Search

Dengue is a mosquito-borne pandemic viral infection, which transmits to humans from Female Aedes albopictis or Aedes agypti mosquitoes. It progressively deteriorates the health of infected individuals and poses a high threat of human morbidity and mortality. This paper proposes an intelligent healthcare system which identifies, monitors, and alerts dengue virus (DeV) infected individuals and other stakeholders in real-time and control the DeV infection outbreak using cloud computing, internet of things and fog computing paradigms. The proposed system uses Naive Bayesian Network (NBN) for diagnosing the possibly DeV infected individuals and generating real-time alerts for suggesting and alerting the concerned stakeholders for taking on-time necessary actions at the fog subsystem. The proposed system also uses Social Network Analysis at the cloud subsystem, to provide Global Positioning Systems (GPS)-based global risk assessment of the DeV infection on Google Maps (Google-based web map service) and control DeV infection outbreak. The analysis of the experimental results acknowledges the efficiency of the NBN-based DeV infection diagnosis, alert generation, and GPS-based risk assessment functionality, of the proposed system, via various statistical measures and experimental approaches.

Without explicit description of map application themes, it is difficult for users to discover desired map resources from massive online Web Map Services (WMS). However, metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples, mixed vocabularies, variable length and content arbitrariness of text fields. In this paper, we propose a novel multi-label text classification method, Text GCN-SW-KNN, based on geographic semantics and collaborative training to improve classification accuracy. The semi-supervised collaborative training adopts two base models, i.e. a modified Text Graph Convolutional Network (Text GCN) by utilizing Semantic Web, named Text GCN-SW, and widely-used Multi-Label K-Nearest Neighbor (ML-KNN). Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text. The distances are calculated with the Semantic Web of Earth and Environmental Terminology (SWEET) and WordNet dictionaries. Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines, and demonstrate better stability in repeating experiments and robustness to less training data. Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.

Massive maps have been shared as Web Map Service (WMS) from various providers, which could be used to facilitate people's daily lives and support space analysis and management. The theme classification of maps could help users efficiently find maps and support theme-related applications. Traditionally, metadata is usually used in analyzing maps content, few papers use maps, especially legends. In fact, people usually considers metadata, maps and legends together to understand what maps tell, however, no study has tried to exploit how to combine them. This paper proposes a method to fuse them with the purpose of classifying map themes, named latent feature based multimodality fusion for theme classification (LFMF-TC). Firstly, a multimodal dataset is created that supports the supervised classification on map themes. Secondly, textual and visual features are designed for metadata, maps, and legends using some advanced techniques. Thirdly, a latent feature based fusion method is proposed to fuse the multimodal features on the feature level. Finally, a neural network classifier is implemented using supervised learning on the multimodal dataset. In addition, a web-based collaboration platform is developed to facilitate users in labeling multimodal samples through an interactive Graphical User Interface (GUI). Extensive experiments are designed and implemented, whose results prove that LFMF-TC could significantly improve the classification accuracy. In theory, the LFMF-TC could be used for other applications with few modifications.

The use of vegetation indices to highlight archaeological features from remote sensing (RS) sensors is increasingly in demand, especially due to the possibility of obtaining high-resolution multispectral images with drones. The objective of this work is divided in two steps: i) to test techniques for the semi-automatic identification of crop marks in a study zone, in Salvada, Portugal, and; ii) to develop a Geographical Information Systems (GIS) open-source application, named ArchMarks, under QGIS software, to enhance the identification of archaeological crop marks using RS data/techniques, integrating the automatic creation of indices and the identification techniques tested before. ArchMarks aims to compute several vegetation and soil indices from multispectral imagery considering four bands (Red, Green and Blue and Near InfraRed (NIR)) for enhancing the identification of archaeological crop marks. In order to define the best approach to implement in the plugin, some tests were performed with two algorithms available in QGIS: ContrastEnhancement and WatershedSegmentation. High-resolution aerial imagery (25-50 cm spatial resolution) was obtained from Web Map Service (WMS) services in addition to Sentinel-2 satellite image, whose spatial resolution is of 10 meters in the Red Green Blue-Near InfraRed bands. The application is free and can be adapted to other region of interest. In the future, the plugin will be improved with a methodology to download remote sensing data (aerial images and satellite data) from WMS sources, band stacking, and machine learning algorithms, such as Support Vector Machine (SVM) algorithm, to automatically classify archaeological traces on the vegetation or bare soil.

