scholarly journals Przyszłość systemów rejestracji nieruchomości wobec rozwoju inteligentnych technologii. Wybrane problemy prawne na tle wniosków z konferencji „Common Vision Conference 2016. Migration to a Smart World”, Amsterdam, 5–7 czerwca 2016 roku

2017 ◽  
Vol 15 (2) ◽  
pp. 301-320
Author(s):  
Maria Kaczorowska
Keyword(s):  
Spatial Data ◽  
Data Exchange ◽  
Spatial Information ◽  
Smart Cities ◽  
Information Service ◽  
Advanced Technology ◽  
Technological Advancement ◽  
Land Registration ◽  
The Impact ◽  
Common Vision

The development of information technologies offers new possibilities of use of information collected in public registers, such as land registers and cadastres, which play a significant role in establishing the infrastructure for spatial information. Efficient use of spatial information systems with the purpose of a sustainable land management shall be based on en suring the interconnection of different information resources, data exchange, as well as a broad access to data. The role of land registration systems in the context of technological advancement was the subject of the Common Vision Conference 2016. Migration to a Smart World, held on 5–7 June 2016 in Amsterdam. The conference was organized by Europe’s five leading mapping, cadastre and land registry associations, cooperating within a “Common Vision” agreement: EuroGeographics, Permanent Committee on Cadastre, European Land Registries Association, European Land Information Service and Council of European Geodetic Surveyors. The discussion during the conference focused on topics regarding the idea of smart cities, marine cadastre, interoperability of spatial data, as well as the impact of land registers and cadastres on creating the infrastructure for spatial information and developing e-government, at both national and European levels. The paper aims to present an overview of issues covered by the conference and also to highlight some important problems arising from implementing advanced technology solutions in the field of land registration.

scholarly journals PREFACE – ISPRS Geospatial Week 2019

2019 ◽  
Vol IV-2/W5 ◽  
pp. 1-1
Author(s):  
G. Vosselman ◽  
S. J. Oude Elberink ◽  
M. Y. Yang
Keyword(s):  
Remote Sensing ◽  
Data Quality ◽  
Spatial Data ◽  
Laser Scanning ◽  
Spatial Information ◽  
Smart Cities ◽  
International Workshop ◽  
Crowd Sourcing ◽  
Spatial Data Quality ◽  
Information Sciences

<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;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>

Research on Spatial Statistical Data Compression Algorithm Based on Point Cloud Clustering

2014 ◽  
Vol 543-547 ◽  
pp. 1619-1622
Author(s):  
Shao Ming Pan ◽  
Hong Li ◽  
Ge Tang
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Statistical Data ◽  
Spatial Information ◽  
Spatial Clustering ◽  
Information Service ◽  
Compression Algorithm ◽  
Compression Efficiency ◽  
Data Services ◽  
Run Length Coding

The spatial information service system have the characteristics of a large amount of massive data and a large number of users, so the compression algorithm for statistical data will affects the system performance of spatial data services. A compression algorithm for spatial statistical data based on point cloud clustering is proposed in this paper to solve the above-mentioned problems. The access rule of the spatial data will be mapped into a point cloud. And the part of points will be deleted according to the clustering gradient of point cloud. Then the statistical data is compressed by run-length coding and spatial clustering extraction. The experimental results show that the algorithm have a high compression efficiency.

scholarly journals Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1372 ◽  
Author(s):  
Manuel Garcia Alvarez ◽  
Javier Morales ◽  
Menno-Jan Kraak
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Spatial Information ◽  
Smart Cities ◽  
Spatial Data Analysis ◽  
Sensor Data ◽  
Reference Architecture ◽  
Real Time Processing ◽  
Time Processing ◽  
Event Driven

Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.

scholarly journals THREE-DIMENSIONAL DIGITIZATION OF ENVIRONMENTS AND BUILDINGS FOR SMART CITY APPLICATIONS

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 65-71
Author(s):  
C. Altuntas
Keyword(s):  
Spatial Data ◽  
Smart City ◽  
Urban Areas ◽  
Spatial Information ◽  
Smart Cities ◽  
Low Cost ◽  
Measurement Techniques ◽  
Night Time ◽  
Data Infrastructure ◽  
Cloud Data

