Advances in Machine Learning and Image Analysis for GeoAI

Advances in Machine Learning and Image Analysis for GeoAI provides state-of-the-art machine learning and signal processing techniques for a comprehensive collection of geospatial sensors and sensing platforms. The book covers supervised, semi-supervised and unsupervised geospatial image analysis, sensor fusion across modalities, image super-resolution, transfer learning across sensors and time-points, and spectral unmixing, among other topics. The chapters in these thematic areas cover a variety of algorithmic frameworks such as variants of convolutional neural networks, graph convolutional networks, multi-stream networks, Bayesian networks, generative adversarial networks, transformers, and more. This book provides graduate students, researchers, and practitioners in the area of signal processing and geospatial image analysis with the latest techniques to implement deep learning strategies in their research.

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