2/W13 of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences.</p><p>The editors would like to thank all contributing authors, reviewers and all workshop organizers for their role in preparing and organizing the Geospatial Week 2019. Thanks to their contributions, we can offer an excessive and varying collection in the Annals and the Archives.</p><p>We hope you enjoy reading the proceedings.</p><p>George Vosselman, Geospatial Week Director 2019, General Chair<br> Sander Oude Elberink, Programme Chair<br> Michael Ying Yang, Programme Chair</p>
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Vosselman, G., S. J. Oude Elberink, and M. Y. Yang. "PREFACE – ISPRS Geospatial Week 2019." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 1. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-1-2019.
Abstract:
<p><strong>Abstract.</strong> The ISPRS Geospatial Week 2019 is a combination of 13 workshops organised by 30 ISPRS Working Groups active in areas of interest of ISPRS. The Geospatial Week 2019 is held from 10–14 June 2019, and is convened by the University of Twente acting as local organiser. The Geospatial Week 2019 is the fourth edition, after Antalya Turkey in 2013, La Grande Motte France in 2015 and Wuhan China in 2017.</p><p>The following 13 workshops provide excellent opportunities to discuss the latest developments in the fields of sensors, photogrammetry, remote sensing, and spatial information sciences:</p> <ul> <li>C3M&amp;GBD – Collaborative Crowdsourced Cloud Mapping and Geospatial Big Data</li> <li>CHGCS – Cryosphere and Hydrosphere for Global Change Studies</li> <li>EuroCow-M3DMaN – Joint European Calibration and Orientation Workshop and Workshop onMulti-sensor systems for 3D Mapping and Navigation</li> <li>HyperMLPA – Hyperspectral Sensing meets Machine Learning and Pattern Analysis</li> <li>Indoor3D</li> <li>ISSDQ – International Symposium on Spatial Data Quality</li> <li>IWIDF – International Workshop on Image and Data Fusion</li> <li>Laser Scanning</li> <li>PRSM – Planetary Remote Sensing and Mapping</li> <li>SarCon – Advances in SAR: Constellations, Signal processing, and Applications</li> <li>Semantics3D – Semantic Scene Analysis and 3D Reconstruction from Images and ImageSequences</li> <li>SmartGeoApps – Advanced Geospatial Applications for Smart Cities and Regions</li> <li>UAV-g – Unmanned Aerial Vehicles in Geomatics</li> </ul> <p>Many of the workshops are part of well-established series of workshops convened in the past. They cover topics like UAV photogrammetry, laser scanning, spatial data quality, scene understanding, hyperspectral imaging, and crowd sourcing and collaborative mapping with applications ranging from indoor mapping and smart cities to global cryosphere and hydrosphere studies and planetary mapping.</p><p>In total 143 full papers and 357 extended abstracts were submitted by authors from 63 countries. 1250 reviews have been delivered by 295 reviewers. A total of 81 full papers have been accepted for the volume IV-2/W5 of the International Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Another 289 papers are published in volume XLII-2/W13 of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences.</p><p>The editors would like to thank all contributing authors, reviewers and all workshop organizers for their role in preparing and organizing the Geospatial Week 2019. Thanks to their contributions, we can offer an excessive and varying collection in the Annals and the Archives.</p><p>We hope you enjoy reading the proceedings.</p><p>George Vosselman, Geospatial Week Director 2019, General Chair<br /> Sander Oude Elberink, Programme Chair<br /> Michael Ying Yang, Programme Chair</p>
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Hosseini, Fatemeh Sadat, Myoung Bae Seo, Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Mohammad Jamshidi, and Soo-Mi Choi. "Geospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting." Sustainability 15, no. 19 (September 24, 2023): 14125. http://dx.doi.org/10.3390/su151914125.
Abstract:
This study aims to predict vital soil physical properties, including clay, sand, and silt, which are essential for agricultural management and environmental protection. Precision distribution of soil texture is crucial for effective land resource management and precision agriculture. To achieve this, we propose an innovative approach that combines Geospatial Artificial Intelligence (GeoAI) with the fusion of satellite imagery to predict soil physical properties. We collected 317 soil samples from Iran’s Golestan province for dependent data. The independent dataset encompasses 14 parameters from Landsat-8 satellite images, seven topographic parameters from the Shuttle Radar Topography Mission (SRTM) DEM, and two meteorological parameters. Using the Random Forest (RF) algorithm, we conducted feature importance analysis. We employed a Convolutional Neural Network (CNN), RF, and our hybrid CNN-RF model to predict soil properties, comparing their performance with various metrics. This hybrid CNN-RF network combines the strengths of CNN networks and the RF algorithm for improved soil texture prediction. The hybrid CNN-RF model demonstrated superior performance across metrics, excelling in predicting sand (MSE: 0.00003%, RMSE: 0.006%), silt (MSE: 0.00004%, RMSE: 0.006%), and clay (MSE: 0.00005%, RMSE: 0.007%). Moreover, the hybrid model exhibited improved precision in predicting clay (R2: 0.995), sand (R2: 0.992), and silt (R2: 0.987), as indicated by the R2 index. The RF algorithm identified MRVBF, LST, and B7 as the most influential parameters for clay, sand, and silt prediction, respectively, underscoring the significance of remote sensing, topography, and climate. Our integrated GeoAI-satellite imagery approach provides valuable tools for monitoring soil degradation, optimizing agricultural irrigation, and assessing soil quality. This methodology has significant potential to advance precision agriculture and land resource management practices.
