In recent years, black-box machine learning approaches have become a dominant
modeling paradigm for knowledge extraction in Remote Sensing. Despite the
potential benefits of uncovering the inner workings of these models with
explainable AI, a comprehensive overview summarizing the used explainable AI
methods and their objectives, findings, and challenges in Remote Sensing
applications is still missing. In this paper, we address this issue by
performing a systematic review to identify the key trends of how explainable AI
is used in Remote Sensing and shed light on novel explainable AI approaches and
emerging directions that tackle specific Remote Sensing challenges. We also
reveal the common patterns of explanation interpretation, discuss the extracted
scientific insights in Remote Sensing, and reflect on the approaches used for
explainable AI methods evaluation. Our review provides a complete summary of
the state-of-the-art in the field. Further, we give a detailed outlook on the
challenges and promising research directions, representing a basis for novel
methodological development and a useful starting point for new researchers in
the field of explainable AI in Remote Sensing.

近年来，黑盒机器学习方法已成为遥感领域知识提取的主要建模范式。本文通过系统回顾，总结了遥感应用中可解释人工智能方法的使用趋势、发现和挑战，并揭示了解释解析的常见模式，提供了该领域的最新综述和未来研究方向的基础。

揭开黑匣子：遥感中可解释人工智能的系统综述

Opening the Black-Box: A Systematic Review on Explainable AI in Remote  Sensing

Geosciences is a field of great societal relevance that requires solutions to
several urgent problems facing our humanity and the planet. As geosciences
enters the era of big data, machine learning (ML) -- that has been widely
successful in commercial domains -- offers immense potential to contribute to
problems in geosciences. However, problems in geosciences have several unique
challenges that are seldom found in traditional applications, requiring novel
problem formulations and methodologies in machine learning. This article
introduces researchers in the machine learning (ML) community to these
challenges offered by geoscience problems and the opportunities that exist for
advancing both machine learning and geosciences. We first highlight typical
sources of geoscience data and describe their properties that make it
challenging to use traditional machine learning techniques. We then describe
some of the common categories of geoscience problems where machine learning can
play a role, and discuss some of the existing efforts and promising directions
for methodological development in machine learning. We conclude by discussing
some of the emerging research themes in machine learning that are applicable
across all problems in the geosciences, and the importance of a deep
collaboration between machine learning and geosciences for synergistic
advancements in both disciplines.

本文介绍了机器学习在地球科学问题中所面临的独特挑战和机遇，强调各种地球科学问题所需的新颖机器学习方法的重要性，并讨论了潜在的方法学发展方向及其在地球科学中的应用。

地球科学中的机器学习：挑战与机遇

Machine Learning for the Geosciences: Challenges and Opportunities

Biomedical information extraction (BioIE) is important to many applications,
including clinical decision support, integrative biology, and
pharmacovigilance, and therefore it has been an active research. Unlike
existing reviews covering a holistic view on BioIE, this review focuses on
mainly recent advances in learning based approaches, by systematically
summarizing them into different aspects of methodological development. In
addition, we dive into open information extraction and deep learning, two
emerging and influential techniques and envision next generation of BioIE.

该研究综述了生物医学信息提取的最新发展，着重关注基于学习的方法以及开放信息提取和深度学习等新兴技术对其未来发展的影响。