Load-balancing strategies address challenges stemming from intensive access and heavy communication traffic on a Web map service platform (WMSP) by collecting workload-related information from cluster-based servers, and distributing tasks to minimize the consumption of computational and caching resources. However, intensive user access has time series patterns that create temporal periodic variations that can be exploited to improve the performance of the WMSP. In this paper, we propose a variable feedback strategy based on time series variations in the intensity of user access to increase the efficiency and reliability of workload feedback with little resource consumption. A task distribution strategy, based on the expected values of the arrival rate that match real-time conditions in workload feedback periods and the real-time processing capability of each cluster-based server, is devised simply and accurately by an association strategy for workload and service rate that supports services insensitive to massive numbers of concurrent access requests. The results of experiments show that the proposed strategy provides quick responses and high throughput for large-scale user access. It implements efficient load balancing for service resource utilization, and can thus improve the stability and capacity of the WMSP server using heterogeneous back-end cloud cluster-based servers.

To facilitate the discovery and use of geographic information, it is necessary to design an effective retrieval strategy to locate the map layers that customers want from massive WMS resources. Existing text-based WMS retrieval strategies are unable to solve the problems of metadata loss and inconsistency between pictures and metadata text, without considering map content. The visual similarity between maps is used to design a WMS layer retrieval method that combines multi-feature multiple kernel learning and user feedback to help users search for desired WMS layers. Color, shape and texture features are fused by multiple kernel learning to classify and rank layers according to similarity. A feedback mechanism is also established in the retrieval strategy, which is an effective guarantee that improves accuracy by collecting user-marked layers. Various kinds of WMS layers are selected to calculate the precision ration, analyze the time cost, and validate the retrieval feedback mechanism. The experimental results of selected WMS layers verified that the proposed algorithm is fast and highly precise. It can be integrated with existing text-based retrieval and discovery portals of geographic information.

Increase in access to mobile phone devices and social media networks has changed the way people report and respond to disasters. Community-driven initiatives such as Stand By Task Force (SBTF) or GISCorps have shown great potential by crowdsourcing the acquisition, analysis, and geolocation of social media data for disaster responders. These initiatives face two main challenges: (1) most of social media content such as photos and videos are not geolocated, thus preventing the information to be used by emergency responders, and (2) they lack tools to manage volunteers contributions and aggregate them in order to ensure high quality and reliable results. This paper illustrates the use of a crowdsourcing platform that combines automatic methods for gathering information from social media and crowdsourcing techniques, in order to manage and aggregate volunteers contributions. High precision geolocation is achieved by combining data mining techniques for estimating the location of photos and videos from social media, and crowdsourcing for the validation and/or improvement of the estimated location. The evaluation of the proposed approach is carried out using data related to the Amatrice Earthquake in 2016, coming from Flickr, Twitter and Youtube. A common data set is analyzed and geolocated by both the volunteers using the proposed platform and a group of experts. Data quality and data reliability is assessed by comparing volunteers versus experts results. Final results are shown in a web map service providing a global view of the information social media provided about the Amatrice Earthquake event., International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W8, ISSN:1682-1750, ISSN:2194-9034, ISSN:1682-1777

