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mail-ej1-f48.google.com with SMTP id k23so25979133ejd.3 for ; Mon, 18 Apr 2022 02:56:46 -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=joiKUE25M0zn8uyV3ibhUOMAd5lb59B7nmhszG0JlwA=; b=Hww0+70mDK41SzpcFKeCNproqH/fLvJgRSgDaiFsw9pMOuIfTywF7LA/b3I6jLXsbc dSDe/v53KSRnX6HOvT7WpUhqhdLyCKGEdKBVuj8FOsRR8r24PVB6YLw1axQBN0UyM9IF wJjEbqIfFL/AI6PGt2ewMnUcoOHldqDlIGYSU= 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=joiKUE25M0zn8uyV3ibhUOMAd5lb59B7nmhszG0JlwA=; b=J7kogDyfWGUiLEKNV7o9WgXAObO/4E/wnkC02X5datnKqhgP5rBtswgOeu2/gzAhQQ x9LkmCxAGBoHHLrEPF45xNK3fobQqUZomG6KkPzPiYBLM/DhpNW+2/ESaaUbervfFYLG 9F5neQ+GoM8G8abMh2gIWo+uMUZq8VICS1AsvS4SOvNGYiSCpyg3nScs3QE2+NgloQtS /MSOuI2r3mQD6U7RZR/ZPhs/ik8/2SZ4sV20Mo0SyFQN+5llg7/BDzWQQcSgvyEiB6/8 +Yma9rMe9OYI/9YaQ+oL08ekOmKL3v1rHTlJFtCS/2508aT0ckK4pgxrntPxNNUyMTrN e3yA== X-Gm-Message-State: AOAM531v9NdA+eh2n6bsfZLCaYM0mVbNl26Prrj4nNKSF+OoBGz97glS PgHQVw4QDy0m9eE2+N21LIJeVIjdjVU01RShG5Ksaq/jUFDs7A== X-Google-Smtp-Source: ABdhPJw19dF/EjWemHWb5OxJaJwwOCMLDbIvo4QYXN0qhU6Fki/AW9p7jJ9tFoEllyntEUiXq2c8/z9Htytd2KtbjIY= X-Received: by 2002:a17:906:7944:b0:6da:b834:2f3e with SMTP id l4-20020a170906794400b006dab8342f3emr8522292ejo.353.1650275805823; Mon, 18 Apr 2022 02:56:45 -0700 (PDT) MIME-Version: 1.0 From: mohamed Lahby Date: Mon, 18 Apr 2022 09:57:42 +0000 Message-ID: To: caml-list@inria.fr Content-Type: multipart/alternative; boundary="000000000000301f4105dceac73d" 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: 18742 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: --000000000000301f4105dceac73d Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable 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 --000000000000301f4105dceac73d Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Dear colleagues,

We are = in the process of coming up with a volume titled=C2=A0=E2=80=9CApplicati= ons of remote sensing techniques for Sustainable Security In Smart 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. The full ch= apter 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=C2=A0lahby@ieee.org=C2=A0is= =C2=A0April, 20th, 2022

SCOPE:
With the advent of the big data era in=C2=A0re= mote=C2=A0sensing,=C2=A0artificial=C2=A0intelligence=C2=A0(AI) has spread t= o almost every corner of various=C2=A0remote=C2=A0sensing=C2=A0applications= . In many cases, the characteristics of=C2=A0remote=C2=A0sensing=C2=A0big d= ata, such as multi-source, multi-scale, high-dimensional, dynamic state, is= omeric, and non-linear features, etc., are well learned by advanced AI algo= rithms. Data-driven methods, especially deep learning models, have achieved= state-of-the-art results for most=C2=A0remote=C2=A0sensing=C2=A0image proc= essing tasks (object detection, segmentation, etc.) and some inverse=C2=A0r= emote=C2=A0sensing=C2=A0tasks (atmosphere, vegetation, etc.). Using large l= abeled datasets, we can often make very accurate predictions on=C2=A0remote= =C2=A0sensing=C2=A0data.
However, current data-driven AI has not provide= d us with clear physical or cognitive meaning of=C2=A0remote=C2=A0sensing= =C2=A0data's internal features and representations. Most deep learning = techniques do not reveal how data features take effect and why predictions = are made.=C2=A0Remote=C2=A0sensing=C2=A0data has exacerbated the problem of= opacity and inexplicability of current AI. It becomes a barrier between th= e 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=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 p= articular, we are looking for research papers on=C2=A0applications of remot= e sensing in many field of smart cities such as smart transportation, smart= agriculture, and smart Environment.

NB:=C2=A0<= /b>There are no submission or acceptance fees for manuscripts submitted to = this book for publication

The tentative structure of the book (but a= re not limited to the following Parts) is mentioned below:.
<= br>
Part 1:=C2=A0Theoretical and Applied Aspects of Remote= Sensing and Smart cities
Part 2:=C2=A0Remote Sensing for = Smart Agriculture Security
Part 3:=C2=A0Remote Sensing for= Smart Transportation Security
Part 4:=C2=A0Remote Sensing= for Smart Environment security
Part=C2=A0 5:=C2=A0 Artificial=C2= =A0Intelligence=C2=A0for=C2=A0Remote=C2=A0Sensing
Part=C2=A0 6= :=C2=A0=C2=A0Big Data=C2=A0for=C2=A0Remote=C2=A0Sensing
<= b>Part=C2=A0 7:=C2=A0=C2=A0Futuristic Ideas


Best regards
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