Titlebook: Application of Remote Sensing and GIS in Natural Resources and Built Infrastructure Management; Vijay P. Singh,Shalini Yadav,Ram Narayan Y

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Geospatial Technology for Estimating the Physical Vulnerability of Building Structures to Natural Hability area and 130 households with structural damage. The flood vulnerability index (FVI) is a powerful tool for a better understanding of community and building structures and to identify adaptations for vulnerability reduction. However, the FVI is limited by a number of factors that reduce its c

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Geo-spatial Modeling of Coastal Flood Exposures Due to Local Sea-Level Rise and Landscape Dynamics:fall events, and rising sea levels. The goal was achieved by spatially overlaying two objectives, viz. (i) Land use dynamics modeling for identifying assets, houses and (ii) Flood inundation modeling. Agent-based land use change model has been used to visualize the likely change pattern for the year

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Decadal Satellite Data Analysis for Flood Hazard Mapping: A Case Study of Eastern Uttar Pradesh,e most vulnerable and heavily affected by the flood hazard viz. Gorakhpur, Shravasti, Maharajganj, Balrampur, Siddharthnagar, Deoria and Sant Kabir Nagar. Spatial intersection technique has been implemented in GIS to determine the stagnated flood water areas.

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0921-092X GIS tools and technologies useful in agriculture and forest.This book discusses the problems in planning, building, and management strategies in the wake of application and expansion of remote sensing and GIS products in natural resources and infrastructure management. The book suggests proactive s

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Three-Dimensional (3D) Noise Pollution Visualization via 3D City Modelling,he noise level in 3 dimension (3D). A 3D geometrical database and the noise level are modelled and processed into a 3D environment. Due to insufficient noise pollution in 2D representation, this chapter presents a 3D noise visualization approach as it offers significant insight into situations where 3D noise effects are relevant.

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S. S. Sreejith,Nithya Mohan,M. R. P. Kurupresolution satellite dataset. The comparative results indicate that random forests have outperformed ML and NN in classifying the urban land cover using a high-resolution image. The user and producer accuracies of LULC are found to show no particular trend with any classification algorithm.

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