Explainable AI for Earth Observation Data Analysis: Applications, Opportunities, and Challenges
暫譯: 可解釋的人工智慧在地球觀測數據分析中的應用、機會與挑戰

Pv, Arun, Chanussot, Jocelyn, Mohan, B. Krishna

Explainable AI for Earth Observation Data Analysis: Applications, Opportunities, and Challenges
  • 出版商: CRC
  • 出版日期: 2025-11-04
  • 售價: $5,500
  • 貴賓價: 9.5 折 $5,225
  • 語言: 英文
  • 頁數: 280
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1032980966
  • ISBN-13: 9781032980966
  • 相關分類: DeepLearning

    • 海外代購書籍(需單獨結帳)

商品描述

The role of artificial intelligence is crucial in the domain of Earth Observation (EO) data analysis. Deep learning-based approaches have improved accuracy, but they have affected the reliability and transparency of EO data. It is critical to improve the explainability of EO data analysis algorithms and complex deep learning models to ensure the quality of spatial decisions. This book discusses the various advancements in Explainable AI and investigates their suitability for various EO data analyses offering best practices for implementing algorithms that facilitate big and efficient data processing. It lays the foundation of Explainable EO and helps readers build trustworthy, secure, and robust EO systems.

Features

  • Examines explainability of algorithms from the aspect of generalizability and reliability.
  • Reviews state-of-the-art explainability strategies related to the preprocessing algorithms.
  • Provides explanations for specific evaluation metrics of various EO data processing and preprocessing algorithms.
  • Discusses explainable ante-hoc and post-hoc approaches for EO data analysis.
  • Serves as a foundational reference for developing future EO data processing strategies.
  • Addresses the key challenges in making EO data processing algorithms interpretable and offers insights for the future of explainable EO data processing.

This book is intended for graduate students, researchers and academics in computer and data science, machine learning, and image processing, as well as professionals in geospatial data science using GIS and remote sensing in Earth and environmental sciences.

商品描述(中文翻譯)

人工智慧在地球觀測（EO）數據分析領域中扮演著至關重要的角色。基於深度學習的方法提高了準確性，但也影響了EO數據的可靠性和透明度。改善EO數據分析算法和複雜深度學習模型的可解釋性對於確保空間決策的質量至關重要。本書討論了可解釋人工智慧的各種進展，並調查其在各種EO數據分析中的適用性，提供實施促進大規模和高效數據處理算法的最佳實踐。它為可解釋的EO奠定了基礎，幫助讀者建立可信、安全和穩健的EO系統。

特色
- 從可泛化性和可靠性的角度檢視算法的可解釋性。
- 回顧與預處理算法相關的最先進可解釋性策略。
- 提供各種EO數據處理和預處理算法的特定評估指標的解釋。
- 討論可解釋的事前（ante-hoc）和事後（post-hoc）方法在EO數據分析中的應用。
- 作為未來EO數據處理策略開發的基礎參考。
- 解決使EO數據處理算法可解釋的關鍵挑戰，並為可解釋的EO數據處理的未來提供見解。

本書適合計算機與數據科學、機器學習和影像處理的研究生、研究人員和學者，以及在地球與環境科學中使用GIS和遙感的地理空間數據科學專業人士。

作者簡介

Arun PV is Assistant Professor at Indian Institute of Information Technology, Sricity, Chittoor, India. He leads the spatial data analytics and machine intelligence group. He has a PhD from IIT Bombay and has expertise in deep learning and remote sensing data analytics. He has over 15 years of research experience and has published over 70 publications in international journals and conference proceedings.

Jocelyn Chanussot is Professor of Signal and Image Processing at the Grenoble Institute of Technology in Grenoble, France. Chanussot was nominated as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2012 for his contributions to data fusion and image processing for remote sensing where he currently serves as an Editor-in-Chief

B Krishna Mohan is Professor at the Indian Institute of Technology, Bombay, India. From 2016 to 2019 he was the Head of the Centre and Institute's Chair Professor. He has authored over 150 publications in journals, book chapters, and conference proceedings. He also has led over 45 national and international sponsored projects. Prof. Mohan is the recipient of the Indian Society of Remote Sensing National Geospatial Award for Excellence in 2012.

D. Nagesh Kumar has been Professor in the Department of Civil Engineering, at the Indian Institute of Science, Bangalore, India since May 2002. He is a Fellow of the Indian Academy of Sciences, Bangalore. He is the co-author of 8 books and has published more than 220 papers including 131 in peer reviewed journals. He is the Editor-in-Chief of a journal on climate change and water and the Associate Editor for a journal on Hydraulic Engineering.

Alok Porwal is Professor at the Indian Institute of Technology, Bombay, India. He specializes in Earth Observation data processing and analysis. From 2021-2024 he was the Head of the Centre and the Institute Chair Professor. He is currently an Editor of an academic journal and has authored over 200 publications in journals, book chapters, and conference proceedings. He has also led over 20 national and international sponsored projects. He is the recipient of SP Sukhatme Award for Excellence.

作者簡介(中文翻譯)

Arun PV 是印度信息技術學院（Indian Institute of Information Technology, Sricity, Chittoor, India）的助理教授。他領導空間數據分析和機器智能小組。他擁有印度理工學院孟買分校（IIT Bombay）的博士學位，專長於深度學習和遙感數據分析。他擁有超過15年的研究經驗，並在國際期刊和會議論文集中發表了70多篇論文。

Jocelyn Chanussot 是法國格勒諾布爾科技學院（Grenoble Institute of Technology）信號與影像處理教授。Chanussot於2012年因其在數據融合和遙感影像處理方面的貢獻被提名為電氣和電子工程師學會（IEEE）院士，目前擔任該學會的主編。

B Krishna Mohan 是印度理工學院孟買分校（Indian Institute of Technology, Bombay, India）的教授。從2016年到2019年，他擔任該中心的主任及學院的講座教授。他在期刊、書籍章節和會議論文中發表了超過150篇論文，並主導了超過45個國內和國際贊助的項目。Mohan教授於2012年獲得印度遙感學會（Indian Society of Remote Sensing）國家地理空間卓越獎。

D. Nagesh Kumar 自2002年5月以來一直是印度科學院（Indian Institute of Science, Bangalore, India）土木工程系的教授。他是班加羅爾印度科學院的院士。他是8本書的共同作者，並發表了超過220篇論文，其中131篇為同行評審期刊論文。他是一本關於氣候變化和水資源的期刊的主編，並擔任一本水利工程期刊的副編輯。

Alok Porwal 是印度理工學院孟買分校（Indian Institute of Technology, Bombay, India）的教授。他專注於地球觀測數據的處理和分析。從2021年到2024年，他擔任該中心的主任及學院的講座教授。他目前是一本學術期刊的編輯，並在期刊、書籍章節和會議論文中發表了超過200篇論文。他還主導了超過20個國內和國際贊助的項目。他是SP Sukhatme卓越獎的獲得者。

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