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1
Eldawy, Ahmed, and Gobe Hobona. "The 2nd ACM SIGSPATIAL International Workshop on Geospatial Data Access and Processing APIs (SpatialAPI 2020)." SIGSPATIAL Special 12, no. 3 (January 25, 2021): 20–22. http://dx.doi.org/10.1145/3447994.3448003.
Повний текст джерелаАнотація:
With the increasing amount of publicly available geospatial data, the demand on spatial data exploration and analysis kept growing. The SIGSPATIAL community is both a provider of new systems with cutting-edge technology on accessing and processing geospatial data, and a user for all these systems. The SpatialAPI workshop is designed to help the SIGSPATIAL community by growing the knowledge of the existing well-established systems that are available for accessing and processing geospatial data. This includes, but is not limited to, web APIs, programming libraries, database systems, and geospatial extensions to existing systems.
2
Mooney, P., and M. Minghini. "GEOSPATIAL DATA EXCHANGE USING BINARY DATA SERIALIZATION APPROACHES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W1-2022 (August 6, 2022): 307–13. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-307-2022.
Повний текст джерелаАнотація:
Abstract. In this paper we investigate the benefits of binary data serialization as a means of storing and sharing large amounts of geospatial data in an interoperable way. De-facto text-based exchange encodings typically exposed by modern Application Programming Interfaces (APIs), including eXtensible Markup Language (XML) and JavaScript Object Notation (JSON), are generally inefficient for an increasingly higher number of applications due to their inflated volumes of data, low speed and the high computational cost for parsing and processing. In this work we consider comparisons of JSON/Geospatial JSON (GeoJSON) and two popular binary data encodings (Protocol Buffers and Apache Avro) for storing and sharing geospatial data. Using a number of experiments, we illustrate the advantages and disadvantages of both approaches for common workflows that make use of geospatial data encodings such as GeoPackage and GeoJSON. The paper contributes a number of practical recommendations around the potential for binary data serialization for interoperable (geospatial) data storage and sharing in the future.
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Tischler, Michael A. "Accelerating Geospatial Modeling in ArcGIS with Graphical Processor Units." International Journal of Applied Geospatial Research 7, no. 4 (October 2016): 41–52. http://dx.doi.org/10.4018/ijagr.2016100104.
Повний текст джерелаАнотація:
Geospatial data can be enormous in size and tedious to process efficiently on standard computational workstations. Distributing the processing tasks through highly parallelized processing reduces the burden on the primary processor and processing times can drastically shorten as a result. ERSI's ArcGIS, while widely used in the military, does not natively support multi-core processing or utilization of graphic processor units (GPUs). However, the ArcPy Python library included in ArcGIS 10 provides geospatial developers with the means to process geospatial data in a flexible environment that can be linked with GPU application programming interfaces (APIs). This research extends a custom desktop geospatial model of spatial similarity for remote soil classification which takes advantage of both standard ArcPy/ArcGIS geoprocessing functions and custom GPU kernels, operating on an NVIDIA Tesla S2050 equipped with potential access to 1792 cores. The author will present their results which describe hardware and software configurations, processing efficiency gains, and lessons learned.
4
Folmer, E., W. Beek, and L. Rietveld. "LINKED DATA VIEWING AS PART OF THE SPATIAL DATA PLATFORM OF THE FUTURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W8 (July 11, 2018): 49–52. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w8-49-2018.
Повний текст джерелаАнотація:
<p><strong>Abstract.</strong> The Land Registry and Mapping Agency of the Netherlands (‘Kadaster’ in Dutch) is developing an online publication platform for sharing its geospatial data assets called KDP (`Kadaster Data Platform’ in Dutch). One of the main goals of this platform is to better share geospatial data with the wider, web-oriented world, including its developers, approaches, and standards. Linked Open Data (W3C), GeoSPARQL (OGC), and Open APIs (OpenAPI Specification) are the predominant standardized approaches for this purpose. As a result, the most important spatial datasets of the Netherlands – including several key registries – are now being published as Linked Open Data that can be accessed through a SPARQL endpoint and a collection of REST APIs. In addition to providing raw access to the data, Kadaster Data Platform also offers developers functionalities that allow them to gain a better understanding about the contents of its datasets. These functionalities include various ways for viewing Linked Data . This paper focuses on two of the main components the Kadaster Data Platform is using for this purpose: FacetCheck and Data Stories.</p>
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Virtanen, Juho-Pekka, Arttu Julin, Kaisa Jaalama, and Hannu Hyyppä. "Creating Open Online Applications with Geospatial Interfaces - Case Study "Palvelututka"." Photogrammetric Journal of FInland 27, no. 2 (2021): 1–14. http://dx.doi.org/10.17690/021272.1.
Повний текст джерелаАнотація:
Three-dimensional city models are an increasingly common data set maintained by many cities globally. At the same time, the focus of research has shifted from their production to their utilization in application development. We present the implementation of a demonstrator application combining the online visualization of a 3D city information model with the data from an application programming interface. By this, we aim to demonstrate the combined use of city APIs and 3D geospatial assets, promote their use for application development and show the performance of existing, openly available tools for 3D city model application development
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Zhang, H., W. Huang, J. Jiang, M. Du, and J. Yang. "A HIGH-CURRENCY GEO-SPATIAL SERVICE GATEWAY FOR NATIONAL GEO-INFORMATION SERVICE PLATFORM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June 30, 2021): 383–88. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2021-383-2021.