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Knyaz, Vladimir A., Vladimir V. Kniaz, Sergey Yu Zheltov, and Kirill S. Petrov. "Multi-sensor Data Analysis for Aerial Image Semantic Segmentation and Vectorization." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 291–96. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-291-2024.
Abstract:
Abstract. One of the urgent and constantly in demand problems is updating maps. Maps, representing geo-information in vector form, have undoubted advantages in compactness and ”readability” compared to aerial photographs. The issue of maps actuality is critically important for rational urban planning, precision farming, the relevance of the cadastre and other geospatial applications. Various sources of data are used for maps updating, with aerial imagery being the main and rich source of information. Automatic processing of aerial photographs makes it possible to efficiently extract vector information, providing operational monitoring and accounting for changes that have appeared. The presented study addresses the problem of multi sensor information fusion in order to obtain accurate vector information. We use aerial images as a main data source and additionally the data of laser scanning and ground survey to increase performance of automatic image semantic segmentation and vectorization. The proposed framework is demonstrated on the task of forest monitoring.
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Lyimo, Neema Nicodemus, Zhenfeng Shao, Ally Mgelwa Ally, Nana Yaw Danquah Twumasi, Orhan Altan, and Camilius A. Sanga. "A Fuzzy Logic-Based Approach for Modelling Uncertainty in Open Geospatial Data on Landfill Suitability Analysis." ISPRS International Journal of Geo-Information 9, no. 12 (December 9, 2020): 737. http://dx.doi.org/10.3390/ijgi9120737.
Abstract:
Besides OpenStreetMap (OSM), there are other local sources, such as open government data (OGD), that have the potential to enrich the modeling process with decision criteria that uniquely reflect some local patterns. However, both data are affected by uncertainty issues, which limits their usability. This work addresses the imprecisions on suitability layers generated from such data. The proposed method is founded on fuzzy logic theories. The model integrates OGD, OSM data and remote sensing products and generate reliable landfill suitability results. A comparison analysis demonstrates that the proposed method generates more accurate, representative and reliable suitability results than traditional methods. Furthermore, the method has facilitated the introduction of open government data for suitability studies, whose fusion improved estimations of population distribution and land-use mapping than solely relying on free remotely sensed images. The proposed method is applicable for preparing decision maps from open datasets that have undergone similar generalization procedures as the source of their uncertainty. The study provides evidence for the applicability of OGD and other related open data initiatives (ODIs) for land-use suitability studies, especially in developing countries.
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Ma, Qiwei, Zhaoya Gong, Jing Kang, Ran Tao, and Anrong Dang. "Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of the Beijing-Tianjin-Hebei Megaregion." Remote Sensing 12, no. 16 (August 5, 2020): 2513. http://dx.doi.org/10.3390/rs12162513.
Abstract:
Most of the shrinking cities experience an unbalanced deurbanization across different urban areas in cities. However, traditional ways of measuring urban shrinkage are focused on tracking population loss at the city level and are unable to capture the spatially heterogeneous shrinking patterns inside a city. Consequently, the spatial mechanism and patterns of urban shrinkage inside a city remain less understood, which is unhelpful for developing accommodation strategies for shrinkage. The smart city initiatives and practices have provided a rich pool of geospatial big data resources and technologies to tackle the complexity of urban systems. Given this context, we propose a new measure for the delineation of shrinking areas within cities by introducing a new concept of functional urban shrinkage, which aims to capture the mismatch between urban built-up areas and the areas where significantly intensive human activities take place. Taking advantage of a data fusion approach to integrating multi-source geospatial big data and survey data, a general analytical framework is developed to construct functional shrinkage measures. Specifically, Landsat-8 remote sensing images were used for extracting urban built-up areas by supervised neural network classifications and Geographic Information System tools, while cellular signaling data from China Unicom Inc. was used to depict human activity areas generated by spatial clustering methods. Combining geospatial big data with urban land-use functions obtained from land surveys and Points-Of-Interests data, the framework further enables the comparison between cities from dimensions characterized by indices of spatial and urban functional characteristics and the landscape fragmentation; thus, it has the capacity to facilitate an in-depth investigation of fundamental causes and internal mechanisms of urban shrinkage. With a case study of the Beijing-Tianjin-Hebei megaregion using data from various sources collected for the year of 2018, we demonstrate the validity of this approach and its potential generalizability for other spatial contexts in facilitating timely and better-informed planning decision support.
