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2002:a25:dcd4:0:b0:670:ccbf:f10 with SMTP id y203-20020a25dcd4000000b00670ccbf0f10mr418114ybe.126.1658502633787; Fri, 22 Jul 2022 08:10:33 -0700 (PDT) MIME-Version: 1.0 From: mohamed Lahby Date: Fri, 22 Jul 2022 16:10:23 +0100 Message-ID: To: caml-list@inria.fr Content-Type: multipart/alternative; boundary="00000000000058c0a505e4663cd2" Subject: [Caml-list] [Free Springer Book on Remote Sensing] Few days left to submit your short abstract!! Deadline: July 25th,2022 Reply-To: mohamed Lahby X-Loop: caml-list@inria.fr X-Sequence: 18826 Errors-To: 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: --00000000000058c0a505e4663cd2 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= . *=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3DCall for chapters=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D* Dear colleague, 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 publ= ished 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 lahby@ieee.org is *July 25th, 2022 (Firm 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 task= s (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 M. LAHBY --00000000000058c0a505e4663cd2 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
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=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3DCall for chapters=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D

Dear colleague,

We are in the process= of coming up with a volume titled=C2=A0=E2=80=9CApplications of=C2=A0Remote<= /span>=C2=A0Sensing=C2=A0Techniques for Sustainable Security =E2=80=9D=C2= =A0to be published by Springer (proposal is initially communicated, awa= iting for final approval) at=C2=A0the end of 202= 2.

We cordially invit= e 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 Titl= e
- Abstract (between 2 and 6 sentences)
The last deadline to submit = your short abstract directly at=C2=A0lahby@ieee.org=C2=A0is=C2=A0July 25th, 2022 (Firm Deadline)

SCO= PE:
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=A0applications. In many = cases, the characteristics of=C2=A0remote=C2=A0sensing=C2=A0big data, s= uch 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=C2=A0remote=C2=A0sensing=C2=A0image proces= sing tasks (object detection, segmentation, etc.) and some inverse=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 not p= rovided us with clear physical or cognitive meaning of=C2=A0remote=C2=A0<= span style=3D"color:rgb(60,64,67);background-color:rgb(253,226,147)">sensin= g=C2=A0data's internal features and representations. Most deep l= earning techniques do not reveal how data features take effect and why pred= ictions are made.=C2=A0Remote=C2=A0sensing=C2=A0data has exacerbated th= e problem of opacity and inexplicability of current AI. It becomes a barrie= r between the latest AI techniques and some=C2=A0remote=C2=A0sensing= =C2=A0applications. Many scientists in hydrological=C2=A0remote=C2=A0sensing, atmospheric=C2=A0remote=C2=A0sensing, oceanic=C2=A0remote=C2= =A0se= nsing, etc. do not even believe the results of deep learning predict= ions, as these communities are more inclined to believe models with clear p= hysical meaning.=C2=A0
This forthcoming book seeks contributions to=C2= =A0re= mote=C2=A0sensing=C2=A0data. In particular, we are looking for res= earch papers on=C2=A0applications of=C2=A0remote=C2=A0sensing=C2=A0in m= any fields of smart cities such as smart transportation, smart agriculture,= and smart Environment.

NB:=C2=A0There are = no submission or acceptance fees for manuscripts submitted to this book for= publication

The tentative structure of the book=C2=A0(but are no= t limited to the following Parts) is mentioned below:.

Part 1: Theoretical and Applied Aspects of=C2=A0Remote=C2=A0Sensing
  • Chapter 1.=C2= =A0Re= mote=C2=A0Sensing=C2=A0Techniques State-of-the-Art
  • Chapter 2. Hyperspectral=C2=A0remote=C2=A0sensing= =C2=A0applications: State-of-the-Art
  • Cha= pter 3. Smart cities: State-of-the-Art
Part 2:=C2=A0Remote=C2= =A0se= nsing=C2=A0and 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:=C2=A0Remote=C2=A0sensing=C2=A0and technologies
  • Chapter 9. Artificial Intelligence for Enabled=C2=A0Remote=C2=A0Sensing<= /span>
  • Chapter 10. machine learning for = Enabled=C2=A0Remote=C2=A0Sensing
  • Cha= pter 11. Deep Learning for Enabled=C2=A0Remote=C2=A0Sensing
  • Chapter 12. XAI for Enabled=C2=A0Remote=C2=A0Sensing
  • Chapter 13. Big Data for Enabled= =C2=A0Remote=C2=A0Sensing
  • Chapter 14= . Blockchain for Enabled=C2=A0remote=C2=A0sensing
Part =C2= =A04: =C2=A0Futuristic Ideas
  • Chap= ter 15. Futuristic Ideas for=C2=A0Remote=C2=A0sensing
Best regards
M. LAHBY
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