Grid based Real Time Collaborative System

Manavalan, R.; Gupta, Kishor Datta; Chattopadhyay, Sankha; Mangala,

doi:10.1109/pdgc.2010.5679599

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…In addition to visualization such system supports interactive collaborations of spatial data between and across the remote labs through which the domain experts can estimates the effect of onsite disaster situations and suggest suitable mitigation operations. Finer details related to such specialized system deployed on top of India's first computational grid environment have been captured in Manavalan et al (2010). …”

Section: Geocarto International 751mentioning

confidence: 99%

DEM and SAR image based flood feature extraction techniques to map the deep and shallow flood inundated regions of known as well as remote disaster regions

Manavalan¹,

Rao²

2013

Geocarto International

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Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tgei20 DEM and SAR image based flood feature extraction techniques to map the deep and shallow flood inundated regions of known as well as remote disaster regions DEM and SAR image based flood feature extraction techniques to map the deep and shallow flood inundated regions of known as well as remote disaster regions It is viable to differentiate the deep and shallow flood inundated regions through a new flood feature extraction techniques named as 'Digital Elevation Model (DEM) and Synthetic Aperture Radar (SAR) image based flood feature extraction model'. The proposed model has been built mainly on the top of DEM of the disaster region without adopting standard multi-layer GIS techniques. To meet the time related factors of flood early warning system the image clustering operations has been automated at three different levels which bifurcates the input datasets and extracts the much required end results such as deep flooded regions, shallow flood inundated regions and non-flooded regions. The model has been tested with SAR flood images of known geographical region as well as remote geographical region. The proposed model can be automated against the input SAR sensor image and corresponding DEM of the respective SAR scene of any part of the world.

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Section: Geocarto International 751mentioning

confidence: 99%