I am deeply honoured to have received this prestigious award from the Australian Museum. It is deeply appreciated recognition of not just my research, but also of the many talented researchers with whom I have been privileged to collaborate.
With the second satellite of the Sentinel-2 mission just launched in 2017, there is an incredible opportunity for the right student to become the world leader on how to analyse and make sense of this vast amount of data. It is anticipated that the project will make contributions to the theory of machine learning, with applications to the study of vegetation in general and more specifically in agriculture. The project however remains open if the successful candidate has other applications at heart (eg landslide, fire prediction). This project is fully funded. There is a paradigm shift in the way we can observe our planet: new-generation satellites (Sentinel-2, Landsat-8) are now imaging Earth completely, every 5 days, at high resolution, and at _no charge to end-users. It is not yet possible to tap the full value of this data, as existing machine learning methods for classifying time series cannot scale to such vast volumes of data. Temporal land-cover maps assign unique labels to geographic areas, describing their evolution over time. One of today’s key challenges is how to automatically produce these maps from the growing torrent of satellite data, to monitor Earth’s highly dynamic systems [a-h]. Presently, state-of-the-art research into time series classification lags behind the demands of the latest space missions, which produce terabytes of data each day. Why? Most of the research into time series classification has been done with datasets that hold no more than 10 thousand time series [i]. In contrast, the Sentinel-2 satellite gathers over 10 *trillion* time series, capturing Earth’s land surfaces and coastal waters at resolutions of 10-60m. Although much research has gone into classifying remote sensing images, few studies have analysed time series extracted from sequences of satellite images. This Project aims to create the machine learning technologies necessary to analyse series of satellite images, and to produce accurate temporal land-cover maps of our planet. Potential high-value applications for Australia include fire prevention, agricultural planning, and mining site monitoring and rehabilitation.
I am honoured to have been elected to the Board of the ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). I am looking forward to working with the new Board, led by incoming Chair Jian Pei, to further strengthen our community's leading conference and to support the community around it.