Disasters such as floods, large fires, landslides, avalanches, or forest fires are often inevitable and cannot be fully prevented, but their impact can be minimized with sound disaster management strategies aided by the latest technological advancements. A key factor affecting these strategies is the time, because any delay can result in dramatic consequences and potentially human losses. Therefore, a quick geo-situation report of the disaster is highly demanded, but still not an easy task because – in most cases – a priori known spatial information like map data or geodatabases, are outdated, and anyway won’t provide an overview on the current situation. This paper provides an exploratory investigation to be smart in providing correct and timely geodata that can help in emergency cases; especially in support decision making in emergency and risk management. In particular, issues related to geodatabase design and visualization of a variety of geodata available play a key role when it comes to efficient data deployment and usability. To this end, a significant part of this research will be devoted to develop a concept for a geodatabase design and dataset management that helps assessing a disaster risk through a potential provision of data needed. Based on this consideration, the proposed concept is to create multi-disciplinary integrated geodatabases as well as an easy-to-use graphical user interface to access the obtained data. To address this concept, hard- and software solutions are being developed through the joint research project ANKommEn and its extension ANKommEn2. In those projects two automated unmanned systems, that is an aerial UAV (Unmanned Aerial Vehicle) and a ground based UGV (Unmanned Ground Vehicle), are being developed to provide up-to-date information of rescue scenarios. Within this paper, highlights about the two project parts will be briefly presented, and then the current state of the art in geospatial database management, followed by focusing on Postgres-based database management connected with QGIS, and finally current results like a Web Map Service will be discussed.

Selection of construction material sources is an important issue in construction supply chain management. This affects not only the shipping cost and schedule of a construction project, but also its environmental impact. However, material delivery cost and time from the source location to the job site concerning sustainability may not be easily estimated. In this paper, a decision support tool that integrates building information modeling (BIM) and web map service (WMS) for the source selection of sustainable construction materials is presented. The BIM-WMS integrated decision support tool is developed in a form of a BIM-integrated plugin, which can help select sustainable construction material sources through evaluating the final cost and delivery time of the material, as well as calculate location-related credits in some green building standards, such as LEED. The developed plugin will make the selection of sustainable construction material and the calculation of location-related credits in green building certification an easy task, with the goal of minimizing environmental impact.

The paper presents the designing and development of the Atlas Information System (AIS) of hazardous hydrometeorological events (HHME) in the Ural Prikamye region. AIS integrates classic mapping approaches and modern web mapping service technologies. AIS information basis includes several data sources, both ground-based and satellite observations. The basic techniques and algorithms for mapping of HHME spatiotemporal distribution and the main AIS content are briefly described. Examples are provided of periodicity and intensity mapping of various HHME types, as well as zoning results of the study area in terms of prevailing types of hazardous weather events. The AIS content functionality is considered, including various techniques of data visualization, connection to external databases, search and query generation, building of cartograms and structural diagrams. AIS is published in open access as cartographic web map service (http://ais.maps.psu.ru). The main result of the project for the Perm region is the development of the unique reference and information cartographic resource on the current climatic conditions of the region, its typical HHME, their spatiotemporal distribution, as well as HHME-related damage.

This paper presents a mobile application and a web-based geographic information system for an efficient and cost-effective sharing of geological hazards information in East and Southeast Asia. The information system uses the Open Geospatial Consortium (OGC), Web Map Service (WMS) and Web Processing Service (WPS) for spatial data rendition and processing online. Free and Open Source Software (FOSS) is also used for other important spatial data processing and sharing operations. WMS and WPS are formulated to access and view the geological hazard data in the system’s database. The mobile application is developed to access and share the system’s geospatial contents by sending requests to these web services. The application also makes geological hazard mitigation measures more effective by incorporating the mobile device’s GPS location information with geological hazard data. Information like the distance from the nearest active fault, active volcano and earthquake hypocenter can be easily determined using the application. The mobile application provides an interface for the users to easily access geological hazard information such as active fault, active volcanoes, shorelines inundated by tsunamis, and historical and real time seismic events. The widespread use of mobile devices with Internet connection makes geological hazard information dissemination very efficient using the application. The mobile application is called G-EVER Mobile. G-EVER stands for Asia Pacific Region Global Earthquake and Volcanic Eruption Risk Management consortium. The application can be accessed at https://ccop-geoinfo.org/gever-mo/index.php.

This paper presents an innovative, cost-effective and efficient web-based sharing system for geoscience information among the countries in East and Southeast Asia. The information system’s main components are organized following the Spatial Data Infrastructure (SDI) model. It also uses the Open Geospatial Consortium (OGC) Web Map Service (WMS) and Web Processing Service (WPS) for online spatial data rendition and processing, respectively. Free and Open Source Software (FOSS) are also used for other important spatial data processing functions. The geospatial contents of the information system are stored and managed using the open source software PostgreSQL and its PostGIS extension. The information system makes spatial data sharing more efficient and organized by formulating a hierarchical information content classification scheme and the option for the users to put up their own customized WebGIS portals. These give users the option to show their chosen geospatial contents on their own web-based platform. The information system also makes spatial data sharing more flexible by providing data owners the choice to determine the data access privileges for each user or group of users. It also provides module for processing, rendering and sharing Japan’s ASTER satellite images. The information system is called GSi for Geoinformation Sharing Infrastructure for East and Southeast Asia. The URL of the web-based information system is https://ccop-gsi.org/main/index.html.

