Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data (Second Edition)

Dear Colleagues,

With the advent of the era of remote sensing big data, artificial intelligence (AI) has spread to almost all corners of various remote sensing applications. In many cases, characteristics of remote sensing big data, such as multi-source, multi-scale, high-dimensional, dynamic state, isomer, non-linear characteristics, etc., are well learned by advanced AI algorithms. Data-driven methods, especially deep learning models, have achieved state-of-the-art results for most remote sensing image processing tasks (object detection, segmentation, etc.) and even some remote sensing inverse tasks (atmosphere, vegetation, etc.). As such, by using large labeled datasets, we can often make highly accurate predictions of remote sensing data.

However, current data-driven AI does not provide us a clear physical or cognitive meaning of the internal features and representations of remote sensing big data. Most deep learning techniques do not disclose how data features take effect and why the predictions are made. Remote sensing big data exacerbate the problem of the untransparent and unexplainable nature of current AI. This is becoming a barrier between the latest AI techniques and some remote sensing applications. Many scientists in hydrology remote sensing, atmospheric remote sensing, ocean remote sensing, etc., do not even believe the prediction results obtained via deep learning, since these communities are more inclined to rely on models with a clear physical meaning. Explainable artificial intelligence (XAI) is widely acknowledged as a crucial step to the practical deployment of AI models in remote sensing communities.

This Special Issue seeks contributions on the theory or applications of XAI in remote sensing big data. In particular, we seek research articles on applications whose physical or cognitive models are represented by XAI, or articles addressing how remote sensing big data drive models based on XAI.

Topics of interest include, but are not limited to, the following:

• Theoretical and philosophical foundations of XAI;
• XAI for remote sensing image visual tasks, such object detection, segmentation, classification, change detection, fusion, etc.;
• XAI for multi-source geospatial data analysis for different environmental applications;
• XAI for terrestrial remote sensing, atmospheric remote sensing, ocean remote sensing, etc.;
• XAI for unmanned aerial vehicle (UAV) remote sensing big data;
• XAI for simultaneous localization and mapping (SLAM) with remote sensing big data;
• XAI for global scale inversion problems, such as biomass, thermal emission, vegetation, etc.;
• XAI for high-performance computation in large-scale remote sensing applications.

Dr. Peng Liu
Dr. Masoud Mahdianpari
Dr. Fang Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• explainable artificial intelligence (XAI)
• remote sensing (RS) big data
• semantic interpretation
• deep feature understanding
• large scale RS image classification/segmentation
• object detection
• multi-source geospatial data
• large scale inversion problems
• spatial optimization
• environmental applications
• climate change
• machine learning
• deep learning

• Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data in Remote Sensing (11 articles)