Water depth estimation from Sentinel-2 imagery using advanced machine learning methods and explainable artificial intelligence

Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

With the availability of large databases and recent improvements in deep learning methodology, the performance of AI systems is reaching, or even exceeding, the human level on an increasing number of complex tasks. Impressive examples of this development can be found in domains such as image classification, sentiment analysis, speech understanding or strategic game playing. However, because of ...

Building Explainable Artificial Intelligence Systems

As artificial intelligence (AI) systems and behavior models in military simulations become increasingly complex, it has been difficult for users to understand the activities of computer-controlled entities. Prototype explanation systems have been added to simulators, but designers have not heeded the lessons learned from work in explaining expert system behavior. These new explanation systems a...

Explainable Artificial Intelligence for Training and Tutoring

This paper describes an Explainable Artificial Intelligence (XAI) tool that allows entities to answer questions about their activities within a tactical simulation. We show how XAI can be used to provide more meaningful after-action reviews and discuss ongoing work to integrate an intelligent tutor into the XAI framework.

Dust source mapping using satellite imagery and machine learning models

Predicting dust sources area and determining the affecting factors is necessary in order to prioritize management and practice deal with desertification due to wind erosion in arid areas. Therefore, this study aimed to evaluate the application of three machine learning models (including generalized linear model, artificial neural network, random forest) to predict the vulnerability of dust cent...

Statistical methods with application to machine learning and artificial intelligence