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system.

Augmented Reality (AR) is the technology that enables us to combine digital knowledge into the user's realworld space. Augmented Reality uses the existing environment to overlay digital content and information onto the physical world. The Proposed method demonstrates AR's use to provide a lively and visual representation of information about the geography of different masses on the globe and how AR can be utilized to enhance research studies and education by visualizing data collected by various statistical sources. This work helps to visualize the globe in Augmented Reality where one can overlay any OGC (Open Geospatial Consortium) Web Map Service (WMS) layer onto the globe's surface and interact with it to understand the visualization of statistics around the world. Users can utilize any pre-defined marker, target or placeholders of the object, which is trained beforehand to be recognized later in the camera stream. When seeing the placeholder from the camera of phone, an augmented reality globe must appear over the placeholder. Moving the phone camera, it have to be possible to overlay any Web Outline on the globe. This application must provide an interface to choose the data such as Climate, Sea Floor Temperature, Social Progress, World Literacy information to be overlaid on the globe. Real-time information changes according to the observer's movement as if that information truly exists in the real world.

Territorial planning is undergoing significant transformations that establish as working conditions the use of web-based geospatial technologies, the use of methods for sharing decisions and listening to citizens and the wide use of mechanisms to facilitate understanding due to geovisualization. Among these resources, the methods of planning by Geodesign stand out, which were adapted in the proposal of the web-based platform GISColab, elaborated with the purpose of favoring the shared planning by co-creation. The platform extensively applies the resources of Spatial Data Infrastructures (SDI’s) through protocols established by OGC (Open Geospatial Consortium) for consumption of information via WMS (Web Map Service) or WFS (Web Feature Service), allowing in both cases the increment in performances by WPS (Web Processing Service). The article discusses the differences in information consumption and illustrates them through two case studies: Trees for Metropolitan Regions, in which the accesses to data were built by WFS and support to decisions were based on WPS; and Geodesign at Participatory Budgeting where accesses were via WMS. The conclusions indicate the importance of public data being, in fact, of public access and consumption, demonstrating the positive impact of alignment with data exchange protocols. The GISColab platform, developed according to these principles, supports participatory planning, expressively communicative and in transformative-learning actions.

A hybrid and distributed geographical database is being developed which utilizes the database engines PostgreSQL and MongoDB for later implementation in Geoserver’s architecture. In the course of the study, it was necessary to establish whether there was difference between the response times of PostgreSQL and MongoDB per type of query and the use of geographical indexes, in order to appropriately select the Database Management System to be used by the Geoserver implemented Web Map Service. After a statistical analysis with 11 different types of query, the conclusion was that the type of query affects the response time of the Database Management Systems.

This article defines the land cover classes used in Meter-Scale Urban Land Cover (MULC), a unique, high resolution (one meter2 per pixel) land cover dataset developed for 30 US communities for the United States Environmental Protection Agency (US EPA) EnviroAtlas. MULC data categorize the landscape into these land cover classes: impervious surface, tree, grass-herbaceous, shrub, soil-barren, water, wetland and agriculture. MULC data are used to calculate approximately 100 EnviroAtlas metrics that serve as indicators of nature’s benefits (ecosystem goods and services). MULC, a dataset for which development is ongoing, is produced by multiple classification methods using aerial photo and LiDAR datasets. The mean overall fuzzy accuracy across the EnviroAtlas communities is 88% and mean Kappa coefficient is 0.84. MULC is available in EnviroAtlas via web browser, web map service (WMS) in the user’s geographic information system (GIS), and as downloadable data at EPA Environmental Data Gateway. Fact sheets and metadata for each MULC community are available through EnviroAtlas. Some MULC applications include mapping green and grey infrastructure, connecting land cover with socioeconomic/demographic variables, street tree planting, urban heat island analysis, mosquito habitat risk mapping and bikeway planning. This article provides practical guidance for using MULC effectively and developing similar high resolution (HR) land cover data.

