Advances in Machine Learning and Image Analysis for Geoai

ISBN-13: 9780443190773 / Angielski

Saurabh Prasad; Jocelyn Chanussot; Jun Li

Spis treści

1. Introduction - GeoAI: Challenges and Opportunities Section I: Unsupervised, Supervised and Semi-Supervised Learning 2. A Review of Deep Neural Networks for Robust Analysis of Wide-Scale Geospatial Imagery 3. Advanced Deep Neural Network Architectures for GeoAI 4. Advances in Change and Anomaly Detection for Geospatial Imagery Data 5. Advances in Spectral Unmixing for Geospatial Image Analysis 6. Advances in Deep Semantic Segmentation for Remote Sensing 7. Compressive Deep Learning for Remote Sensing 8. Semi-Supervised and Active Deep Learning - Geospatial Image Analysis with Limited Ground Truth 9. Visual Question Answering for Remotely Sensed Imagery Section II: Multi-Modal GeoAI 10. Generative Adversarial Networks for Transfer Learning, Data Augmentation and Super-Resolution 11. Data Fusion Networks for Analysis of Multi-Modal Imagery Acquired from Multiple Heterogenous Sensors 12. Advances in Transfer Learning for Multi-Sensor, Multi-Temporal Earth Science Data 13. Advances in Image Super-Resolution for Analysis of Multi-Scale Earth Science Data 14. Harmonizing Contextual and Deep Features for Multi-Modal Geospatial Image Analysis Section III: GeoAI in Practice 15. GeoAI for Earth Science - A NASA and ESA Perspective 16. GeoAI on the Cloud - State-of-the-Art Operational GeoAI Solutions on the Cloud for Earth Science 17. Deep Learning and Earth Observations in Support of Climate Science and Sustainable Development 18. Advances in Multi-Modal Machine Learning with Applications to Precision Agriculture