Geosciences & Remote Sensing

There is an exponential increase in the volume and variety of geo- and environmental data coming from different sources, including numerous earth observation monitoring networks and remote sensing images. In order to be used in intelligent decision making process, these data and information have to leverage machine learning in order to be efficiently processed and understood. In this session we will consider the application of Machine Learning, which is nonlinear, robust and universal tool for the analysis, modelling and visualisation of complex phenomena, in geoscience & remote sensing fundamental and applied studies for the recognition and classification of environmental patterns.

Biodiversity

Maintaining and protecting biodiversity of plants and animals is essential for our life on earth. Due to climate change, human destruction of precious habitats, and industrial agriculture, biodiversity is decreasing in many countries. A major bottleneck for countermeasures is lack of accurate, dense biodiversity data at large scale. Today, biodiversity is mainly measured with field surveys that assess biodiversity manually in situ, which is labor-intensive, costly, and delivers only scarce, point-wise data with long revisit cycles. In this session, we will explore how machine learning can help automating, scaling, and improving quality of biodiversity estimation to help protecting the environment.

Sustainability & Energy

The exponential growth of various types of data together with the availability of machine learning and artificial intelligence methods are dramatically impacting the urban energy sector. It generates big data through smart meters, sensor networks, customer payments, satellite imagery, etc. This session provides a framework to discuss the use of big data and associate analytic methods in the urban energy sector with emphasis on machine learning and artificial intelligence methods for modelling and optimization of power generation and heat production. Furthermore, the session explores means of recognizing patterns in energy consumption as well as forecasting the energy resource potentials.

Climate

Droughts, heat waves, floods and storms induced by climate changes are some of the major natural disasters which are directly impacting population and society. Researchers develop predictive tools that can possibly reduce adverse impacts by allowing some kind of preventive action. Using artificial intelligence on the flood of data that is generated every day from sensors, gauges and monitors will allow to spot patterns quickly and automatically. In this session, we will explore how machine learning can help improving climate forecasts, better identifying atmospheric processes for building a resilient framework to face the effects of climate change.