Повний текст джерелаАнотація:
Abstract. Today, more and more geospatial services are provided by the governments and enterprises to share various geographic information data and functions, and services-based application integration has become a trend. However, many problems existed in the geo-platform for Geographic information sharing while providing services in the form of API, such as the coexistence of different versions of the same service, similar service routes of different APIs, cluttered service protocols, and complex authority management, that makes the integration among different geographic information services difficult and reduces the development efficiency. There are already some API gateway technologies to solve the problem, but the characteristics of geospatial services are not considered in the existing product. To address these problems, this paper proposed a high-currency geospatial service gateway system for National Geo-Information Service Platform based on the opensource framework of Kong for realizing the unified management and authorized open. The system provides the whole lifecycle management and fine-grained control for the service, and the functions such as unified geospatial service access, protocol conversion, service management, authorization verification, rate limiting, and security protection are also equipped. The system has been released and integrated in the National Geo-Information Service Platform, supporting hundreds of millions of service invocation every day. The result proves it simplifies geospatial services management, deployment, and application, and benefits the exchanging and sharing of geographic information.
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Simoes, J., and A. Cerciello. "SERVING GEOSPATIAL DATA USING MODERN AND LEGACY STANDARDS: A CASE STUDY FROM THE URBAN HEALTH DOMAIN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W1-2022 (August 6, 2022): 419–25. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-419-2022.
Повний текст джерелаАнотація:
Abstract. The eMOTIONAL Cities project sets out to understand how the natural and built environment can shape the feelings and emotions of those who experience it. It does so with a cross-disciplinary approach which includes urban planners, doctors, psychologists, neuroscientists and engineers. At the core of this research project, lies a Spatial Data Infrastructure (SDI) which assembles datasets that characterise the emotional landscape and built environment, in different Cities across Europe and the US. The SDI is a key tool, not only to make the research data available within the project consortium, but also to allow cross-fertilisation with other ongoing projects and later on, to reach a wider public audience. For more than twenty years SDIs have adopted the OGC W*s service interfaces, which are based on SOAP, the Simple Object Access Protocol. In recent years a new “family” of APIs has emerged within OGC, which is more aligned with modern web practices. In this project, we set out to leverage the advantages of this new approach, and compiled a stack to implement an SDI based on OGC APIs. However, we realised that we still need to support the legacy standards, either because an OGC API replacement is not mature enough, or there are no implementations available. This has led us to compile another stack based on the legacy standards. In this paper we describe our architecture, along with the challenges that we had to address. Both stacks are based on OSGeo Software, and they are available on GitHub.
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Bhattacharya, D., and M. Painho. "DESIGN FOR CONNECTING SPATIAL DATA INFRASTRUCTURES WITH SENSOR WEB (SENSDI)." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 33–39. http://dx.doi.org/10.5194/isprsannals-iii-4-33-2016.
Повний текст джерелаАнотація:
Integrating Sensor Web With Spatial Data Infrastructures (SENSDI) aims to extend SDIs with sensor web enablement, converging geospatial and built infrastructure, and implement test cases with sensor data and SDI. It is about research to harness the sensed environment by utilizing domain specific sensor data to create a generalized sensor webframework. The challenges being semantic enablement for Spatial Data Infrastructures, and connecting the interfaces of SDI with interfaces of Sensor Web. The proposed research plan is to Identify sensor data sources, Setup an open source SDI, Match the APIs and functions between Sensor Web and SDI, and Case studies like hazard applications, urban applications etc. We take up co-operative development of SDI best practices to enable a new realm of a location enabled and semantically enriched World Wide Web - the "Geospatial Web" or "Geosemantic Web" by setting up one to one correspondence between WMS, WFS, WCS, Metadata and 'Sensor Observation Service' (SOS); 'Sensor Planning Service' (SPS); 'Sensor Alert Service' (SAS); a service that facilitates asynchronous message interchange between users and services, and between two OGC-SWE services, called the 'Web Notification Service' (WNS). Hence in conclusion, it is of importance to geospatial studies to integrate SDI with Sensor Web. The integration can be done through merging the common OGC interfaces of SDI and Sensor Web. Multi-usability studies to validate integration has to be undertaken as future research.
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Bhattacharya, D., and M. Painho. "DESIGN FOR CONNECTING SPATIAL DATA INFRASTRUCTURES WITH SENSOR WEB (SENSDI)." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 33–39. http://dx.doi.org/10.5194/isprs-annals-iii-4-33-2016.
Повний текст джерелаАнотація:
Integrating Sensor Web With Spatial Data Infrastructures (SENSDI) aims to extend SDIs with sensor web enablement, converging geospatial and built infrastructure, and implement test cases with sensor data and SDI. It is about research to harness the sensed environment by utilizing domain specific sensor data to create a generalized sensor webframework. The challenges being semantic enablement for Spatial Data Infrastructures, and connecting the interfaces of SDI with interfaces of Sensor Web. The proposed research plan is to Identify sensor data sources, Setup an open source SDI, Match the APIs and functions between Sensor Web and SDI, and Case studies like hazard applications, urban applications etc. We take up co-operative development of SDI best practices to enable a new realm of a location enabled and semantically enriched World Wide Web - the "Geospatial Web" or "Geosemantic Web" by setting up one to one correspondence between WMS, WFS, WCS, Metadata and 'Sensor Observation Service' (SOS); 'Sensor Planning Service' (SPS); 'Sensor Alert Service' (SAS); a service that facilitates asynchronous message interchange between users and services, and between two OGC-SWE services, called the 'Web Notification Service' (WNS). Hence in conclusion, it is of importance to geospatial studies to integrate SDI with Sensor Web. The integration can be done through merging the common OGC interfaces of SDI and Sensor Web. Multi-usability studies to validate integration has to be undertaken as future research.
