Distinguished Professor of Spatial Data Science at Lancaster University

Talk: Spatial Latent Gaussian Modelling with Change of Support

Spatial environmental data on some property of interest are often derived from multiple sources (e.g. EO satellites, in situ sensors, survey samples) with different spatial supports (the space on which a measurement or observation is defined). It is also common for predictors to be measured on different spatial supports than the response variable in a regression-type setting. The spatial support is ubiquitous for environmental science data, while notably absent in some other fields such as those that depend on natural language. Thus, for environmental science, image and data fusion are crucial tasks, including as part of multi-modal fusion. A common approach for handling different supports, used by practitioners, is to project all variables onto a common support (e.g., via downscaling or interpolation) and then use standard spatial models on this common support. However, this approach can introduce biases and the statistical coherence between different supports is not guaranteed. I will introduce a Bayesian spatial latent Gaussian model (SLGM) that can handle data with different rectilinear supports in the response variable and the predictors. The SLGM can handle changes of support naturally according to the properties of the spatial stochastic process being used, and can account for change of support uncertainty in parameter estimation and prediction. In the SLGM, spatial stochastic processes are defined as linear combinations of basis functions where Gaussian Markov random fields (GMRFs) define the weights. This process can then be projected to different (any) spatial supports whilst maintaining the same underlying parameters. The generalisation potential of the approach is demonstrated through simulation and a real use case, modelling the land suitability of improved grassland in South Wales, UK.

Asst. Prof. at the University of Copenhagen

Talk: AI for Climate Risk and Environmental Monitoring

This talk will present recent advances in applying machine learning to climate-risk assessment and environmental monitoring. It will cover the integration of satellite imagery, sensor networks, and geospatial data for detecting environmental change, forecasting risk, and supporting evidence-based decision making for climate adaptation and resilience.

Principal Researcher at RISE Research institutes of Sweden

Talk: From Pixels to Decisions: Trustworthy Machine Vision for the Environment

The presentation will examine the pathway from remote-sensing imagery to reliable environmental decisions. Topics will include uncertainty estimation, explainability, domain shift, data quality, and evaluation of machine-vision systems used for biodiversity, land-use change, disaster response, and sustainable infrastructure monitoring.

Postdoctoral Researcher at IIT Bombay

Talk: Multimodal Learning for a Changing Planet

This keynote will focus on multimodal approaches that combine optical, radar, LiDAR, text, and other geospatial data sources. It will highlight how multimodal learning can improve robustness and coverage in Earth-observation applications, and discuss emerging research directions in self-supervised learning, data fusion, and scalable environmental intelligence.