Advances in Machine Learning and Image Analysis for GeoAI

Prasad, Saurabh (EDT)/ Chanussot, Jocelyn (EDT)/ Li, Jun (EDT)

Elsevier - Health Sciences Division（2024/04発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 364 p.
• 言語 ENG
• 商品コード 9780443190773
• DDC分類 526.0285

Full Description

Advances in Machine Learning and Image Analysis for GeoAI presents recent advances in applications and algorithms that are at the intersection of Geospatial imaging and Artificial Intelligence (GeoAI). The book covers algorithmic advances in geospatial image analysis, sensor fusion across modalities, few-shot open-set recognition, explainable AI for Earth Observations, self-supervised learning, image superresolution, Visual Question Answering, and spectral unmixing, among other topics.
This book offers a comprehensive resource for graduate students, researchers, and practitioners in the area of geospatial image analysis. It provides detailed descriptions of the latest techniques, best practices, and insights essential for implementing deep learning strategies in GeoAI research and applications.

Contents

1. Introduction
2. Deep Learning for Super-resolution in Remote Sensing
3. Few-Shot Open-Set Recognition of Hyperspectral Images
4. Deep Semantic Segmentation Networks for GeoAI: Impact of Design Choices on Segmentation Performance
5. Estimation of Class Priors for Improving Classification Accuracy
6. Benchmarking and end-to-end considerations for GeoAI-enabled decision making
7. Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities
8. Self-supervised Contrastive Learning for Wildfire Detection: Utility and Limitations
9. Multi-Modal Deep Learning for GeoAI
10. The Power of Voting - Ensemble Learning in Remote Sensing
11. Language and Remote Sensing
12. Spectral Unmixing for Geospatial Image Analysis
13. Applying GeoAI for Effective Large-Scale Wetland Monitoring
14. Leveraging ML approaches for scaling climate data in an atmospheric urban digital twin framework