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NOLAN, JAMES J., ARUN K. SOOD, and ROBERT SIMON. "DISCOVERING AND COMPOSING DISTRIBUTED ATOMIC AGENTS FOR IMAGERY AND GEOSPATIAL PROBLEM SOLVING." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 08 (December 2002): 995–1019. http://dx.doi.org/10.1142/s021800140200212x.
Повний текст джерелаАнотація:
This paper describes our approach to building a scalable, flexible agent-based architecture for imagery and geospatial processing. Central to this approach is the agent discovery and composition mechanism which scales to support networks with thousands of agents. The agent architecture implements over 100 imagery and geospatial processing agents based on the Java Advanced Imaging and OpenMap(TM) APIs. The agents are distributed over a Jini enabled network, and communicate with one another via JavaSpaces. We discuss our "atomic" approach in this paper: developing low-level processing agents that are used by application of specific agents. We discuss several concepts in this approach: agent lookup and discovery through traditional information retrieval techniques, the ability to rapidly prototype agents based on commercial software products, and a knowledge management approach that reuses prior processing approaches and results. We present results demonstrating the scalability of our agent discovery and composition mechanism to compare them with other traditional discovery mechanisms, and demonstrate how the discovery mechanism scales to support thousands of agents.
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Eldawy, Ahmed. "The 1st ACM SIGSPATIAL International Workshop on Geospatial Data Access and Processing APIs (SpatialAPI 2019)." SIGSPATIAL Special 11, no. 3 (February 13, 2020): 14. http://dx.doi.org/10.1145/3383653.3383658.
Повний текст джерела
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Bharadwaj, S., R. Dubey, M. I. Zafar, and S. Biswas. "RASTER DATA BASED AUTOMATED NOISE DATA INTEGRATION FOR NOISE MAPPING LIMITING DATA DEPENDENCY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June 30, 2021): 159–66. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2021-159-2021.
Повний текст джерелаАнотація:
Abstract. Noise has become a recurrent problem worldwide. Road traffic noise studies in India are fewer and restricted only to the metropolitan areas. These studies focused on recording, monitoring, analysis, modelling, and mapping. The major concern is with the onsite collection of vehicular noise data from road sites. Road traffic noise maps have been generated by using traditional techniques that involve the collection of road traffic noise by experts. There are negligible studies in the area of automated noise generation for road traffic noise. In this paper, the study examines the problems that an individual is facing in collecting onsite noise data. Onsite Noise data collection with Sound Pressure level increases the delay. A noise map is a graphical representation of the spatial dissemination in a given area for a characterized period. Developing any geospatial application requires the collection of geospatial data and attribute information. Open-source geospatial data are largely available today in the form of Map APIs. Making a model to extract spatial and attribute information from it can offer an easy solution for urban applications, without needing a separate collection of geospatial or attribute information. Google raster maps for city roads and surrounding buildings in UP are tried to be used to extract roads, buildings, vehicles, trees, etc. Various geometrical setups of vehicles in several similar road segments are tried classified using ML algorithms. Vehicular clusters in road segments are classified into 3 categories, high, medium, and low. Further characterized in terms of the range of noise spectra associated with it incorporating field data. These noise scenes are then utilized to predict the various types of simulated noise maps predicted around the road segments on an instantaneous scale, with an estimation of accuracy.
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Bhattacharya, D., and M. Painho. "SMART CITIES INTELLIGENCE SYSTEM (SMACiSYS) INTEGRATING SENSOR WEB WITH SPATIAL DATA INFRASTRUCTURES (SENSDI)." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W3 (September 25, 2017): 21–28. http://dx.doi.org/10.5194/isprs-annals-iv-4-w3-21-2017.
Повний текст джерелаАнотація:
The paper endeavours to enhance the Sensor Web with crucial geospatial analysis capabilities through integration with Spatial Data Infrastructure. The objective is development of automated smart cities intelligence system (SMACiSYS) with sensor-web access (SENSDI) utilizing geomatics for sustainable societies. There has been a need to develop automated integrated system to categorize events and issue information that reaches users directly. At present, no web-enabled information system exists which can disseminate messages after events evaluation in real time. Research work formalizes a notion of an integrated, independent, generalized, and automated geo-event analysing system making use of geo-spatial data under popular usage platform. Integrating Sensor Web With Spatial Data Infrastructures (SENSDI) aims to extend SDIs with sensor web enablement, converging geospatial and built infrastructure, and implement test cases with sensor data and SDI. The other benefit, conversely, is the expansion of spatial data infrastructure to utilize sensor web, dynamically and in real time for smart applications that smarter cities demand nowadays. Hence, SENSDI augments existing smart cities platforms utilizing sensor web and spatial information achieved by coupling pairs of otherwise disjoint interfaces and APIs formulated by Open Geospatial Consortium (OGC) keeping entire platform open access and open source. SENSDI is based on Geonode, QGIS and Java, that bind most of the functionalities of Internet, sensor web and nowadays Internet of Things superseding Internet of Sensors as well. In a nutshell, the project delivers a generalized real-time accessible and analysable platform for sensing the environment and mapping the captured information for optimal decision-making and societal benefit.
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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.
