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ESMTP/TLS/ECDHE-RSA-AES256-GCM-SHA384; 10 Apr 2022 23:47:44 +0200 Received: by mail-ej1-f51.google.com with SMTP id r13so27330428ejd.5 for ; Sun, 10 Apr 2022 14:47:43 -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=8rBz9vlR5v+NevrBkRZcfwe50+KVLBhwPABw2uk+io8=; b=LDiMqFlUNSrLO+wxCg1ASjSNR8W3y5Ysk+q+FAeKyyTJs/JXwaeFqc/68u0qYCPLAH o4cMFiWXZA1r7Gv88WSJW7kufxV+G4Sd44Nfn6cw4KVsmAthoBAqC1Iv4Ux8v1Q7E3sH IzNlVCvQFWV8USQbaINiGBMV1a015vh6sWc9s= 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=8rBz9vlR5v+NevrBkRZcfwe50+KVLBhwPABw2uk+io8=; b=1efhtydvCZ6UxeHmd6C8A63RJYE8aVhY1ZLGkoVk3qOYMJnXDTz9Gd41ItQPEH5tK2 KNTOiTJNAZBohms4Op9vXOvKiHpyXA3TiuBuXTsmuRBXmm1Eq5T1nwOA1XJUmDZIrXHd D9sFY97y+tXVCVpouZFNBsF1TvB8/I8rDpelXl9vfFkVEiPlGPbTtl9OT/bmgVv+kB30 lcAJpcHPoJXNKtYc/kAOXwH3rtxkhbc0Icrq/z+F/wIsy8jTy1EGknTlVWXfG39YS57O 30TT4SHdjrcDEneYkc3Uxym4I4MIEbvjxdNxKmAKGJM3MqptvJOLsjZvN4/ZXlzlWE+m /U9g== X-Gm-Message-State: AOAM531n0gLowBVwKCbX9V+onr8opTlBbNxgO8ykztI9WdIhaHwY+2fR Ht9iczHx8XYycznhM8O5fpsJ+iU7Zt3Yn4aX+nzADRDqIRoGxw== X-Google-Smtp-Source: ABdhPJznFb7OB2ioC3YmUZ46JDnfS1BAGmQEU+Un154B9cpTHQIHH2jXt4l0gKD3Rodsb3F8foJj4AZks0qSlchkmz8= X-Received: by 2002:a17:906:1f11:b0:685:d50e:3bf9 with SMTP id w17-20020a1709061f1100b00685d50e3bf9mr28758544ejj.275.1649627263102; Sun, 10 Apr 2022 14:47:43 -0700 (PDT) MIME-Version: 1.0 From: mohamed Lahby Date: Sun, 10 Apr 2022 21:48:13 +0000 Message-ID: To: caml-list@inria.fr Content-Type: multipart/alternative; boundary="00000000000007953105dc53c725" 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 Reply-To: mohamed Lahby X-Loop: caml-list@inria.fr X-Sequence: 18738 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: --00000000000007953105dc53c725 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 In Smart cities =E2=80= =9D *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 citie= s *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 --00000000000007953105dc53c725 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 In Sm= art cities=C2=A0=E2=80=9D=C2=A0to 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. T= he full chapter is due later this year but for now, I will just need the fo= llowing:
- Author 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=A0April, 20th, 2022
<= b>
SCOPE:
With the advent of the big data era i= n=C2=A0remote=C2=A0sensing,=C2=A0artificial=C2=A0intelligence=C2=A0(AI) has= spread to almost every corner of various=C2=A0remote=C2=A0sensing=C2=A0app= lications. In many cases, the characteristics of=C2=A0remote=C2=A0sensing= =C2=A0big data, such as multi-source, multi-scale, high-dimensional, dynami= c state, isomeric, and non-linear features, etc., are well learned by advan= ced AI algorithms. Data-driven methods, especially deep learning models, ha= ve achieved state-of-the-art results for most=C2=A0remote=C2=A0sensing=C2= =A0image processing tasks (object detection, segmentation, etc.) and some i= nverse=C2=A0remote=C2=A0sensing=C2=A0tasks (atmosphere, vegetation, etc.). = Using large labeled datasets, we can often make very accurate predictions o= n=C2=A0remote=C2=A0sensing=C2=A0data.
However, current data-driven AI ha= s not provided us with clear physical or cognitive meaning of=C2=A0remote= =C2=A0sensing=C2=A0data's internal features and representations. Most d= eep learning techniques do not reveal how data features take effect and why= predictions are made.=C2=A0Remote=C2=A0sensing=C2=A0data has exacerbated t= he problem of opacity and inexplicability of current AI. It becomes a barri= er between the latest AI techniques and some=C2=A0remote=C2=A0sensing=C2=A0= applications. Many scientists in hydrological=C2=A0remote=C2=A0sensing, atm= ospheric=C2=A0remote=C2=A0sensing, oceanic=C2=A0remote=C2=A0sensing, etc. d= o not even believe the results of deep learning predictions, as these commu= nities 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=A0applicat= ions of remote sensing in many field of smart cities such as smart transpor= tation, smart agriculture, and smart Environment.


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