Explaining Deep lEarning Models of Satellite Time Series for the Agritech domain. – EDEM

Deep Learning (DL) models are today the most widely and successful techniques adopted on Multi-Variate Time Series (MTS) of satellite data for agricultural monitoring and mapping. Although effective, DL models are often considered black boxes due to their complex and opaque decision-making processes. This lack of transparency is a showstopper in the adoption process of these technologies by end-users. Efforts have been made in recent years to produce Explainable Machine Learning techniques (xAI) to provide a stronger descriptive approach to Deep Learning (DL) algorithms, allowing end-users to increase their trust and confidence in the model output. The most recurrent xAI method is based on Relevance Attribution, where importance scores are assigned to each data feature over time according to their importance for the model prediction. Relevance Attribution methods cannot explain the causes that drive a model to assign a specific target to a given instance. Such causes can have a dual nature: (1) some data variables or instances can induce a DL prediction model to assign importance to specific features, and (2) the obtained results can also depend on the model's internal primitives. In our work, we want to propose a novel Explainability (xAI) framework that aims to produce relevant explanations for DL models applied to MTS satellite data, allowing the users to perform large-scale agricultural mapping and monitoring. This high-level problem will be at the core of our project, which we name EDEM: Explaining Deep Learning Models of Satellite Time Series for the Agritech domain. EDEM aims to define novel Explainability methods that capture causal relationships between model outcomes and important events inferred from the data and DL building blocks. Throughout this project, we will validate the utility of the proposed explanations in several real-world use cases from agricultural monitoring using publicly available benchmark datasets, including France-specific data.