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Poliyapram, Vinayaraj, Weimin Wang, and Ryosuke Nakamura. "A Point-Wise LiDAR and Image Multimodal Fusion Network (PMNet) for Aerial Point Cloud 3D Semantic Segmentation." Remote Sensing 11, no. 24 (December 10, 2019): 2961. http://dx.doi.org/10.3390/rs11242961.
Abstract:
3D semantic segmentation of point cloud aims at assigning semantic labels to each point by utilizing and respecting the 3D representation of the data. Detailed 3D semantic segmentation of urban areas can assist policymakers, insurance companies, governmental agencies for applications such as urban growth assessment, disaster management, and traffic supervision. The recent proliferation of remote sensing techniques has led to producing high resolution multimodal geospatial data. Nonetheless, currently, only limited technologies are available to fuse the multimodal dataset effectively. Therefore, this paper proposes a novel deep learning-based end-to-end Point-wise LiDAR and Image Multimodal Fusion Network (PMNet) for 3D segmentation of aerial point cloud by fusing aerial image features. PMNet respects basic characteristics of point cloud such as unordered, irregular format and permutation invariance. Notably, multi-view 3D scanned data can also be trained using PMNet since it considers aerial point cloud as a fully 3D representation. The proposed method was applied on two datasets (1) collected from the urban area of Osaka, Japan and (2) from the University of Houston campus, USA and its neighborhood. The quantitative and qualitative evaluation shows that PMNet outperforms other models which use non-fusion and multimodal fusion (observational-level fusion and feature-level fusion) strategies. In addition, the paper demonstrates the improved performance of the proposed model (PMNet) by over-sampling/augmenting the medium and minor classes in order to address the class-imbalance issues.
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Wang, Jifei, Chen-Chieh Feng, and Zhou Guo. "A Novel Graph-Based Framework for Classifying Urban Functional Zones with Multisource Data and Human Mobility Patterns." Remote Sensing 15, no. 3 (January 26, 2023): 730. http://dx.doi.org/10.3390/rs15030730.
Abstract:
Recent research has shown the advantages of incorporating multisource geospatial data into the classification of urban functional zones (UFZs), particularly remote sensing and social sensing data. However, the effects of combining datasets of varying quality have not been thoroughly analyzed. In addition, human mobility patterns from social sensing data, which capture signals of human activities, are often represented by origin-destination pairs, thus ignoring spatial relationships between UFZs embedded in mobility trajectories. To address the aforementioned issues, this study proposed a graph-based UFZ classification framework that fuses semantic features from high spatial resolution (HSR) remote sensing images, points of interest, and GPS trajectory data. The framework involves three main steps: (1) High-level scene information in HSR remote sensing imageries was extracted through deep neural networks, and multisource semantic embeddings were constructed based on physical features and social sensing features from multiple geospatial data sources; (2) UFZ mobility graph was constructed by spatially joining trajectory information with UFZs to construct topological connections between functional parcel segments; and (3) UFZ segments and multisource semantic features were transformed into nodes and embeddings in the mobility graphs, and subsequently graph-based models were adopted to identify UFZs. The proposed framework was tested on Zhuhai and Singapore datasets. Results indicated that it outperformed traditional classification methods with an overall accuracy of 76.7% and 84.5% for Zhuhai and Singapore datasets, respectively. The proposed framework contributes to literature in heterogeneous data fusion and is generalizable to other UFZ classification scenarios where human mobility patterns play a role.
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Xie, Yilin, Song Zhang, Xiaolin Meng, Dinh Tung Nguyen, George Ye, and Haiyang Li. "An Innovative Sensor Integrated with GNSS and Accelerometer for Bridge Health Monitoring." Remote Sensing 16, no. 4 (February 6, 2024): 607. http://dx.doi.org/10.3390/rs16040607.
Abstract:
This paper presents an innovative integrated sensor that combines GNSS and a low-cost accelerometer for bridge health monitoring. GNSS and accelerometers are both significant and effective sensors for structural monitoring, but they each have limitations. The sampling rate of GNSS data is relatively low, making it challenging to capture high-frequency vibrations, while accelerometers struggle with low-frequency signals and are susceptible to environmental changes. Additionally, GNSS receivers and accelerometers are often installed separately, leading to challenges in data fusion processing due to differing temporal and geospatial references. The proposed integrated sensor addresses these issues by synchronizing GNSS and an accelerometer’s time and geospatial coordinate reference. This allows for a more accurate and reliable deformation and vibration measurement for bridge monitoring. The performance of the new sensor was assessed using a high-quality/cost Leica GM30 GNSS receiver and a Sherborne A545 accelerometer. Experiments conducted on the Wilford suspension bridge demonstrate the effectiveness of this innovative integrated sensor in measuring deformation and vibration for bridge health monitoring. The limitation of the low-cost MEMS (Micro Electromechanical System) accelerometer for the weak motion frequency detection is also pointed out.