Повний текст джерелаАнотація:
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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Jiang, W., and E. Stefanakis. "A RESTFUL API FOR THE EXTENDED WHAT3WORDS ENCODING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 97–104. http://dx.doi.org/10.5194/isprs-annals-iv-4-97-2018.
Повний текст джерелаАнотація:
<p><strong>Abstract.</strong> Geocoding is a fundamental task of Geographic Information System (GIS) processing and analysis. It allows the transformation between a location reference (i.e., an address or an alphanumeric code) and coordinates, which is often an essential step when performing spatial analysis, mapping, and other geolocation related processes. Providing software functionality through RESTful APIs is a common practice in geospatial applications. Client programs are able to access or process geospatial data easily through a lightweight and scalable RESTful web service. Existing geocoding RESTful API providers include Google Maps Geocoding API, ArcGIS Geocoding REST API, MapQuest Open Geocoding API and what3words (w3w) Geocoding API. Extensions of what3words geocoding system have recently been proposed to overcome its limitation of fixed resolution and lack of consideration of the third dimension. This paper considers one of the extensions, the Quadtree Extension Model (QTEM) and introduces a RESTful API that provides operations for forward geocoding, reverse geocoding, single line and polygon encoding, and centre points encoding for a given area. The resources published by the web service could be implemented by software programs performing indoor and outdoor location referencing, location marking and path finding. This API could facilitate various geographic applications such as facility management, emergency evacuation, navigation and student survey data management in a university campus environment.</p>
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Xie, Qingyun. "THE DESIGN OF A HIGH PERFORMANCE EARTH IMAGERY AND RASTER DATA MANAGEMENT AND PROCESSING PLATFORM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 14, 2016): 551–55. http://dx.doi.org/10.5194/isprs-archives-xli-b4-551-2016.
Повний текст джерелаАнотація:
This paper summarizes the general requirements and specific characteristics of both geospatial raster database management system and raster data processing platform from a domain-specific perspective as well as from a computing point of view. It also discusses the need of tight integration between the database system and the processing system. These requirements resulted in Oracle Spatial GeoRaster, a global scale and high performance earth imagery and raster data management and processing platform. The rationale, design, implementation, and benefits of Oracle Spatial GeoRaster are described. Basically, as a database management system, GeoRaster defines an integrated raster data model, supports image compression, data manipulation, general and spatial indices, content and context based queries and updates, versioning, concurrency, security, replication, standby, backup and recovery, multitenancy, and ETL. It provides high scalability using computer and storage clustering. As a raster data processing platform, GeoRaster provides basic operations, image processing, raster analytics, and data distribution featuring high performance computing (HPC). Specifically, HPC features include locality computing, concurrent processing, parallel processing, and in-memory computing. In addition, the APIs and the plug-in architecture are discussed.
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McCafferty, Conor. "Distributing Authorship at a Localised Scale: Developing public engagement in sound mapping." Organised Sound 26, no. 2 (August 2021): 255–64. http://dx.doi.org/10.1017/s1355771821000297.
Повний текст джерелаАнотація:
Sound maps, particularly the web-based examples that have proliferated since the early 2000s, have proven compelling and valuable as means of conveying diverse perspectives of urban, rural and wilderness sound environments, while opening the creative process of mapping through field recording to non-expert user groups. As such, sound maps hold the promise of broad public engagement with everyday sonic experience and spatial typologies. Yet this straightforward participatory aim is prone to complication in terms of participatory frameworks and scale of analysis. Drawing on a catalogue of sound maps by the author, this article problematises the participatory norms of sound mapping and, in tandem, calls for a more nuanced approach to scale than typically seen to date in sound maps based on geospatial mapping APIs. A sound mapping workshop in Lisbon with a multidisciplinary participant group provided the opportunity to ‘re-prototype’ sound maps at the scale of a local neighbourhood using multimodal means of representation; the results highlighted questions of form, scale, representation, authorship and purpose in sound mapping and demonstrated its continuing potential as a participatory practice.
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Xie, Qingyun. "THE DESIGN OF A HIGH PERFORMANCE EARTH IMAGERY AND RASTER DATA MANAGEMENT AND PROCESSING PLATFORM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 14, 2016): 551–55. http://dx.doi.org/10.5194/isprsarchives-xli-b4-551-2016.
Повний текст джерелаАнотація:
This paper summarizes the general requirements and specific characteristics of both geospatial raster database management system and raster data processing platform from a domain-specific perspective as well as from a computing point of view. It also discusses the need of tight integration between the database system and the processing system. These requirements resulted in Oracle Spatial GeoRaster, a global scale and high performance earth imagery and raster data management and processing platform. The rationale, design, implementation, and benefits of Oracle Spatial GeoRaster are described. Basically, as a database management system, GeoRaster defines an integrated raster data model, supports image compression, data manipulation, general and spatial indices, content and context based queries and updates, versioning, concurrency, security, replication, standby, backup and recovery, multitenancy, and ETL. It provides high scalability using computer and storage clustering. As a raster data processing platform, GeoRaster provides basic operations, image processing, raster analytics, and data distribution featuring high performance computing (HPC). Specifically, HPC features include locality computing, concurrent processing, parallel processing, and in-memory computing. In addition, the APIs and the plug-in architecture are discussed.
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Zafar, Noureen, Irfan Ul Haq, Jawad-ur-Rehman Chughtai, and Omair Shafiq. "Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas." Sensors 22, no. 9 (April 27, 2022): 3348. http://dx.doi.org/10.3390/s22093348.