Abstract. The smart cities that promise a sustainable future cannot be thought of independently from the spatial information infrastructure. It is very important to keep the spatial data infrastructure up-to-date for guidance and information in smart city applications (SCA). Easy and low-cost acquisition is an important factor in updating spatial data. Today, there are many measurement techniques to collecting 3-D spatial data of urban areas and land topography. On the other hand, indoor measurement and 3-D modelling techniques are used in the creation of building information modelling (BIM). In this study, measurement techniques that provide 3-D point cloud data to SCA are examined. Consequently, image based photogrammetry and dense matching methods enable low cost measurement than LiDAR based active measurement. The active 3-D measurement techniques have high accuracy especially for mid and long ranges. The LiDAR, that can be applied at day or night time, offer more opportunity to performing SCA like autonomous vehicle and robotic navigations. Nevertheless, LiDAR can only capture structure, not texture, and therefore has limits to the types of data that it can capture. The LiDAR and image based methods are complement to each other in 3-D reality capture. The 3-D measurement techniques are exploited according to SCA as alone or together.

scholarly journals Designing the Cyberinfrastructure for Spatial Data Curation, Visualization, and Sharing

10.29173/iq11 ◽  
2017 ◽  
Vol 41 (1-4) ◽  
pp. 15 ◽  
Author(s):  
Yue Li ◽  
Nicole Kong ◽  
Stanislav Pejša
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Data Curation ◽  
Data Repository ◽  
Health Humanities ◽  
Web Mapping ◽  
Web Map ◽  
Institutional Data ◽  
The Impact ◽  
The Web

Widely used across disciplines such as natural resources, social sciences, public health, humanities, and economics, spatial data is an important component in many studies and has promoted interdisciplinary research development. Though an institutional data repository provides a great solution for data curation, preservation, and sharing, it usually lacks the spatial visualization capability, which limits the use of spatial data to professionals. To increase the impact of research-generated spatial data and truly turn them into digital maps for a broader user base, we have designed and developed the workflow and cyberinfrastructure to extend the current capability of our institutional data repository by visualizing the spatial data on the web. In this project, we added a GIS server to the original institutional data repository cyberinfrastructure, which enables web map services. Then, through a web mapping API, we visualized the spatial data as an interactive web map and embedded in the data repository web page. From the user’s perspective, researchers can still identify, cite and reuse the dataset by downloading the data and metadata and the DOI offered by the data repository. General information users can also browse the web maps to find location-based information. In addition, these data was ingested into the spatial data portal to increase the discoverability for spatial information users. Initial usage statistics suggest that this cyberinfrastructure has greatly improved the spatial data usage and extended the institutional data repository to facilitate spatial data sharing.

scholarly journals PREFACE – ISPRS GEOSPATIAL WEEK 2019

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1-1
Author(s):  
G. Vosselman ◽  
S. J. Oude Elberink ◽  
M. Y. Yang
Keyword(s):  
Remote Sensing ◽  
Data Quality ◽  
Spatial Data ◽  
Laser Scanning ◽  
Spatial Information ◽  
Smart Cities ◽  
International Workshop ◽  
Crowd Sourcing ◽  
Spatial Data Quality ◽  
Information Sciences

<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;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>

scholarly journals NEW COMPETENCES IN ACADEMIC EDUCATION THROUGH REALISATION OF THE BESTSDI AND EO4GEO PROJECTS

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Željko Bačić
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Smart Cities ◽  
Earth Observation ◽  
Spatial Data Infrastructure ◽  
Academic Education ◽  
Data Infrastructure ◽  
Solution Approach ◽  
Global Challenge ◽  
Erasmus Programme

Development of Earth observation technologies from Space and air which are enabling intensive use of spatial information and Spatial data infrastructure which regulates usage of spatial information, respectively also concepts like Internet of things, Smart cities, Precise farming, Intelligent transportation and similar, impose on academic society necessity to redefine competences which students of bright variety of study programmes must overmaster during their study. In the frame of European Union Erasmus+ programme is, among other, initated two projects which goal is to solve this global challenge. The EO4GEO project aim is to define Earth observation from Space and air competences on the European level, while the BESTSDI project aim is to define Spatial data infrastructure competences on the regional level. Content of those projects, solution approach obstinance, methodology and their goals, respectively also expected effects on study programmes are shown in this paper.

