Mobile app approach by open source stack for satellite images utilization

Kang, Sanggoo; Lee, Ki-Won

doi:10.1080/2150704x.2013.781286

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…The only suitable choice for this study was gvSIG, a tool for mobile GIS application developers [12][13][14][15] published by OSGeo.org, who organizes yearly FOSS4G (Free and Open Source Software for Geospatial) conferences. gvSIG is open and free, and it supports the viewing of geographic data [16] on OpenStreetMap (OSM), an open source collaborative project for creating maps. gvSIG Mini is a comprehensive mobile GIS client on Android that allows custom editing and accessing, address lookup, and POI search.…”

Section: Implementation Platform Of a Park Guiding Systemmentioning

confidence: 99%

Mobile Guiding System for Parks Based on Highly Precision Electronic Map

Meng¹,

Huang²,

Ming-sheng³

2014

IJMUE

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Mobile Guiding System for Parks based on highly precision electronic map is of many researchers' concern, but commercial software cannot perform GPS positioning on custom-made maps for parks, and is not accurate enough to satisfy the requirements for drawing high precision electronic maps. This study uses trajectories measured by My Tracks software and a map edited in JOSM software to implement a custom-made electronic map. By processing GPS data gathered by smooth filtering, k-mean algorithm, mean filtering, and weighted-averaging (SKMW), it calibrated a custom-made map for a park mobile guiding system, based on a design from the gvSIG Mini open source project. Using Beijing JiuFeng National Forest Park as a case, this study developed LBS and SNS related service functions. The field test shows that the SKMW method can effectively improve the accuracy of a custom-made electronic map, and the practicality and expandability of a park mobile guiding system.

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Section: Implementation Platform Of a Park Guiding Systemmentioning

confidence: 99%

Mobile Guiding System for Parks Based on Highly Precision Electronic Map

Meng¹,

Huang²,

Ming-sheng³

2014

IJMUE

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“…On a mobile device in cloud computing, the system implementation model for satellite image processing based on OTB algorithms has been studied [18,19]. Figure 3 shows tiled pieces of auto-scaling experiment data and the results obtained by OTB-based algorithms in a cloud computing environment.…”

Section: Experimental Condition For Auto-scalingmentioning

confidence: 99%

Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment

Kang

Lee

2016

Remote Sensing

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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.

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“…As the previous works, a smartphone application for satellite image information processing service were implemented on Android OS (Kang and Lee, 2013a). Mobile application providing image processing algorithms, such as Maximum Autocorrelation Factor (MAF) and edge detection,was designed and developed on Amazon Web Service Elastic Cloud Computing (AWS EC2) and Simple Storage Service (S3) (Lee and Kang, 2013 Image fusion is regarded as one of important functionalities according to the continuously increasing the types of multiple geo-based data sources (Zhang, 2010).…”

mentioning

confidence: 99%