Contributing a chapter for a Springer Book on XAI in Remote Sensing: Theories, Applications and Challenges
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Mohamed lahby
Sep 12, 2021, 2:07:03 AM9/12/21
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Dear colleagues,
We cordially invite you to contribute a chapter. The full chapter is due later this year but for now, I will just need the following:
- Author List
- Chapter Title
- Abstract (between 2 and 6 sentences)
The last deadline to submit your short abstract directly at la...@ieee.org is Oct, 10th, 2021
SCOPE:
With the advent of the big data era in remote sensing, artificial intelligence (AI) has spread to almost every corner of various remote sensing applications. In many cases, the characteristics of remote sensing big data, such as multi-source, multi-scale, high-dimensional, dynamic state, isomeric, and non-linear features, 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 some inverse remote sensing tasks (atmosphere, vegetation, etc.). Using large labeled datasets, we can often make very accurate predictions on remote sensing data.
However, current data-driven AI has not provided us with clear physical or cognitive meaning of remote sensing data's internal features and representations. Most deep learning techniques do not reveal how data features take effect and why predictions are made. Remote sensing data has exacerbated the problem of opacity and inexplicability of current AI. It becomes a barrier between the latest AI techniques and some remote sensing applications. Many scientists in hydrological remote sensing, atmospheric remote sensing, oceanic remote sensing, etc. do not even believe the results of deep learning predictions, as these communities are more inclined to believe models with clear physical meaning. Explainable Artificial Intelligence (XAI) is widely recognized as a crucial step for the practical deployment of AI models in remote sensing communities.
This forthcoming book seeks contributions to the theory or applications of XAI in remote sensing data. In particular, we are looking for research papers on applications with physical or cognitive models represented by XAI, or papers dealing with how remote sensing data drives the XAI-based model.
With the advent of the big data era in remote sensing, artificial intelligence (AI) has spread to almost every corner of various remote sensing applications. In many cases, the characteristics of remote sensing big data, such as multi-source, multi-scale, high-dimensional, dynamic state, isomeric, and non-linear features, 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 some inverse remote sensing tasks (atmosphere, vegetation, etc.). Using large labeled datasets, we can often make very accurate predictions on remote sensing data.
However, current data-driven AI has not provided us with clear physical or cognitive meaning of remote sensing data's internal features and representations. Most deep learning techniques do not reveal how data features take effect and why predictions are made. Remote sensing data has exacerbated the problem of opacity and inexplicability of current AI. It becomes a barrier between the latest AI techniques and some remote sensing applications. Many scientists in hydrological remote sensing, atmospheric remote sensing, oceanic remote sensing, etc. do not even believe the results of deep learning predictions, as these communities are more inclined to believe models with clear physical meaning. Explainable Artificial Intelligence (XAI) is widely recognized as a crucial step for the practical deployment of AI models in remote sensing communities.
This forthcoming book seeks contributions to the theory or applications of XAI in remote sensing data. In particular, we are looking for research papers on applications with physical or cognitive models represented by XAI, or papers dealing with how remote sensing data drives the XAI-based model.
Topics
Topics of interest include, but are not limited to:
Part A: Fundamental Concepts of Explainable Artificial Intelligence
Part B: Fundamental Concepts of Big Data Mining
Part C: Artificial Intelligence for Remote Sensing
Part D: Explainable Artificial Intelligence for Remote Sensing
Part E: Futuristic Ideas
Topics of interest include, but are not limited to:
Part A: Fundamental Concepts of Explainable Artificial Intelligence
Part B: Fundamental Concepts of Big Data Mining
Part C: Artificial Intelligence for Remote Sensing
Part D: Explainable Artificial Intelligence for Remote Sensing
Part E: Futuristic Ideas
Editors:
Mohamed Lahby, Hassan II University, Casablanca Morocco
Yassine Maleh, Sultan Moulay Slimane University, Morocco
Al-Fuqaha, Hamad Bin Khalifa University, Qatar
Luca Davoli, University of Parma, Italy
Dr. M.Lahby |
Laboratory of Mathematics and Applications, University Hassan II, Ecole Normale Supérieure (ENS) Casablanca, Morocco |
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