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Renoy Girindran

PhD Researcher,

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Biography

Renoy loves interdisciplinary research and main research interests include application of geospatial technologies in sustainable environmental management, urban development and climate change. He holds Master's degree in both Physics (MSc) and Remote sensing (M.Tech). He has more than eight years of domain experience having worked in the UK, Germany & India. Renoy served as the Geospatial Advisor in Government of Delhi Company, India between 2012 - 2014. He was a visiting scientist at University of Bremen, Germany in 2015. He served as a Project Scientist at National Centre for Sustainable Coastal Management, India in 2016.

Expertise Summary

Remote Sensing, GIS, Geoinformatics

Research Summary

His current PhD research is part of the wider project, 'Sustaining Urban Habitats: An interdisciplinary approach' funded by the Leverhulme Trust and based at the Laboratory of Urban Complexity &… read more

Current Research

His current PhD research is part of the wider project, 'Sustaining Urban Habitats: An interdisciplinary approach' funded by the Leverhulme Trust and based at the Laboratory of Urban Complexity & Sustainability (LUCAS). Present project mainly contribute towards the data and modeling component of the wider project. Renoy's work mainly focusing on three components: 1) To develop a globally replicable methodology to generate 3D building height information for OSM building footprint data using open data, considering case studies from Nottingham, UK and Shanghai, China; 2) Increase the spatial resolution of open source satellite image using sample high resolution satellite image and 3) To extract building footprints from these open source satellite data where OSM footprints are absent.

Results from this research is expected to contribute in global change studies, vulnerability assessments in disaster management, energy modeling and climate change studies. Further these methods will help to generate 3D models in areas where people do not have access to expensive high resolution datasets, especially in middle and low income countries.

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