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From: mohamed Lahby <lahby@ieee.org>
To: caml-list@inria.fr
Subject: [Caml-list] [Free Springer Book]Contributing a chapter for a Springer Book on Applications of Remote Sensing Techniques for Sustainable Security In Smart cities
Date: Sun, 10 Apr 2022 21:48:13 +0000
Message-ID: <CAMo8cMq75NZd3dHtZzM=X3w9z4fpSa9JOkCjt_T_+KjcwKt_kA@mail.gmail.com> (raw)

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Dear colleagues,

We are in the process of coming up with a volume titled *“Applications of
remote sensing techniques for Sustainable Security In Smart cities ” *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 lahby@ieee.org
 is *April, 20th, 2022*

*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.
This forthcoming book seeks contributions to remote sensing data. In
particular, we are looking for research papers on applications of remote
sensing in many field 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 and Smart cities
*Part 2: *Remote Sensing for Smart Agriculture Security
*Part 3:* Remote Sensing for Smart Transportation Security
*Part 4:* Remote Sensing for Smart Environment security
*Part  5:*  Artificial Intelligence for Remote Sensing
*Part  6: * Big Data for Remote Sensing
*Part  7: * Futuristic Ideas


Best regards

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Thread overview: 2+ messages / expand[flat|nested]  mbox.gz  Atom feed  top
2022-04-10 21:47 mohamed Lahby [this message]
2022-04-18  9:56 mohamed Lahby

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