The wide availability of multispectral satellite imagery through projects such as Landsat and Sentinel, combined with the introduction of deep learning in general and Convolutional Neural Networks (CNNs) in particular, has allowed for the rapid and effective analysis of multiple classes of problems pertaining to land coverage. Taking advantage of the two phenomena, we propose a machine learning model for the classification of land abandonment. We designed a Convolutional Neural Network architecture that outputs a classification probability for the presence of land abandonment in a given 15&ndash, 25 ha grid element by using multispectral imaging data obtained through Sentinel Hub. For both the training and validation of the model, we used imagery of the Ł&oacute, dź Voivodeship in central Poland. The main source of truth was a 2009 orthophoto study available from the WMS (Web Map Service) of the Geoportal site. The model achieved 0.855 auc (area under curve), 0.47 loss, and 0.78 accuracy for the test dataset. Using the classification results and the Getis&ndash, Ord Gi* statistic, we prepared a map of cold- and hotspots with individual areas that exceed 50 km2. This thresholded heatmap allowed for an analysis of contributing factors for both low and intense land abandonment, demonstrating that common trends are identifiable through the interpretation of the classification results of the chosen model. We additionally performed a comparative field study on two selected cold- and hotspots. The study, along with the high-accuracy results of the model&rsquo, s validation, confirms that CNN-type models are an effective tool for the automatic detection of land abandonment.

The paper reports the main technological characteristics and functionalities of a web-based Multi-criteria Spatial Decision Support System (MC-SDSS) implemented on free open source software for geospatial (FOSS4G) environment. The web-based MC-SDSS was designed to perform land suitability evaluation (LSE) for olive crops and applied to the territory of Calabria (Italy). From a technological point of view, the MC-SDSS was designed on a multi-tier architecture compliant with the Open Geospatial Consortium (OGC) services (WFS, web feature service; WCS, web coverage service, and WMS, web map service. Moreover, it was enabled to processes executable via OGC web processing services (WPSs). LSE of olive crops is provided following the analytical hierarchy process (AHP) based on the different judgements obtained from experts and aggregated through the weighted linear combination (WLC). With the aim to implement an effective planning tool, the WebGIS client was designed to manage three different type of users with different level of privileges: Guest, Expert and Decision Maker. The results are expressed as multi-attribute geospatial maps (land suitability maps) and as distribution graphs according to the envisaged land suitability classes (not suitable, low, medium, high and very high suitability).

The cadastral data, including land parcels, are the basic reference data for presenting various objects collected in spatial databases. Easy access to up-to-date records is a very important matter for the individuals and institutions using spatial data infrastructure. The primary objective of the study was to check the current accessibility of cadastral data as well as to verify how current and complete they are. The author started researching this topic in 2007, i.e. from the moment the Team for National Spatial Data Infrastructure developed documentation concerning the standard of publishing cadastral data with the use of the WMS. Since ten years, the author was monitoring the status of cadastral data publishing in various districts as well as participated in data publishing in many districts. In 2017, when only half of the districts published WMS services from cadastral data, the questions arise: why is it so and how to change this unfavourable status? As a result of the tests performed, it was found that the status of publishing cadastral data is still far from perfect. The quality of the offered web services varies and, unfortunately, many services offer poor performance; moreover, there are plenty services that do not operate at all.

Thumbnail can greatly increase the efficiency of browsing pictures,videos and other image resources and improve the user experience prominently. Map service is a kind of graphic resource coupling spatial information and representation scale,its crafting,retrieval and management will not function well without the support of thumbnail. Sophisticated designed thumbnails bring users vivid first impressions and help users make efficient exploration. On the contrast,coarse thumbnail cause negative emotion and discourage users to explore the map service positively. Inspired by video summarization,key position and key scale of web map service were proposed. Meanwhile,corresponding quantitative measures and an automatic algorithm were drawn up and implemented. With the help of this algorithm,poor visual quality,lack of map information and low automation of current thumbnails was solved successfully. Information entropy was used to determine areas richer in content and tran-scale similarity was calculated to judge at which scale the appearance of the map service has changed drastically,and finally a series of static pictures were extracted which can represent the content of the map service. Experimental results show that this method produced medium-sized,content-rich and well-representative thumbnails which effectively reflect the content and appearance of map service.