Повний текст джерелаАнотація:
With the advent of the Internet of Things (IoT), it has become possible to have a variety of data sets generated through numerous types of sensors deployed across large urban areas, thus empowering the notion of smart cities. In smart cities, various types of sensors may fall into different administrative domains and may be accessible through exposed Application Program Interfaces (APIs). In such setups, for traffic prediction in Intelligent Transport Systems (ITS), one of the major prerequisites is the integration of heterogeneous data sources within a preprocessing data pipeline resulting into hybrid feature space. In this paper, we first present a comprehensive algorithm to integrate heterogeneous data obtained from sensors, services, and exogenous data sources into a hybrid spatial–temporal feature space. Following a rigorous exploratory data analysis, we apply a variety of deep learning algorithms specialized for time series geospatial data and perform a comparative analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrid combinations. The hybrid LSTM–GRU model outperforms the rest with Root Mean Squared Error (RMSE) of 4.5 and Mean Absolute Percentage Error (MAPE) of 6.67%.
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Koukofikis, A., V. Coors, and R. Gutbell. "INTEROPERABLE VISUALIZATION OF 3D CITY MODELS USING OGC’S STANDARD 3D PORTRAYAL SERVICE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 113–18. http://dx.doi.org/10.5194/isprs-annals-iv-4-113-2018.
Повний текст джерелаАнотація:
<p><strong>Abstract.</strong> The demand of serving large 3D spatial data, mainly of urban areas, reflects the need of hierarchical data structures for 3D data. During the last years the OGC community standard I3S (Indexed 3d Scene Layer, ESRI) and 3D Tiles (Analytical Graphics, Inc.) emerged in order to deal with this challenge. Conceptually, hierarchical structures for 3D data operate similarly to web map tiles, differing only in the implementation. Although 3D hierarchical formats can transmit arbitrary sized geospatial data, they are not interoperable with consuming/visualization technologies on the client. A series of prototype implementations focus on rendering of hierarchical organized massive 3D data in various web client technologies employing the 3D Portrayal Service. As a result, the user can query a scene via the 3D Portrayal Service by specifying a spatial region, rather than a specific resource via a URI. The result is delivered either using I3S or 3D Tiles as a data delivery format, depending on which data is available for the specified region. The client APIs are capable of rendering either the I3S or the 3D Tiles content.</p>
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Batita, Wided. "GeoWeb 2.0 applications could support the Geodesign process." Revue Internationale de Géomatique 29, no. 3-4 (July 2019): 263–85. http://dx.doi.org/10.3166/rig.2019.00092.
Повний текст джерелаАнотація:
The emergence of Web 2.0 is materialized by new technologies (APIs, Ajax, etc.), by new practices (mashup, geotagging, etc.) an, by new tools (wiki, blog, etc.). It is primarily based on the principle of participation and collaboration. In this dynamic, the web mapping with spatial character or simply called Geospatial Web (or Geoweb) evolves by strong technological and social changes. Participatory GeoWeb 2.0 is materialized in particular by mashups among wikis and géobrowsers (ArgooMap, Geowiki, WikiMapia, etc.). The new applications resulting from these mashups are moving towards more interactive forms of collective intelligence. The Geodesign is a new area, which is the coupling between GIS and design, allowing a multidisciplinary team to work together. As it is an emergent term, the Geodesign has not be well defined and it requires innovative theoretical basis, new tools, media, technologies and practices to fit its complex requirements. In this document, we propose some GeoWeb 2.0 tools and technologies that could support the Geodesign process. The main contributions of the present research are firstly identifying the needs, requirements and constraints of Geodesign process as an emergent fuzzy field, and secondly offering new supports that are best meeting to the collaborative dimension of this process.
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Ajao, Adeyemi Mufutau, Gabriel Salako, Segun Olayinka Oladipo, and Abdulrasheed Abidemi Adio. "Application of Geo-spatial Technology in Characterization of African Honeybee (Apis mellifera) Apiaries into Ecosystem Zones for Productivity and Pollination Service." European Journal of Agriculture and Food Sciences 3, no. 1 (January 18, 2021): 61–67. http://dx.doi.org/10.24018/ejfood.2021.3.1.221.
Повний текст джерелаАнотація:
Seven major ecological zones of Nigeria with modern functional apiaries were mapped and sampled in the rainy season between April 2016 and October 2018 for estimating the bee foraging flight range and floral species diversity; collating satellite imagery data for geospatial zone mapping using GPS (Garmin Etrex 20) to capture the coordinates of apiaries locations and their attributes, climatic and topographic data and generating ecological zones: the wetland vegetation, savanna grassland, savanna woodland (disturbed), shrubby forest, lowland dense forest, and Highland rain forest. Honeybee flight ranges between 1 and 5 kilometer radius from where bees at the apiaries can forage for pollen and nectar. Igbeti/Malete/Buari bees forages at similar ecosystems within this range while those at Saki and Abuja apiaries have potentials to forage at different ecological zones ranging from woodland savanna to lowland and high forests. Abuja (woodland savanna and shrubby forest zones) apiaries produces average of 32 liters of honey per hive and Kwara (savanna grassland and wood land Savanna) produces average of 24 liters of honey per hive. There is also structural variation in the floral species diversity and richness as richness marginally differed at the locations. Highest index was observed in Orozo (H=2.62; Margalef = 3.05) and Agbungburu (H=2.54; Margalef = 3.06). Low index was attained at Buari (H= 2.10; Margalef= 2.32) and Igbeti (H= 2.11; Margalef= 2.62). Apiary managers and farmers acquiring relevant knowledge on apiculture and prevailing A. mellifera apiaries would assist management activities; contribute to better production and pollination for food security.
