Month: May 2022

1. Discovering optimally representative dynamical locations (ORDL) in big multivariate spatiotemporal data: a case study of precipitation in Australia from space to ground sensors

   We would like to draw attention to the work of one of our researchers Benjamin Hines, who has a newly published paper “Discovering optimally representative dynamical locations (ORDL) in big multivariate spatiotemporal data: a case study of precipitation in Australia from space to ground sensors.” His work conducts a kind of ‘data fusion’ from rainfall […]

   safe-risccs.science.unimelb.edu.au/2022/05/31/discovering-optimally-representative-dynamical-locations-ordl-in-big-multivariate-spatiotemporal-data-a-case-study-of-precipitation-in-australia-from-space-to-ground-sensors-2

2. Methodology to deal with extremely high dimensional, nonstationary, land topography data

   We congratulate one of our researchers, Hangfei Zheng, for her recent work in developing methodology to deal with extremely high dimensional, nonstationary, land topography data. This is a key advancement in dealing with “big data” issues that arise in our work in predicting large scale catastrophic failure events such as landslides. These, by their nature, […]

   safe-risccs.science.unimelb.edu.au/2022/05/31/methodology-to-deal-with-extremely-high-dimensional-nonstationary-land-topography-data

3. Sentinel 1 Mission

   Two of our researchers, Ran Huo and Michael Manthey have been involved in processing complex satellite data from the European Space Agency’s Sentinel 1 mission. This uses a microwave technique known as “synthetic aperture radar” to record soundings of the entire globe. These are relatively impervious to weather conditions unlike optical imaging and once “unwrapped” […]

   safe-risccs.science.unimelb.edu.au/2022/05/31/sentinel-1-mission