Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences
暫譯: 地球科學的深度學習：遙感、氣候科學與地球科學的綜合方法

Camps-Valls, Gustau, Tuia, Devis, Zhu, Xiao Xiang

Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences
  • 出版商: Wiley
  • 出版日期: 2021-08-16
  • 定價: $4,200
  • 售價: 9.5 折 $3,990
  • 語言: 英文
  • 頁數: 432
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1119646146
  • ISBN-13: 9781119646143
  • 相關分類: DeepLearning

    • 立即出貨 (庫存=1)

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商品描述

Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices in the field

Deep learning is a fundamental technique in modern artificial intelligence and is being applied to disciplines across the scientific spectrum. Earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferate broad spread. Deep Learning for the Earth Sciences delivers a perspective and unique treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described within in their own research.

The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of:

  • An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation
  • An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration
  • Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation
  • An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations

    Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

    • 商品描述(中文翻譯)

    探索這本由四位領域內的領導者所撰寫的深度學習在地球科學領域的深刻探討

    深度學習是現代人工智慧中的一項基本技術，並且正在應用於科學範疇的各個學科。地球科學也不例外。然而，深度學習與地球科學之間的聯繫最近才進入學術課程，因此尚未廣泛普及。《Deep Learning for the Earth Sciences》提供了一個獨特的視角，介紹了快速熟悉將深度學習技術應用於地球科學所需的概念、技能和實踐。本書使讀者準備好在自己的研究中使用書中描述的技術和原則。

    這本書的傑出編輯還包含了資源，解釋並提供新的想法和建議，特別適合那些參與高級研究教育或尋求博士論文方向的人。讀者還將受益於以下內容的納入：
    - 深度學習在分類目的上的介紹，包括圖像分割和編碼先驗的進展、異常檢測和目標檢測，以及領域適應
    - 學習表示和無監督深度學習的探索，包括深度學習圖像融合、圖像檢索、匹配和共同註冊
    - 實用的回歸、擬合、參數檢索、預測和插值的討論
    - 物理感知深度學習模型的檢視，包括複雜代碼的模擬和模型參數化

    《Deep Learning for the Earth Sciences》非常適合地球科學、圖像處理、遙感、電機工程、計算機科學和機器學習領域的博士生和研究人員，並且也將在機器學習和模式識別研究者、工程師和科學家的圖書館中佔有一席之地。

    作者簡介

    Gustau Camps-Valls is Professor of Electrical Engineering and Lead Researcher inthe Image Processing Laboratory (IPL) at the Universitat de València. His interests include development of statistical learning, mainly kernel machines and neural networks, for Earth sciences, from remote sensing to geoscience data analysis. Models efficiency and accuracy but also interpretability, consistency and causal discovery are driving his agenda on AI for Earth and climate.

    Devis Tuia, PhD, is Associate Professor at the Ecole Polytechnique FÃ(c)dÃ(c)rale de Lausanne (EPFL). He leads the Environmental Computational Science and Earth Observation laboratory, which focuses on the processing of Earth observation data with computational methods to advance Environmental science.

    Xiao Xiang Zhu is Professor of Data Science in Earth Observation and Director of the Munich AI Future Lab AI4EO at the Technical University of Munich and heads the Department EO Data Science at the German Aerospace Center. Her lab develops innovative machine learning methods and big data analytics solutions to extract large scale geo-information from big Earth observation data, aiming at tackling societal grand challenges, e.g. Urbanization, UNÂs SDGs and Climate Change.

    Markus Reichstein is Director of the Biogeochemical Integration Department at the Max-Planck-Institute for Biogeochemistry and Professor for Global Geoecology at the University of Jena. His main research interests include the response and feedback of ecosystems (vegetation and soils) to climatic variability with a Earth system perspective, considering coupled carbon, water and nutrient cycles. He has been tackling these topics with applied statistical learning for more than 15 years.

    作者簡介(中文翻譯)

    Gustau Camps-Valls 是瓦倫西亞大學電機工程教授及影像處理實驗室 (IPL) 的首席研究員。他的研究興趣包括統計學習的發展，主要是核機器和神經網絡，應用於地球科學，從遙感到地球科學數據分析。他的研究議程專注於人工智慧在地球與氣候領域的模型效率、準確性、可解釋性、一致性及因果發現。

    Devis Tuia, PhD, 是洛桑聯邦理工學院 (EPFL) 的副教授。他領導環境計算科學與地球觀測實驗室，專注於利用計算方法處理地球觀測數據，以推進環境科學。

    Xiao Xiang Zhu 是慕尼黑工業大學地球觀測數據科學教授及慕尼黑人工智慧未來實驗室 AI4EO 的主任，同時負責德國航空航天中心的地球觀測數據科學部門。她的實驗室開發創新的機器學習方法和大數據分析解決方案，旨在從大型地球觀測數據中提取大規模地理資訊，以應對社會重大挑戰，例如城市化、聯合國可持續發展目標 (SDGs) 和氣候變遷。

    Markus Reichstein 是馬克斯·普朗克生物地球化學研究所生物地球化學整合部門的主任，以及耶拿大學全球生態學的教授。他的主要研究興趣包括生態系統（植被和土壤）對氣候變異的反應和反饋，從地球系統的角度考慮耦合的碳、水和養分循環。他已經用應用統計學習研究這些主題超過15年。

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