Cloud computing is a base platform for the distribution of large volumes of data and high-performance image processing on the Web. Despite wide applications in Web-based services and their many benefits, geo-spatial applications based on cloud computing technology are still developing. Auto-scaling realizes automatic scalability, i.e., the scale-out and scale-in processing of virtual servers in a cloud computing environment. This study investigates the applicability of auto-scaling to geo-based image processing algorithms by comparing the performance of a single virtual server and multiple auto-scaled virtual servers under identical experimental conditions. In this study, the cloud computing environment is built with OpenStack, and four algorithms from the Orfeo toolbox are used for practical geo-based image processing experiments. The auto-scaling results from all experimental performance tests demonstrate applicable significance with respect to cloud utilization concerning response time. Auto-scaling contributes to the development of web-based satellite image application services using cloud-based technologies.

Abstract:Managing geo-based indoor content is important, because the components used to construct an urban environment are complex. Geospatial data are available worldwide, but services are tailored only to local features. As the accuracy of online maps increases, the buildings in a web-mapping service can be created exactly as they are, in terms of actual features and geometric properties, and can provide some information on indoor elements. Nevertheless, not many practical use cases exist, as the available scope and volume of indoor content are limited. In Korea's metropolitan areas, an indoor geospatial information management scheme was built to manage internal facility information for public and underground buildings on a three-dimensional (3D) basis and to provide online visualization services for users. Based on this enterprise system for public use of indoor 3D content, we conducted a case study with add-on features to manipulate and manage data by adding two-dimensional (2D) building data that are linked to the 3D models. We also changed the classification system of the points of interest (POIs) for each internal facility. To enhance public usability, a portion of the usable information in this scheme can be offered via an open application programming interface (Open API). To create a 2D POIs obtained from an indoor 3D object that was provided as a relative coordinate with only 3D geometric features, several steps were needed: adding the object to the system, storing the object as an absolute coordinate, and linking the object with an outdoor mapping service. In addition, to provide more useful information about indoor POIs generated from 3D models for users, detailed information should be further managed by directly using the Open APIs designed in this study. Subsequently, a mobile web mapping service system to visualize indoor contents was deployed to deliver practical processing and improvements based on the deployed Open API. The possibility of effective management and application of POIs related to indoor contents was confirmed through the mobile web-mapping demo service that was established using Open API.

Recently, web application services based on cloud computing technologies are being offered. In the web-based application field of geo-spatial data management or processing, data processing services are produced or operated using various information communication technologies. Platform-as-a-Service (PaaS) is a type of cloud computing service model that provides a platform that allows service providers to implement, execute, and manage applications without the complexity of establishing and maintaining the lower-level infrastructure components, typically related to application development and launching. There are advantages, in terms of cost-effectiveness and service development expansion, of applying non-proprietary PaaS cloud computing. Nevertheless, there have not been many studies on the use of PaaS technologies to build geo-spatial application services. This study was based on open source PaaS technologies used in a geo-spatial image processing service, and it aimed to evaluate the performance of that service in relation to the Web Processing Service (WPS) 2.0 specification, based on the Open Geospatial Consortium (OGC) after a test application deployment using the configured service supported by a cloud environment. Using these components, the performance of an edge extraction algorithm on the test system in three cases, of 300, 500, and 700 threads, was assessed through a comparison test with another test system, in the same three cases, using Infrastructure-as-a-Service (IaaS) without Load Balancer-as-a-Service (LBaaS). According to the experiment results, in all the test cases of WPS execution considered in this study, the PaaS-based geo-spatial service had a greater performance and lower error rates than the IaaS-based cloud without LBaaS.IaaS is a cloud service that provides an infrastructure environment that is highly available without the need to purchase computing resources or equipment. The hardware resources are virtualized and are being offered to potential customers. The user is not paying any hardware infrastructure and maintenance costs, but only an operational cost due to the use of virtualized resources, which are controlled by another party. The user can acquire virtualized resources on demand from the web by exploiting a certain service that is offered by a particular endpoint, but that service is not necessarily centralized. Rapid elasticity, as one of the main advantageous points of a cloud computing environment, enables the user to allocate infrastructure resources to meet the usual incoming traffic and automatically allocate additional infrastructure resources to meet demand when traffic is heavy beyond a specified threshold. On the other hand, if traffic is kept below the lower threshold after a certain period of time, unnecessary resources are cleaned up to keep infrastructure resources to a minimum.Early on, cloud computing technologies focused on optimizing IaaS, for instance, using the Amazon Web Service (AWS). PaaS is also the core service model for cloud computing, providing servi...

Surface reflectance products obtained through the absolute atmospheric correction of multispectral satellite images are useful for precise scientific applications. For broader applications, the reflectance products computed using high-resolution images need to be validated with field measurement data. This study dealt with 2.2-m resolution Korea Multi-Purpose Satellite (KOMPSAT)-3A images with four multispectral bands, which were used to obtain top-of-atmosphere (TOA) and top-of-canopy (TOC) reflectance products. The open-source Orfeo Toolbox (OTB) extension was used to generate these products. Next, these were subsequently validated by considering three sites (i.e., Railroad Valley Playa, NV, USA (RVUS), Baotou, China (BTCN), and La Crau, France (LCFR)) in RadCalNet, as well as a calibration and validation portal for remote sensing. We conducted the validations comparing satellite image-based reflectance products and field measurement reflectance based on data sets acquired at different times. The experimental results showed that the overall trend of validation accuracy of KOPSAT-3A was well fitted in all the RadCalNet sites and that the accuracy remained quite constant. Reflectance bands showing the minimum and maximum differences between the sets of experimental data are presented in this paper. The vegetation indices (i.e., the atmospherically resistant vegetation index (ARVI) and the structure insensitive pigment index (SIPI)) and three TOC reflectance bands obtained from KOMPSAT-3A were computed as a case study and used to achieve a detailed vegetation interpretation; finally, the correspondent results were compared with those obtained from Landsat-8 images (downloaded from the Google Earth Engine (GEE)). The validation and the application scheme presented in this study can be potentially applied to the generation of analysis ready data from high-resolution satellite sensor images.

There are many cases wherein services offered in geospatial sectors are integrated with other fields. In addition, services utilizing satellite data play important roles in daily life and in sectors such as environment and science. Therefore, a management structure appropriate to the scale of the system should be clearly defined. The motivation of this study is to resolve issues, apply standards related to a target system, and provide practical strategies with a technical basis. South Korea uses the e-Government Standard Framework, using the Java-based Spring framework, to provide guidelines and environments with common configurations and functions for developing web-based information systems for public services. This web framework offers common sources and resources for data processing and interface connection to help developers focus on business logic in designing a web system. In this study, a geospatial image processing system-linked with the Open Geospatial Consortium (OGC) Web Processing Service (WPS) 2.0 standard for real geospatial information processing, and based on this standard framework-was designed and built utilizing fully open sources. This is the first case of implementation based on WPS 2.0 running on the e-Government Standard Framework. Establishing a standard for its use will be important, and the system built in this study can serve as a reference for the foundational architecture in building geospatial web service systems with geodata-processing functionalities in government agencies.