E‐ARK Dissemination Information Package (DIP) Final Specification

The primary aim of this report is to present the final version of the E-ARK Dissemination Information Package (DIP) formats. The secondary aim is to describe the access scenarios in which these DIP formats will be rendered for use

Spatiotemporal data as the foundation of an archaeological stratigraphy extraction and management system

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Modifications To Web Processing Service Standard For Client-Side Geoprocessing

Nowadays we see the rapid growth of solutions number for geospatial data processing in the Web (i.e. geoprocessing). One of the main trends of Web geotechnologies evolution is the transition from Web map applications to the Web GIS applications, which are supplement the maps delivery with the analytic tools providing to the end user through Web interface. In fact, the only general open standard describes implementation rules for Web geoprocessing services. This is the Open Geospatial Consortium Web Processing Service standard, which is fully server-oriented. Moreover, the vast majority of currently used solutions (both open source and proprietary) are server-oriented, i.e. assume the server resources only as the computational resource. However, some researchers underline that it is possible way to transmit the executable code to the client for client-side computations and geoprocessing. Also, some general Web architecture concepts assume the effectiveness of client-side computations, e.g. Fog Computing concept. Our practical experience also shows that in some cases it is useful to have ability of client-side geoprocessing, which is not opposite but complement technology to the server-side processing technologies. In addition, we believe that it is more useful to have the ability to run the same processing tool by choice on server or client side. We name such double-sided services as Hybrid Geoprocessing Web Services. We study and discuss the approaches to gap filling in client-side geoprocessing general schema. For this purpose, we implemented previously the getProcess request as addition to the WPS protocol. Additionally at the previous steps of our study, we proposed a possible structure of getProcess request and draft XML file structure for its response, which describes the list of executable resources and their dependencies. Currently we working on detailed methodology of processing tools implementation and testing. We use the Python programming language as primary development tool, because of its applicability to build both server- and client-side crossplatform processing tools using single core program code. We use Python also for implementation of needed infrastructure components, such as HGWS server that supports the getProcess request/response performing, and client-side Runtime Environment that provides executable code orchestration on the client. Achieved results need to be discussed widely and carefully. However, main conclusion of our current work is that client-side geoprocessing schema in general could be relatively simple and compatible backward with current standards. The HGWS concept is applicable when implementing client-side geoprocessing Web services in small-scale projects and could be the entering point for study of distributed geoprocessing systems implementation

Archival Information Package (AIP) Pilot Specification

This report presents the E-ARK AIP format specification as it will be used by the pilots (implementations in pilot organizations). The deliverable is a follow-up version of E-ARK deliverable D4.2. The report describes the structure, metadata, and physical container format of the E-ARK AIP, a container which is the result of converting an E-ARK Submission Information Package (SIP) into the E-ARK Archival Information Package (AIP). The conversion will be implemented in the Integrated Platform as part of the component earkweb

Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities

International audienceSocial media, particularly Twitter, is increasingly used to improve resilience during extreme weather events/emergency management situations, including floods: by communicating potential risks and their impacts, and informing agencies and responders. In this paper, we developed a prototype national-scale Twitter data mining pipeline for improved stakeholder situational awareness during flooding events across Great Britain, by retrieving relevant social geodata, grounded in environmental data sources (flood warnings and river levels). With potential users we identified and addressed three research questions to develop this application, whose components constitute a modular architecture for real-time dashboards. First, polling national flood warning and river level Web data sources to obtain at-risk locations. Secondly, real-time retrieval of geotagged tweets, proximate to at-risk areas. Thirdly, filtering flood-relevant tweets with natural language processing and machine learning libraries, using word embeddings of tweets. We demonstrated the national-scale social geodata pipeline using over 420,000 georeferenced tweets obtained between 20-29th June 2016. Highlights • Prototype real-time social geodata pipeline for flood events and demonstration dataset • National-scale flood warnings/river levels set 'at-risk areas' in Twitter API queries • Monitoring multiple locations (without keywords) retrieved current, geotagged tweets • Novel application of word embeddings in flooding context identified relevant tweets • Pipeline extracts tweets to visualise using open-source libraries (SciKit Learn/Gensim) Keywords Flood management; Twitter; volunteered geographic information; natural language processing; word embeddings; social geodata. Hardware required: Intel i3 or mid-performance PC with multicore processor and SSD main drive, 8Gb memory recommended. Software required: Python and library dependencies specified in Appendix A1.2.1, (viii) environment.yml Software availability: All source code can be found at GitHub public repositorie

Public Geospatial Data in Wisconsin: Information Access, Data Sharing, and the University

This research explores public geospatial data sharing in Wisconsin. The research is informed by literature on GIS and Society, Participatory GIS, Spatial Data Infrastructure, Information Justice, The Digital Divide, and Library and Information Science. Original research consists of a survey and follow up interview to public land information professionals in Wisconsin gauging their interest in a UW System-wide geographic information portal for distributing public spatial data to UW System users. The research finds that social and institutional rather than technical factors are major drivers of data-sharing activities in Wisconsin. However, technical aspects of geographic information are changing quickly with a move to more hosted services in the cloud. This research explores how this shift influences data-sharing, academic library GIS services, and university level education. While social and institutional influences are critical, GIS professionals, students, and educators must be ready for the cloud

LiDAR-Assisted Extraction of Old Growth Baldcypress Stands Along The Black River of North Carolina

The remnants of ancient baldcypress forests continue to grow across the Southeastern United States. These long lived trees are invaluable for biodiversity along riverine ecosystems, provide habitat to a myriad of animal species, and augment the proxy climate record for North America. While extensive logging of the areas along the Black River in North Carolina has mostly decimated ancient forests of many species including the baldcypress, conservation efforts from The Nature Conservancy and other partners are under way. In order to more efficiently find and study these enduring stands of baldcypress, some of which are estimated to be more than 1,000 years old, LiDAR remote sensing and geospatial analysis techniques can be employed. Promising results have been discovered correlating LiDAR-derived metrics and known stands of old growth baldcypress. A number of percentile height metrics and other composite metrics like canopy cover and density were extracted from LiDAR data collected across North Carolina. Along with the metrics, locations of known stands of old growth were used as training data for a supervised classification with the C5.0 decision tree algorithm. C5.0 was used to condense the patterns found across the training data into a set of rules that could then be applied to other areas within the study site or anywhere else across the LiDAR data. Both existing stands and new areas were selected by the machine learning rulesets indicating that the use of machine learning is valid to identify stands of ancient trees along the Black River. Overall C5.0 accuracies of approximately 98.5% (based on training data) and 88.6% (based on independent test data) were achieved. More than 8 km2 of predicted old growth forests, outside of available in situ reference areas, were also identified within the Black River site

Geospatial Web Services, Open Standards, and Advances in Interoperability: A Selected, Annotated Bibliography

This paper is designed to help GIS librarians and information specialists follow developments in the emerging field of geospatial Web services (GWS). When built using open standards, GWS permits users to dynamically access, exchange, deliver, and process geospatial data and products on the World Wide Web, no matter what platform or protocol is used. Standards/specifications pertaining to geospatial ontologies, geospatial Web services and interoperability are discussed in this bibliography. Finally, a selected, annotated list of bibliographic references by experts in the field is presented