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Durante, Kimberly, and Emily Prince. "Visualizing and Linking Cartographic Resources across Discovery Environments." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-69-2019.
Повний текст джерелаАнотація:
<p><strong>Abstract.</strong> This presentation will highlight Stanford University Libraries’ development of institutional best practices for identifying relationships among cartographic resources stored within the university’s digital preservation repository and made available to end users from contextualized discovery catalogs: SearchWorks and EarthWorks. The use of map interfaces as a tool for searching and retrieving of geographic information has greatly influenced the design of structural metadata and the related management of elegant workflows for supporting consistent procedures, both amongst the cartographic metadata librarians, as well as across greater Stanford Library policies.</p><p> As collections of maps, atlases, and geospatial imagery are increasingly being digitized and georeferenced for use in a number of research contexts and purposes, new digital data are produced and made available through spatial data infrastructures, geoportals, and programmatic APIs. Our goal is to preserve the lineage that exists between primary source materials - such as centuries-old maps - and their derivative or versioned datasets, and to make this track both transparent and easily actionable for end users who are evaluating materials found through search engines or contextualized discovery platforms. The goal is to provide users with easily navigable links between the source materials and all subsequent versions.</p><p> Our work relies on the use of structured metadata to define relationship types that commonly exist between cartographic materials old and new, and includes the use of persistent links that direct users to both source materials, as well as to georectified imagery, or shapefile index maps that are based upon those source materials. Using examples of Japanese maps of Tokyo, from several of Stanford’s rare and historic cartographic collections (housed at the East Asia Library, David Rumsey Map Center, and Branner Earth Sciences Library), we will present a standardized list of relationships which are prevalent among cartographic data collections and we will demonstrate how we have designed and leveraged metadata in these cases in order to facilitate the user’s intuitive discovery, reuse, and citation of related map content.</p>
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Hobern, Donald, Andrea Hahn, and Tim Robertson. "Options to streamline and enrich biodiversity data aggregation." Biodiversity Information Science and Standards 2 (May 21, 2018): e26808. http://dx.doi.org/10.3897/biss.2.26808.
Повний текст джерелаАнотація:
The success of Darwin Core and ABCD Schema as flexible standards for sharing specimen data and species occurrence records has enabled GBIF to aggregate around one billion data records. At the same time, other thematic, national or regional aggregators have developed a wide range of other data indexes and portals, many of which enrich the data by interpreting and normalising elements not currently handled by GBIF or by linking other data from geospatial layers, trait databases, etc. Unfortunately, although each of these aggregators has specific strengths and supports particular audiences, this diversification produces many weaknesses and deficiencies for data publishers and for data users, including: incomplete and inconsistent inclusion of relevant datasets; proliferation of record identifiers; inconsistent and bespoke workflows to interpret and standardise data; absence of any shared basis for linked open data and annotations; divergent data formats and APIs; lack of clarity around provenance and impact; etc. The time is ripe for the global community to review these processes. From a technical standpoint, it would be feasible to develop a shared, integrated pipeline which harvested, validated and normalised all relevant biodiversity data records on behalf of all stakeholders. Such a system could build on TDWG expertise to standardise data checks and all stages in data transformation. It could incorporate a modular structure that allowed thematic, national or regional networks to generate additional data elements appropriate to the needs of their users, but for all of these elements to remain part of a single record with a single identifier, facilitating a much more rigorous approach to linked open data. Most of the other issues we currently face around fitness-for-use, predictability and repeatability, transparency and provenance could be supported much more readily under such a model. The key challenges that would need to be overcome would be around social factors, particularly to deliver a flexible and appropriate governance model and to allow research networks, national agencies, etc. to embed modular components within a shared workflow. Given the urgent need to improve data management to support Essential Biodiversity Variables and to deliver an effective global virtual natural history collection, we should review these challenges and seek to establish a data management and aggregation architecture that will support us for the coming decades.
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Tengtrairat, Naruephorn, Wai Lok Woo, Phetcharat Parathai, Chuchoke Aryupong, Peerapong Jitsangiam, and Damrongsak Rinchumphu. "Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models." Sensors 21, no. 13 (July 5, 2021): 4620. http://dx.doi.org/10.3390/s21134620.
Повний текст джерелаАнотація:
Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations—Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool.
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Alzubaidi, Ahmad N., Stephen Sekoulopoulos, Jonathan Pham, Vonn Walter, Jay G. Fuletra, and Jay D. Raman. "Incidence and Distribution of New Renal Cell Carcinoma Cases." Journal of Kidney Cancer and VHL 9, no. 2 (April 18, 2022): 7–12. http://dx.doi.org/10.15586/jkcvhl.v9i2.219.
Повний текст джерелаАнотація:
Nationwide databases have implicated an increased incidence of renal cell carcinoma (RCC). The Pennsylvania (PA) Cancer Registry was queried to better define incidence, geographic distribution, and statewide trends of new RCC cases over a 27-year period. JoinPoint Trend Analysis Software modeled average annual percent changes (APCs) in age-adjusted rates (AAR). Maps plotting county-level incidence rates and stage distribution of disease across the state in 5-year time intervals were created using R 4.0.2 software. Overall, 59,628 cases of RCC were recorded in PA from 1990 to 2017. Eighty six percent of patients were >50 years of age, 61% were males, and 89% were Caucasian. Stage distribution using the SEER staging system included 64% local, 17% regional, and 16% distant. Over the study interval, AAR of all RCC cases increased from 9.9 to 18.0 patients per 100,000 population with an APC of 2.3% (p < 0.01). AAR of local disease increased from 5.4 to 12.7 patients per 100,000 population with an APC of 3.2% (p < 0.01). AAR of regional disease also increased from 1.9 to 2.9 patients per 100,000 population with an APC of 1.0% (p = 0.01). Younger patients (<50 years) had a greater rate of increase than older counterparts (APC 3.8% vs. 2.0%, p < 0.05). Geospatial investigation of new RCC cases noted certain geographic concentrations of greater disease incidence. The incidence of RCC in PA has increased over the past 27 years in PA. One-third of the cases are regional or metastatic at presentation and rates of increase were most notable in younger patients.
