Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …

Remote sensing visual question answering (RSVQA) was recently proposed with the aim of
interfacing natural language and vision to ease the access of information contained in Earth …

Peoples' recreation and well-being are closely related to their aesthetic enjoyment of the
landscape. Ecosystem service (ES) assessments record the aesthetic contributions of …

In past years, we have witnessed the fields of geosciences and remote sensing and artificial
intelligence (AI) become closer. Thanks to the massive availability of observational data …

Forest maps are essential to understand forest dynamics. Due to the increasing availability
of remote sensing data and machine learning models like convolutional neural networks …

The recent mass adoption of DNNs, even in safety-critical scenarios, has shifted the focus of
the research community towards the creation of inherently intrepretable models. Concept …

Deep neural networks (DNNs) have found their way into many applications with potential
impact on the safety, security, and fairness of human-machine-systems. Such require basic …

Recently, interpretable machine learning has re-explored concept bottleneck models (CBM).
An advantage of this model class is the user's ability to intervene on predicted concept …

Learning visual concepts from raw images without strong supervision is a challenging task.
In this work, we show the advantages of prototype representations for understanding and …