Automatic extraction of tents during Hajj from airborne images to support land use optimization

Alharthy, Abdulatif; Koshak, Nabeel

doi:10.1016/j.autcon.2006.02.001

Cited by 3 publications

(1 citation statement)

References 3 publications

(2 reference statements)

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“…For instance in [4], the authors use Global Positioning System (GPS) () and GIS concepts to analyze the movement of pilgrims in Mataf area in order to support space redesign. In [5], airborne images have been used to extract pilgrims' tents and optimize the exploitation of limited land in Mina area. However, none of the existing works take into consideration the context of individual and the whole crowd.…”

Section: Introductionmentioning

confidence: 99%

A framework for crowd-sourced data collection and context-aware services in Hajj and Umrah

Ahmad

Rahman

Rehman

et al. 2014

2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)

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We propose a context aware framework that offers a set of cloud-based services to support a very large Hajj and Umrah crowd by capturing their contexts using smartphones. The proposed framework captures the individual's context, provides a set of adapted services, and allows being in touch with a subset of one's community of interest. We leverage the spatiotemporal sensory data captured by our framework to define users' contexts for optimized services. Our proposed framework is also envisioned to assist the Hajj and Umrah authorities to (1) improve Hajj & Umrah documentation, (2) improve Hajj organization through better understanding of pilgrims' (individual and crowd) spatial and temporal behavior and needs, and (3) protect pilgrims' environment through environmental monitoring. In particular, the developed methods, techniques, and algorithms will support the pilgrimage quality of experience. We have tested our system through end-user subjects and due to apply for the upcoming Hajj events. We present our implementation details and the general impression of end users about our system.

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Section: Introductionmentioning

confidence: 99%