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Roggeri, Paolo, Alan Belward, Philippe Mayaux, Hugh Eva, Andreas Brink, Grégoire Dubois, Stephen Peedell, and Olivier Leo. "SUSTAINABLE DEVELOPMENT IN DEVELOPING COUNTRIES: THE AFRICAN, CARIBBEAN AND PACIFIC OBSERVATORY /DARNUS VYSTYMASIS BESIVYSTANČIOSE ŠALYSE: AFRIKOS, KARIBŲ IR RAMIOJO VANDENYNO VALSTYBIŲ APŽVALGA." Technological and Economic Development of Economy 16, no. 4 (December 31, 2010): 736–52. http://dx.doi.org/10.3846/tede.2010.45.
Повний текст джерелаАнотація:
Freshwater, aquaculture, fisheries, biodiversity, forests, and agricultural land have high economic and social value throughout the Africa, Caribbean and Pacific (ACP) region; but they can also be over‐exploited, with damaging consequences for local economies, long term stability and for the Earth system as a whole – especially the climate system. The ACP's fast growing population puts growing pressure on the environment to provide food, water and fibre, on the regions’ urban centres and transport networks, and on energy sources. Information on the location, condition and evolution of resources is an important step towards sustainability, but unfortunately such information can be hard to get. Earth observing satellite technology combined with geographical information management can help fill the information gap. In this objective, and because of its unique position to support the implementation of advanced interoperable geospatial technologies, the Joint Research Centre (JRC) of the European Commission (EC) is setting‐up of an “Observatory for sustainable development” as single portal to support decision‐making for development in the fields of natural resource and food security. The African Union and European Union recognise the importance of this service and are beginning to develop this capacity as part of the AU EU joint strategic partnership. This paper describes the needs, and presents the first steps taken by the JRC and by the joint partnership in harnessing space technologies to help meet Millennium Development Goals, in particular eradication of poverty, and environmental sustainability. Santrauka Gėlasis vanduo, akvakultūra, žuvininkystė, biologinė įvairovė, miškų ir žemės ūkio paskirties žemė turi didėlę ekonominę ir socialinę vertę visoje Afrikoje, Karibų ir Ramiojo vandenyno (AKR) valstybių regione, bet šie veiksniai gali būti per daug eksploatuojami ir tureti žalingų padarinių vietinei ekonomikai, ilgalaikiam stabilumui ir visai Žemės sistemai, ypač klimato sistemai. Dėl greitai augančio AKR gyventojų skaičiaus regionų miestų centruose transporto tinklų ir energijos šaltinių vietose dideja aplinkos naudojimo mastas, siekiant gyventojus aprūpinti maistu, vandeniu ir lasteliena. Informacija apie vietoves išteklius, išteklių būklę ir raidą yra svarbus žingsnis siekiant darnos, bet, dėja, tokia informacija sunku surinkti. Žemės stebejimo palydovais technologija, sujungta su geografines informacijos valdymu, gali padėti užpildyti šios informacijos spragą. Dėl šio tikslo ir dėl unikalios padėties, siekiant diegti pažangias tarpusavyje saveikaujančias geoerdvines technologijas, Europos Komisijos (EK) jungtinis tyrimu centras (JTC) yra įkūręs Darnaus vystymosi observatorijas kaip viena portalą, kad palaikytų sprendimų priemimo plėtote gamtinių išteklių ir maisto saugumo srityse. Afrikos Sąjunga (AS) ir Europos Sąjunga (ES) pripažįsta šios paslaugos svarbą ir pradėjo plėtoti šiuos pajėgumus kaip dali strateginės AS ir ES partnerystės. Straipsnyje pristatomi pirmieji žingsniai, žengti AKR valstybių, plėtojant jungtinę partnerystę kosminių technologijų srityje, siekiant spręsti tūkstantmečio tikslus – mažinti skurdą ir tobulinti darnų aplinkos vystymą.
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Beconytė, Giedrė, та Dovid Katz. "ATLAS OF NORTHEASTERN YIDDISH: IMPORTANCE OF MAPS IN LINGUISTIC RESEARCH / ŠIAURĖS RYTŲ TERITORIJOS JIDIŠ ATLASAS: ŽEMĖLAPIŲ SVARBA ATLIEKANT LINGVISTINIUS TYRIMUS / АТЛАС СЕВЕРО-ВОСТОЧНОГО ИДИШ: РОЛЬ КАРТЫ В ЛИНГВИСТИЧЕСКИХ ИССЛЕДОВАНИЯХ". Geodesy and Cartography 37, № 3 (1 листопада 2011): 119–24. http://dx.doi.org/10.3846/13921541.2011.626257.
