Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences

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
  • 售價: $4,200
  • 貴賓價: 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)

    探索地球科學領域中深度學習的深入研究，來自該領域的四位領先專家。

    深度學習是現代人工智慧的基本技術，並被應用於科學領域的各個學科。地球科學也不例外。然而，深度學習與地球科學之間的聯繫直到最近才進入學術課程，因此尚未廣泛普及。《地球科學的深度學習》提供了一個獨特的觀點和研究方法，幫助讀者快速熟悉將深度學習技術應用於地球科學的概念、技能和實踐。本書使讀者能夠準備好在自己的研究中使用所描述的技術和原則。

    傑出的編輯還提供了解釋和提供新想法和建議的資源，對於從事高級研究教育或尋求博士論文方向的人特別有用。讀者還將從以下內容中受益：

    - 用於分類目的的深度學習介紹，包括圖像分割和編碼先驗、異常檢測和目標檢測以及領域適應的進展。
    - 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、圖像檢索和匹配以及共註冊。
    - 實用的回歸、擬合、參數檢索、預測和插值討論。
    - 物理感知的深度學習模型探討，包括複雜代碼的仿真和模型參數化。

    非常適合地球科學、圖像處理、遙感、電氣工程和計算機科學以及機器學習領域的博士生和研究人員，此書也將成為機器學習和模式識別研究人員、工程師和科學家的圖書館收藏。

    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學教授，也是影像處理實驗室(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，應用於從遙感到地球科學數據分析的領域。他的AI研究議程主要關注模型的效率和準確性，以及可解釋性、一致性和因果發現在地球和氣候領域的應用。

    Devis Tuia博士是洛桑聯邦理工學院(EPFL)的副教授。他領導著環境計算科學和地球觀測實驗室，該實驗室專注於利用計算方法處理地球觀測數據，推進環境科學的發展。

    Xiao Xiang Zhu是慕尼黑工業大學的地球觀測數據科學教授，也是慕尼黑AI未來實驗室AI4EO的主任，並領導德國航空航天中心的地球觀測數據科學部門。她的實驗室致力於開發創新的機器學習方法和大數據分析解決方案，從大規模地球觀測數據中提取地理信息，旨在應對城市化、聯合國可持續發展目標和氣候變化等重大社會挑戰。

    Markus Reichstein是馬克斯普朗克生物地球化學研究所的生物地球化學整合部門主任，也是耶拿大學的全球地球生態學教授。他的主要研究興趣包括生態系統（植被和土壤）對氣候變異的響應和反饋，以地球系統的角度考慮碳、水和營養循環的耦合。他已經使用應用統計學習研究這些主題超過15年。

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