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* [Caml-list] [Free Springer Book on Remote Sensing] Few days left to submit your short abstract!! Deadline: July 25th,2022
@ 2022-07-22 15:10 mohamed Lahby
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From: mohamed Lahby @ 2022-07-22 15:10 UTC (permalink / raw)
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*===========================Call for chapters=====================*
Dear colleague,

We are in the process of coming up with a volume titled *“Applications
of Remote Sensing Techniques for Sustainable Security ” *to be published by
Springer (proposal is initially communicated, awaiting for final approval)
at *the end of 2022.*

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
 is *July 25th, 2022 (Firm Deadline)*

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.
This forthcoming book seeks contributions to remote sensing data. In
particular, we are looking for research papers on applications of remote
sensing in many fields of smart cities such as smart transportation, smart
agriculture, and smart Environment.

*NB: *There are no submission or acceptance fees for manuscripts submitted
to this book for publication

The tentative structure of the book *(but are not limited to the following
Parts*) is mentioned below:.

*Part 1: Theoretical and Applied Aspects of Remote Sensing*

   - Chapter 1. Remote Sensing Techniques State-of-the-Art
   - Chapter 2. Hyperspectral remote sensing applications: State-of-the-Art
   - Chapter 3. Smart cities: State-of-the-Art

*Part 2: Remote sensing and Smart cities Applications*

   - Chapter 4. Smart Agriculture Security
   - Chapter 5. Smart Transportation Security
   - Chapter 6. Smart Environment security
   - Chapter 7. Smart Buildings security;
   - Chapter 8. Smart Economy security

*Part 3: Remote sensing and technologies*

   - Chapter 9. Artificial Intelligence for Enabled Remote Sensing
   - Chapter 10. machine learning for Enabled Remote Sensing
   - Chapter 11. Deep Learning for Enabled Remote Sensing
   - Chapter 12. XAI for Enabled Remote Sensing
   - Chapter 13. Big Data for Enabled Remote Sensing
   - Chapter 14. Blockchain for Enabled remote sensing

*Part  4:  Futuristic Ideas*

   - Chapter 15. Futuristic Ideas for Remote sensing

Best regards

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