Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction

Unreliable predictions can occur when using artificial intelligence (AI) systems with negative consequences for downstream applications, particularly when employed for decision-making. Conformal prediction provides a model-agnostic framework for uncertainty quantification that can be applied to any dataset, irrespective of its distribution, post hoc. In contrast to other pixel-level uncertainty quantification methods, conformal prediction operates without requiring access to the underlying model and training dataset, concurrently offering statistically valid and informative prediction regions, all while maintaining computational efficiency. In response to the increased need to report uncertainty alongside point predictions, we bring attention to the promise of conformal prediction within the domain of Earth Observation (EO) applications. To accomplish this, we assess the current state of uncertainty quantification in the EO domain and found that only 20% of the reviewed Google Earth Engine (GEE) datasets incorporated a degree of uncertainty information, with unreliable methods prevalent. Next, we introduce modules that seamlessly integrate into existing GEE predictive modelling workflows and demonstrate the application of these tools for datasets spanning local to global scales, including the Dynamic World and Global Ecosystem Dynamics Investigation (GEDI) datasets. These case studies encompass regression and classification tasks, featuring both traditional and deep learning-based workflows. Subsequently, we discuss the opportunities arising from the use of conformal prediction in EO. We anticipate that the increased availability of easy-to-use implementations of conformal predictors, such as those provided here, will drive wider adoption of rigorous uncertainty quantification in EO, thereby enhancing the reliability of uses such as operational monitoring and decision making

Characterisation of industrial thermal plumes discharged into coastal waters using remote sensing and simulation techniques

Coastal power stations use sea water as a coolant. The cooling waters discharges released by nuclear power stations, referred to in this thesis as thermal plumes, result in locally raised temperatures of the surrounding environments in the coastal regions. Since raised temperatures can impact aquatic flora and fauna, there are environmental permits and policies describing the limits for the allowed maximum temperatures of the discharged thermal plumes. It is therefore of paramount importance that we can characterise the industrial thermal plumes to a sufficient extent. Achieving this using traditional methods has been challenging due to high cost of the field campaigns, high dependence on weather and no repetition of the measuring campaigns. Access to freely available high-resolution satellite imagery has opened up a potentially viable way of characterising surface thermal plumes through satellite remote sensing. Such observations present an opportunity to study sea surface temperature (SST) distributions in the vicinity of the power stations at spatial resolution of 30 m - 100 m and temporal resolution of up to 16 days. To evaluate the potential of high resolution remote sensing, a methodology for thermal plume detection is developed. Thermal plumes observed by the satellite imagery show high dependence on the tidal conditions for the majority of the investigated sites. The plumes have been found to be embedded within the tidal stream and their direction of dispersion followed the direction of the tidal currents. The observed surface thermal gains were highest in the summer months and lowest in the winter months. In order to gain understanding of plume dispersion subsurface, high resolution three dimensional (3-D) simulations coupled with satellite observations were used. Plume dispersion was modelled for an inter-tidal area during the ebb and the flood tide using FLOW-3D software. The simulated plume was found to raise to the surface and spread depending on the strength and direction of the tidal currents, with limited area of raised temperatures at the seabed concentrated close to the discharge pipes. Available satellite data was used to compare with the simulation outputs and gain validation of the high resolution 3-D model of the plume. Despite the potential of high resolution satellite data sets and 3-D simulations in understanding industrial thermal plumes, a thorough evaluation of their capabilities, limitations and a consideration of routine use of such techniques and scientific advances compared to traditional methods have not been fully explored in previous studies. This work provides a detailed comparison of thermal plume characterisation methods, their limitations and recommendations for future set-up