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Batsis, Georgios, Panagiotis Partsinevelos, and Georgios Stavrakakis. "A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters." Energies 14, no. 20 (October 17, 2021): 6773. http://dx.doi.org/10.3390/en14206773.
Abstract:
Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output.
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Zhang, Chunhua, Xiangkun Qi, Kelin Wang, Mingyang Zhang, and Yueming Yue. "The application of geospatial techniques in monitoring karst vegetation recovery in southwest China." Progress in Physical Geography: Earth and Environment 41, no. 4 (July 12, 2017): 450–77. http://dx.doi.org/10.1177/0309133317714246.
Abstract:
The karst region in southwestern China, one of the largest continuous karst areas in the world, is special for its high landscape heterogeneity, unique hydrology, high endemism among vegetation species and high intensity of human disturbance. The region had experienced severe degradation through karst rocky desertification (KRD) between the 1950s and 1990s. Starting in the late 1990s, various levels of the Chinese government conducted several ecological projects to recover degraded karst ecosystems. It was reported that the implementation of these projects had been successful in facilitating the recovery of karst vegetation in many areas. However, global climate changes may compromise the efficacy of recovery. Geospatial techniques had been employed to map and monitor karst ecosystem conditions during the recovery process. We examined the history and progress of the various geospatial techniques applied to monitor and evaluate karst vegetation conditions. In addition, we reviewed the techniques used to assess and monitor KRD, KRD influencing factors, vegetation community type, fractional vegetation cover, vegetation dynamics, vegetation productivity, ecosystem goods and services, vegetation biodiversity, ecosystem health and rural society changes. We also explored the potential to apply geospatial techniques for karst vegetation recovery in the future. It is projected that there will be more remotely sensed images for the vegetation dynamics monitoring at numerous scales. New techniques (e.g. image fusion and data assimilation) will be available to manage scale and heterogeneity issues in the karst landscape.
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Moumni, Aicha, Tarik Belghazi, Brahim Maksoudi, and Abderrahman Lahrouni. "Argan Tree (Argania spinosa (L.) Skeels) Mapping Based on Multisensor Fusion of Satellite Imagery in Essaouira Province, Morocco." Journal of Sensors 2021 (October 5, 2021): 1–17. http://dx.doi.org/10.1155/2021/6679914.
Abstract:
Tree species identification and their geospatial distribution mapping are crucial for forest monitoring and management. The satellite-based remote sensing time series of Sentinel missions (Sentinel-1 and Sentinel-2) are a perfect tool to map the type, location, and extent of forest cover over large areas at local or global scale. This study is focused on the geospatial mapping of the endemic argan tree (Argania spinosa (L.) Skeels) and the identification of two other tree species (sandarac gum and olive trees) using optical and synthetic aperture radar (SAR) time series. The objective of the present work is to detect the actual state of forest species trees, more specifically the argan tree, in order to be able to study and analyze forest changes (degradation) and make new strategies to protect this endemic tree. The study was conducted over an area located in Essaouira province, Morocco. The support vector machine (SVM) algorithm was used for the classification of the two types of data. We first classified the optical data for tree species identification and mapping. Second, the SAR time series were used to identify the argan tree and distinguish it from other species. Finally, the two types of satellite images were combined to improve and compare the results of classification with those obtained from single-source data. The overall accuracy (OA) of optical classification reached 86.9% with a kappa coefficient of 0.84 and declined strongly to 37.22% (kappa of 0.29) for SAR classification. The fusion of multisensor data (optical and SAR images) reached an OA of 86.51%. A postclassification was performed to improve the results. The classified images were smoothed, and therefore, the quantitative and qualitative results showed an improvement, in particular for optical classification with a highest OA of 89.78% (kappa coefficient of 0.88). The study confirmed the potential of the multitemporal optical data for accurate forest cover mapping and endemic species identification.
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Alipour, Mohamad, Inga La Puma, Joshua Picotte, Kasra Shamsaei, Eric Rowell, Adam Watts, Branko Kosovic, Hamed Ebrahimian, and Ertugrul Taciroglu. "A Multimodal Data Fusion and Deep Learning Framework for Large-Scale Wildfire Surface Fuel Mapping." Fire 6, no. 2 (January 17, 2023): 36. http://dx.doi.org/10.3390/fire6020036.
Abstract:
Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels.