Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure

Geospatial User Feedback: how to raise users’ voice and collectively build knowledge at the same time

Geospatial data is used not only to contemplate reality but also, in combination with analytical tools, to generate new information that requires interpretation. In this process data users gain knowledge about the data and its limitations (the user side of data quality) as well as knowledge on the status and evolutions of the studied phenomena. Knowledge can be annotations on top of the data, responses to questions, a careful description of the processes applied, a piece of software code or scripts applied to the data, usage reports or a complete scientific paper. This paper proposes an extension of the current Open Geospatial Consortium standard for Geospatial User Feedback to include the required knowledge elements, and a practical implementation. The system can incrementally collect, store, and communicate knowledge elements created by users of the data and keep them linked to the original data by means of permanent data identifiers. The system implements a Web API to manage feedback items as a frontend to a database. The paper demonstrates how a JavaScript widget accessing this API as a client can be easily integrated into existing data catalogues, such as the ECOPotential web service or the GEOEssential data catalogue, to collectively collect and share knowledge

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

Education on the Gis Frontier: Cybergis and Its Components

Geographic information systems (GIS) are a fundamental information technology. Coupled with advancing developments in spatial analysis through geographic information science (GISci), the capabilities and applications of GIS and GISci continue to rapidly expand. This expansion requires practitioners to have new skills and competencies, especially in computer science and programming. One developing framework for GIS’ future is that of Cyber Geographic Information Systems (CyberGIS), which fuses the technical capabilities of advanced cyber-infrastructure, like cloud and server computing, with the spatial analysis capabilities of GIS. This structure of GIS requires further computer science and programming abilities, but how GIS practitioners use and value the variant components within CyberGIS is unknown. This gap makes teaching and preparing students on the CyberGIS frontier difficult. The GIS skillset is in an ever-present state of re-imagination, but with the growing prominence of CyberGIS, which seeks to capitalize on advanced computing to benefit analysis in GIS, the need for an understanding of educational implications continues to grow. This dissertation uses a mixed-methods approach to explore how CyberGIS functions academically. First, I explore how university geography departments in the U.S. integrate computer science and programming skills in their undergraduate geography and GIS degree programs by reviewing degree requirements in highly-ranked departments. Few departments require computer science or programming courses for undergraduate degrees. Then, I explore the nature of knowledge and skills in CyberGIS using machine reading and q- methodology to explore viewpoints of how key CyberGIS skills function. The three viewpoints I identify reveal highly conflicting mindsets of how GIS functions. Finally, I use syllabi from different GIS programming and computer science courses to identify common topics, course structures, and instructional materials across a broad sample of courses. Three major topic foci emerged, including GIS scripting with Python, web-enabling GIS with JavaScript and HTML, and geodatabase manipulation with SQL. Some common instructional materials exist, but syllabi show little consistency in their curriculum focus and instructional design within or across topics relating GIS programming and computer science. There is little consistency or emphasis in current educational efforts concerning computer science and programming and how they function in building competencies required in CyberGIS. While CyberGIS promises advanced computing capabilities using complex systems, the fractured and uneven nature of basic computer science and programming instruction in GIS indicates that to achieve a Cyber-enabled GIS future, a much larger chasm between GIS and computer science must be bridged