caml-list - the Caml user's mailing list
 help / color / mirror / Atom feed
From: mohamed Lahby <>
Subject: [Caml-list] [Free Springer Book] Contributing a chapter for a Springer Book on Applications of Remote Sensing Techniques for Sustainable Security
Date: Sun, 1 May 2022 10:17:07 +0000	[thread overview]
Message-ID: <> (raw)

[-- Attachment #1: Type: text/plain, Size: 3805 bytes --]

-  We apologize if you receive multiple copies of this CFC.

-  We appreciate your help to forward this CFC to your friends & email

Dear colleagues,

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 t*he 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 *May, 10th, 2022 (Extended 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

[-- Attachment #2: Type: text/html, Size: 5596 bytes --]

                 reply	other threads:[~2022-05-01 10:15 UTC|newest]

Thread overview: [no followups] expand[flat|nested]  mbox.gz  Atom feed

Reply instructions:

You may reply publicly to this message via plain-text email
using any one of the following methods:

* Save the following mbox file, import it into your mail client,
  and reply-to-all from there: mbox

  Avoid top-posting and favor interleaved quoting:

* Reply using the --to, --cc, and --in-reply-to
  switches of git-send-email(1):

  git send-email \ \ \ \

* If your mail client supports setting the In-Reply-To header
  via mailto: links, try the mailto: link
Be sure your reply has a Subject: header at the top and a blank line before the message body.
This is a public inbox, see mirroring instructions
for how to clone and mirror all data and code used for this inbox;
as well as URLs for NNTP newsgroup(s).