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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).