Geographic Information System and Big Spatial Data

2020 ◽  
Vol 16 (4) ◽  
pp. 101-145
Author(s):  
Raja Sher Afgun Usmani ◽  
Ibrahim Abaker Targio Hashem ◽  
Thulasyammal Ramiah Pillai ◽  
Anum Saeed ◽  
Akibu Mahmoud Abdullahi
Keyword(s):  
Information System ◽  
Big Data ◽  
Geographic Information System ◽  
Spatial Data ◽  
Spatial Information ◽  
Geographic Information ◽  
Data Sources ◽  
Spatial Datasets ◽  
Big Data Technologies ◽  
The Impact

Geographic information system (GIS) is designed to generate maps, manage spatial datasets, perform sophisticated “what if” spatial analyses, visualize multiple spatial datasets simultaneously, and solve location-based queries. The impact of big data is in every industry, including the GIS. The location-based big data also known as big spatial data has significant implications as it forces the industry to contemplate how to acquire and leverage spatial information. In this study, a comprehensive taxonomy is created to provide a better understanding of the uses of GIS and big spatial data. The taxonomy is made up of big data technologies, GIS data sources, tools, analytics, and applications. The authors look into the importance of big spatial data and its implications, review the data sources, and GIS analytics, applications, and GIS tools. Furthermore, in order to guide researchers interested in GIS, the challenges that require substantial research efforts are taken into account. Lastly, open issues in GIS that require further observation are summarized.

scholarly journals Measuring the Impact of Natural Hazards with Citizen Science: The Case of Flooded Area Estimation Using Twitter

2021 ◽  
Vol 13 (6) ◽  
pp. 1153
Author(s):  
Pierrick Bruneau ◽  
Etienne Brangbour ◽  
Stéphane Marchand-Maillet ◽  
Renaud Hostache ◽  
Marco Chini ◽  
...  
Keyword(s):  
Natural Hazards ◽  
Spatial Data ◽  
Spatial Information ◽  
Temporal Frequency ◽  
Geographic Information ◽  
Area Measurement ◽  
Remotely Sensed Data ◽  
Aggregation Techniques ◽  
Flooded Area ◽  
The Impact

Twitter has significant potential as a source of Volunteered Geographic Information (VGI), as its content is updated at high frequency, with high availability thanks to dedicated interfaces. However, the diversity of content types and the low average accuracy of geographic information attached to individual tweets remain obstacles in this context. The contributions in this paper relate to the general goal of extracting actionable information regarding the impact of natural hazards on a specific region from social platforms, such as Twitter. Specifically, our contributions describe the construction of a model classifying whether given spatio-temporal coordinates, materialized by raster cells in a remote sensing context, lie in a flooded area. For training, remotely sensed data are used as the target variable, and the input covariates are built on the sole basis of textual and spatial data extracted from a Twitter corpus. Our contributions enable the use of trained models for arbitrary new Twitter corpora collected for the same region, but at different times, allowing for the construction of a flooded area measurement proxy available at a higher temporal frequency. Experimental validation uses true data that were collected during Hurricane Harvey, which caused significant flooding in the Houston urban area between mid-August and mid-September 2017. Our experimental section compares several spatial information extraction methods, as well as various textual representation and aggregation techniques, which were applied to the collected Twitter data. The best configuration yields a F1 score of 0.425, boosted to 0.834 if restricted to the 10% most confident predictions.

DARCY RIBEIRO UNIVERSITY CAMPUS MAP – 2017- TECHNICAL NOTE

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
Rafael Sanzio Araújo dos Anjos ◽  
Jose Leandro de Araujo Conceição ◽  
Jõao Emanuel ◽  
Matheus Nunes
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Technical Note ◽  
University Campus ◽  
Communication Tool ◽  
Sources Of Information ◽  
Data Manipulation ◽  
Geographic Space ◽  
The Many ◽  
New Generation

The spatial information regarding the use of territory is one of the many strategies used to answer and to inform about what happened, what is happening and what may happen in geographic space. Therefore, the mapping of land use as a communication tool for the spatial data made significant progress in improving sources of information, especially over the last few decades, with new generation remote sensing products for data manipulation.

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