We present the design, development, and testing of a new software package for generating snow cover maps. Using a custom inverse distance weighting method, we combine volunteer snow reports, cross-country ski track reports and station measurements to fill cloud gaps in the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product. The method is demonstrated by producing a continuous daily time step snow probability map dataset for the Czech Republic region. For validation, we checked the ability of our method to reconstruct MODIS snow cover under cloud by simulating cloud cover datasets and comparing estimated snow cover to actual MODIS snow cover. The percent correctly classified indicator showed accuracy between 80 and 90% using this method. The software is available as an R package. The output data sets are published on the HydroShare website for download and through a web map service for re-use in third-party applications.

In green buildings design and construction, evaluating the sustainable effects of site location and transportation to the ecosystem and human life is a critical and difficult task. Works regarding these matters require experience, time, labor, and manual calculations. In recent years, many studies have been conducted to enhance the application of Building Information Modeling (BIM) in green building certifications. However, the application of BIM to site location and transportation analysis is usually considered impractical due to the lack of a powerful map application in present BIM products. The aim of this research is to develop a framework for the integration of BIM and Web Map Service (WMS) technologies for location and transportation analysis in green building certifications. Using Autodesk Revit API and Google Maps API as the development tools, this research converts the integration model into the BIM-integrated plugin in Autodesk Revit. The plugin is used to streamline the certification process of site location and transportation analysis in LEED (Leadership in Energy and Environmental Design), one of the most popular and globally recognized green building standards.

Problem: The power & electric outages cause business disruptions that result in economic impacts to cities. A web map service (WMS), as a RESTful API with URL, that makes real-time information of power outage and economic loss estimation accessible and available across the web would be valuable and important for decision makers in smart cities and smart energy utility. Objective: This paper presents a web-based system that is capable of creating real-time WMS. Our system enables geo-enabled applications (such as web mapping, mobile GIS app, operations dashboard) to access, query, and display a web map of real-time outage status and economic loss information associated with outages in the county scale. Method: Our method is based on the Internet of Things (IoT), web services, GIS web services, web mapping, and web GIS (Geographic Information Systems). To implement our system, we used esri ArcGIS for Server, SQL Server Spatial, Amazon Web Services, and C#.NET. The system architecture consists of autonomous computer program, spatial database, GIS Server, cloud computing platform, a WMS, web feature service (WFS). Our insight is in real-time by continuously 1) connecting to web sensors and collecting outage data, 2) connecting to web services for calculating economics loss associated with outages, 4) manipulating (modify, insert, delete) our spatial database over the Internet, 4) publishing a real-time WMS. Result: The system implementation resulted in a back-end server that includes /serves a real-time WMS. A data record includes: name of county, name of state that contains this county, total number of costumers located within the county, total number of costumers without electricity who are located into the county, and total economic losses (modeled as total jobs loss, total GDP loss, and total cost in $) caused by outages for the county. Findings: Real-time WMS created by our system can be used by applications to display and query a web map of electric outage status and their macroeconomic loss in real-time. They provide a decision support tool for disaster emergency management in the energy sector by aiding in prioritization of restoration operations and recovery phase in electric utilities in order to reduce the economic impact of outages. Our system provides an important platform for smart cities, real-time GIS, Internet on Things, map-based crowdsourcing applications, real-time intelligent systems, and for real-time monitoring and analytical applications.