Повний текст джерелаАнотація:
The paper introduces the application of cartographic methods to research on a culture at the last moment of its in situ existence. The atlas in progress seeks to determine the historic external borders, the internal differentiation and the cultural and linguistic structure and characteristics of Líte ([lítə] – the territory of traditional jewish Lithuania (coterritorial with today's Lithuania, Latvia, Belarus, and swaths of northeastern Poland, northern and eastern Ukraine and westernmost Russia). The main linguistic data were initially organized by lists of locations where use of a particular form had been documented. Sparse information has been converted to a relational database model, linked to geographic data (locations) and analyzed. The discovered information was sufficient to approximately locate spatial clusters that were not thought to be recoverable when the project was initiated. The results of the geographic analysis are presented in the form of maps in the evolving draft of Litvish: An Atlas of Northeastern Yiddish that is accessible for preview at http://www.dovidkatz.net/WebAtlas/AtlasSamples.htm. The structure of the linguistic database also enables publication of the data as a web service representing the location of occurrences of linguistic forms on a larger scale map. However, the small scale linguistic maps represent characteristics of the dialect areas that are more convenient for readers who specialize in the relevant language and culture, but are not familiar with geospatial technologies. Santrauka Aptariamas ir iliustruojamas kartografinių ir erdvinės analizės metodų taikymas atliekant lingvistinius tyrimus. Lietuvos jidiš tarmių žemėlapiais siekiama atskleisti iš esmės jau išnykusios kultūros teritorinę įvairovę, istorines ribas, kultūrinius ir lingvistinius ypatumus. Tyrimų objektas yra tradicinė Lietuvos žydų teritorija Líte, apimanti didžiąją dalį dabartinės Lietuvos, Latvijos, Baltarusijos bei šiaurės rytų Lenkijos, šiaurės ir rytų Ukrainos, vakarinės Rusijos pakraščio teritorijas. Kuriamas atlasas apima per 30 žemėlapių, kuriuose pavaizduota dažniau pasitaikančių žodžių formų įvairovė ir teritorinė jų sklaida. žemėlapiai sudaryti naudojantis GIS duomenų baze, kurioje registruoti D. Kadz beveik 20 metų trukusių tyrimų Lietuvos žydų teritorijoje duomenys. Prielaidos apie Lietuvos jidiš dialektų paplitimą buvo tikrinamos erdvinės statistikos metodais. Nors duomenys nėra labai išsamūs ar vientisi, sudarytuose žemėlapiuose aiškiai matyti skirtingų lingvistinių formų paplitimo teritorijos, šių teritorijų homogeniškumas ar heterogeniškumas, jų kitimo laikui bėgant ypatumai. žemėlapiai sudaryti taip, kad būtų lengva pastebėti ir interpretuoti juose įžvelgiamus dėsningumus. Atlasą galima rasti internete: <http://www.dovidkatz.net/WebAtlas/AtlasSamples.htm>. Резюме В статье обсуждается применение картографических методов в лингвистических исследованиях территории северо-восточного (Литовского) идиш. На составленных авторами картах представлено распределение характерных форм идиш на территории современных Литвы, Латвии, Беларуси и соседних стран. Данные, собранные во время экспедиций по территории Литовского идиш, неоднородны и недостаточны для статистических исследований, тем не менее карты позволяют выявить интересные пространственные закономерности и глубже познать культуру на грани исчезновения. Карты составляют атлас, с которым можно познакомиться на сайте: http://www.dovidkatz.net/WebAtlas/AtlasSamples.htm.
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Rashidan, Muhammad Hanis, and Ivin Amri Musliman. "GeoPackage as Future Ubiquitous GIS Data Format: A Review." Jurnal Teknologi 73, no. 5 (March 31, 2015). http://dx.doi.org/10.11113/jt.v73.4319.
Повний текст джерелаАнотація:
The emerging geospatial technologies in earth and space science informatics have led to the advancement in developing international standards for geospatial interoperability. In the last few years, two main trends are making disruptions in geospatial applications; mobile and context sharing. Geospatial data format used in mobile GIS to support advance mobile application is challenged. This is due to the lack of interoperability, open-standard, cross platform and standard APIs for access and management. For instance, most mobile GIS developments are application-dependent, contains redundant geospatial data, consume large storage capacity, and require custom applications for data translation. Based on these issues, new OGC file format named GeoPackage will enable greater geospatial data sharing on mobile and web platform. This data format is an open standard, non-proprietary, platform-independent, container for distribution, and direct use of all kinds of geospatial data will increase cross-platform interoperability, geospatial applications and web services. This presents a comprehensive review of mobile GIS hence, the concept of GeoPackage as a modern geospatial tool was discussed, while its relevance in contemporary geospatial technology are highlighted.
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Vinhas, Lubia, Gilberto Ribeiro De Queiroz, Karine Reis Ferreira, and Gilberto Camara. "WEB SERVICES FOR BIG EARTH OBSERVATION DATA." Revista Brasileira de Cartografia 69, no. 5 (May 13, 2017). http://dx.doi.org/10.14393/rbcv69n5-44004.
Повний текст джерелаАнотація:
The aim of geospatial web services is to enable users to share and process geospatial data on the Web, no matter what platform or protocol is used. In this paper, we investigate what the design decisions needed to implement web services for big Earth observation (EO) data are. The focus of the work is discussing what is unique about big EO data and why current standards are unsuitable for big EO data analytics. Instead, simpler APIs can be more eff ective for accessing big EO data than generic services such as WMS and WCS, especially for data analytics. We support this viewpoint by describing the WTSS - Web Time Series Service - that off ers time series of remote sensing data using a simple API and is suited for big data analytics.