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([209.85.218.49]) by mail3-smtp-sop.national.inria.fr with ESMTP/TLS/ECDHE-RSA-AES256-GCM-SHA384; 01 May 2022 12:15:27 +0200 Received: by mail-ej1-f49.google.com with SMTP id m20so22988049ejj.10 for ; Sun, 01 May 2022 03:15:26 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=ieee.org; s=google; h=mime-version:from:date:message-id:subject:to; bh=69faj0cBm6nzTEGlfu1AeIxLHWp1HgotzOJHjp37W68=; b=VmaCR+Ei+wK8QhHQkHFkS78rfu4dBYcpUr/D+0OJFazFPpzSJoVQT0LGxssf9ofmVx ZTCJmo3tjfy/PFe0e3To2cVw5h4BXn4UTifx1XjSOKo/EDdlVJKIzvtUtQw6jOVfkQ0a 4doCAUwISZMnHh+qSbTgsePx9cCaB7C7LB7Rw= X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20210112; h=x-gm-message-state:mime-version:from:date:message-id:subject:to; bh=69faj0cBm6nzTEGlfu1AeIxLHWp1HgotzOJHjp37W68=; b=y+PSM2f7RIkdD61TbVilvGH9wEmPZRnnw6IDiS84cY3UedLKjtYgh6cvxc8a0fBR2l HQ4YbpQ2wxIZHCg12KKghqsq3hnsRrpjHQqJ9GMAaS7bUDZuAqLjqbxpiAh4y4pr4Zr1 zE/cTfLE8fmnh+kUe3uxXaGRq42/L+s6yxlPkNbrsF1RuTWWmqWm68UNKW60SEC3brk2 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caml-list-owner@inria.fr Precedence: list Precedence: bulk Sender: caml-list-request@inria.fr X-no-archive: yes List-Id: List-Help: List-Subscribe: List-Unsubscribe: List-Post: List-Owner: List-Archive: Archived-At: --000000000000dd82cc05ddf08d3d Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable - We apologize if you receive multiple copies of this CFC. - We appreciate your help to forward this CFC to your friends & email lists. Dear colleagues, We are in the process of coming up with a volume titled *=E2=80=9CApplicati= ons of Remote Sensing Techniques for Sustainable Security =E2=80=9D *to be publish= ed 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 *May, 10th, 2022 (Extended Deadline)* *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 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 --000000000000dd82cc05ddf08d3d Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable

-= =C2=A0=C2=A0We apologize if you receive multiple copies of this= =C2=A0CFC.

-<= /span>=C2=A0=C2=A0We appreciate your help to forward this=C2=A0CFC to yo= ur friends & email lists.


Dear colleagues,

<= div>We are in the process of coming up with a volume titled=C2=A0=E2=80= =9CApplications of Remote Sensing Techniques for Sustainable Security =E2= =80=9D=C2=A0to be published by Springer (proposal is initially communic= ated, 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:
- Autho= r List
- Chapter Title
- Abstract (between 2 and 6 sentences)
The = last deadline to submit your short abstract directly at=C2=A0lahby@ieee.org=C2=A0is=C2=A0May, 10th, 2022 (Extended Deadline)
=
SCOPE:
With the advent of the big data era in= =C2=A0remote=C2=A0sensing,=C2=A0artificial=C2=A0intelligence=C2=A0(AI) has = spread to almost every corner of various=C2=A0remote=C2=A0sensing=C2=A0appl= ications. In many cases, the characteristics of=C2=A0remote=C2=A0sensing=C2= =A0big data, such as multi-source, multi-scale, high-dimensional, dynamic s= tate, 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=C2=A0remote=C2=A0sensing=C2=A0im= age processing tasks (object detection, segmentation, etc.) and some invers= e=C2=A0remote=C2=A0sensing=C2=A0tasks (atmosphere, vegetation, etc.). Using= large labeled datasets, we can often make very accurate predictions on=C2= =A0remote=C2=A0sensing=C2=A0data.
However, current data-driven AI has no= t provided us with clear physical or cognitive meaning of=C2=A0remote=C2=A0= sensing=C2=A0data's internal features and representations. Most deep le= arning techniques do not reveal how data features take effect and why predi= ctions are made.=C2=A0Remote=C2=A0sensing=C2=A0data has exacerbated the pro= blem of opacity and inexplicability of current AI. It becomes a barrier bet= ween the latest AI techniques and some=C2=A0remote=C2=A0sensing=C2=A0applic= ations. Many scientists in hydrological=C2=A0remote=C2=A0sensing, atmospher= ic=C2=A0remote=C2=A0sensing, oceanic=C2=A0remote=C2=A0sensing, etc. do not = even believe the results of deep learning predictions, as these communities= are more inclined to believe models with clear physical meaning.=C2=A0
= This forthcoming book seeks contributions to remote=C2=A0sensing=C2=A0data.= In particular, we are looking for research papers on=C2=A0applications of = remote sensing in many fields of smart cities such as smart transportation,= smart agriculture, and smart Environment.

NB:= =C2=A0There are no submission or acceptance fees for manuscripts submit= ted 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 R= emote Sensing
  • Chapter 1. Remote S= ensing Techniques State-of-the-Art
  • Chapt= er 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: Re= mote sensing and technologies
    • Cha= pter 9. Artificial Intelligence for Enabled Remote Sensing
    • Chapter 10. machine learning for Enabled Remote Sensing<= /li>
    • Chapter 11. Deep Learning for Enabled Re= mote Sensing
    • Chapter 12. XAI for Enabled= Remote Sensing
    • Chapter 13. Big Data for= Enabled Remote Sensing
    • Chapter 14. Bloc= kchain for Enabled remote sensing
    Part =C2=A04: =C2=A0Futuristi= c Ideas
    • Chapter 15. Futuristic Id= eas for Remote sensing
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Best regards

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