The starting point for developing a new map was defined by the requirements of the “MeinGrün” project (can be translated as ‘my green’ project). The aim is the development of a mobile app, which enables routing to urban areas, including suggestions for attractive places for different activities. A specific map style was designed to communicate information related to green spaces effectively and to make users excited about the topic of the project.Among other reasons, we also want to apply our knowledge on vector tile generation and use it in teaching. For the demonstration of our research on generalization, it is beneficial to offer a web map service because such a service allows illustrating developed methods and results. Finally, the experience enables us to train students in the design and development of customized styles for zoomable web maps.The aim is to build a map service with several zoom levels up to small scales using data from OpenStreetMap and NaturalEarth. For better adaptivity, it is aimed to offer the map as vector and raster tiles with the same style. Besides, making high-resolution raster tiles available for retina displays is considered as well. The rationale is that everyone can choose the appropriate method of delivery for different contexts of application. These requirements result in some technological challenges: provide vector and raster tiles at the same time and style, keep the computation time for an update, and generation low. For providing and delivering up to date data, on-the-fly map generalization has been implemented, e.g., scale-dependent selection, simplification, and aggregation of various features.Figure 1 shows our result, a map in warm colors, and a reduced information load for serving a reference map. The visualization of roads with a simple line is unique, but this was also the intension of the applied research project: the production of something unique and easy rememberable for the “MeinGrün” project. The hill-shading uses raster tiles while all other map features work with vector tiles.The data transformation from the OpenStreetMap format to the spatial PostgreSQL/PostGIS database has been applied with IMPOSM, a well-documented and efficient tool. GDAL is used to import the NaturalEarth data and doing some processing steps. PostgreSQL and its spatial extension PostGIS serve in many web-mapping projects as a data store and offer a wide range of options for processing data. After specified queries are applied, the t_rex software generates the vector tiles from the database, which are then packed as MBtiles using mbutil. Finally, Tileserver GL is used to serve vector and raster tiles, including further necessary additions such as style, sprites, and glyphs.To sum up, we already have a working service, which is available on our server with an ongoing blog about the project. Still, it is a work on progress project that needs further research and development. For example, the integration of Wikipedia/Wikidata information for selecting place by their importance, transliteration of no-Latin place names for a world-wide coverage, and more and better labeling of features. Now the map extent is scale depended and covers at low zoom levels the whole world. In contrast, the higher zoom levels are only available in Dresden and Heidelberg to match the requirements of the “MeinGrün” project but will be extended in the future.

Recent advances in Open Source mapping software are opening up new possibilities for online visualization of satellite data. Bhuvan, Indian Geo-platform of ISRO, hosts multi-temporal, multi-sensor and multi-resolution satellite data over internet and is used for various remote sensing applications. Serving satellite images over internet with good quality and faster response is essential for better user experience and increases the use of satellite data online. Map rendering software is generally used for serving satellite data as web map tiles over internet. This software will process satellite images and generate fixed size tiles based on parameters like zoom level, projection and area of interest. In this study, we explore open source map tile rendering software Mapnik and MapServer. The overall implementation process for hosting satellite data as web map tiles is discussed in detail. Using these two software, satellite data is published with different re-sampling methods as map layers. The published map layers are compared for web tile quality and performance under different load conditions. We also discuss various possible methods for improving the performance of tile creation and rendering.

Spatial data could be sectoral data or city spatial planning data. Province government manage sectoral data, whereas local government manages city spatial planning. To management spatial data, local government can’t access sectoral data in the provincial government. It has happened because there is no procedure to integrate their data. Each region/city also has their different map version so, that allows overlapping. This is because each local government hasn’t reference in solving spatial, land, or development problems. Therefore a One Map Policy was formed, is becoming an important point that should be considered to be realized. This paper presents a new automatic WebGIS framework to integrate sectoral data and city spatial planning for support One Map Policy. This system using REST API Geoserver technology. This technology is carried out automatically with a web browser. In this case, Every local government can manage and access this system. In carrying out the process, each data must be in accordance with the Standard Operating Procedure (SOP) of the application system. Each local government will upload data using a web browser and the system will work automatically. After the data is successfully stored, the system also automatically processes the Geoserver until a layer is formed. The formed layer is ready to be displayed to WebGIS using Web Map Service. With REST API technology, it will help to manage the creation of a spatial and information system more efficient. The experiment result shows that this system can integrate the data between local government and provincial government. And it can use as a reference in the process of managing spatial data and information in the local government of East Java Province to support One Map Policy.

An antibiotic resistance is a serious problem on health and humanity. Our human society needs to understand the threat of antibiotic resistance. To geo-visualize the location, biotic information and spatial pattern of antibiotic resistance matters is an effective way for intuitive findings. We focused on the antibiotic resistance issue of Han-river in South Korea and analyzed their spread, as well as range of influence.We developed the geo-database of antibiotic resistance matters and represented spatial information based on WMS (Web Map Service) which is adjusted to OGC (Open Geospatial Consortium) specifications. We designed correlated data schema structure of antibiotic matters, degrees of resistance, water quality and location of the sampled points on the river. Our WMS system followed the open source architecture and Q-GIS to make geo-server. This system was organized server-client structure.As for antibiotic resistance map on our WMS system, we illustrated the infographics and related tables of many kinds of information about antibiotic resistance. The WMS system represented simple spatial information, temporal variation of antibiotic resistance and degrees of correlation between antibiotic resistance and human activities such as emissions of antibiotic resistance matters on hospital or pharmacy. We also considered domestic sewage discharged into river through sewage processing facilities.This system will be contributed to reducing social cost and economic loss caused by antibiotic resistance spreads. These web maps will provide users with correct information on threat of antibiotic resistance.

Worldwide, traffic congestion is increasingly recognized as a serious public health and environmental concern. Besides, traffic jams cause large economic damages for companies and cities. Many efforts have been made to mitigate these issues. Attempts to reduce the amount of traffic congestion strongly depend on the availability of traffic information in real time. Multiple providers of such data exist. However, there is no generally accepted source that provides accurate and publicly available live traffic information. The goal of this study is to evaluate real time traffic data offered by web map service providers. Therefore, we first identify the most prominent providers and evaluate their range of services. Further, we collect actual data traces and perform a thorough comparison of their scope and granularity. Finally, in a real world case study, we analyze the predicted travel duration for the selected providers. The results indicate not only that the range of services varies widely among traffic information providers but also the travel time predictions diverge.

The aim of this work is to facilitate access to geological data coming from distributed data sources and the information (metadata) related to these data. For that purpose, a spatial data infrastructure (SDI) prototype has been established which comprises a geoportal that provides access to a geological Catalogue through Catalogue Service for the Web (CSW) in order to get the metadata that describes the data as well as the available services as the WMS (Web Map Service) view and Web Feature Service (WFS) download services, which aims to be compliant with the Open Geospatial Consortium (OGC) standards rules. Indeed, the establishment of spatial data infrastructure that has been elaborated in accordance with internationally recognized standards allows the exploration and sharing of geological data information, and will also allow researchers and professionals to spend more time in the analysis and the discovery of these data. This work can be used as a model for other fields in geoscience such as geophysics and hydrology or other close fields.

There are several options how to configure Web Map Service using severalmap servers. GeoServer is one of most popular map servers nowadays.GeoServer is able to read data from several sources. Very popular datasource is ESRI Shapefile. It is well documented and most of softwarefor geodata processing is able to read and write data in this format.Another very popular data store is PostgreSQL/PostGIS object-relationaldatabase. Both data sources has advantages and disadvantages and userof GeoServer has to decide which one to use. The paper describescomparison of performance of GeoServer Web Map Service when readingdata from ESRI Shapefile or from PostgreSQL/PostGIS database.

This paper presents the methods and implementation processes of a WebGIS platform designed to publish the available land use and land cover maps of Europe at continental scale. The system is built completely on open source infrastructure and open standards. The proposed architecture is based on a server-client model having GeoServer as the map server, Leaflet as the client-side mapping library and the Bootstrap framework at the core of the front-end user interface. The web user interface is designed to have typical features of a desktop GIS (e.g. activate/deactivate layers and order layers by drag and drop actions) and to show specific information on the activated layers (e.g. legend and simplified metadata). Users have the possibility to change the base map from a given list of map providers (e.g. OpenStreetMap and Microsoft Bing) and to control the opacity of each layer to facilitate the comparison with both other land cover layers and the underlying base map. In addition, users can add to the platform any custom layer available through a Web Map Service (WMS) and activate the visualization of photos from popular photo sharing services. This last functionality is provided in order to have a visual assessment of the available land coverages based on other user-generated contents available on the Internet. It is supposed to be a first step towards a calibration/validation service that will be made available in the future.