Date of Award

Spring 2-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Computer Science (PHDCS)

First Advisor

Austin O'Brien

Second Advisor

Mark Spanier

Third Advisor

Neal Pastick

Abstract

This research explores the dynamics of land cover classification using remote sensing time series data, emphasizing the need for efficient monitoring and resource management on Earth’s surface. With advancements in computational power and analytical methods, deep learning techniques, including Convolutional Neural Networks (CNNs) and Transformer neural networks, have emerged as state-of-the-art approaches for automating and operationalizing land cover classification at regional and global scales. This study introduces two distinct methodologies for land cover time series classification: Spatial Recognition and Temporal Alignment (SpaRTA) and Land Cover Artificial Mapping System (LCAMS). SpaRTA employs a U-Net architecture coupled with a Transformer encoder for effectively generating annual classifications and ensuring temporal alignment, outperforming comparable methods in terms of both validation and independent test datasets. LCAMS builds upon SpaRTA by integrating regional model fine-tuning, ensemble modeling, change detection, and multitask learning to enhance its scalability and generalization capabilities. Key findings indicate that both methodologies achieve high levels of temporal and spatial consistency, comparable to legacy products like the National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP), while expediting product generation and reducing latency. Despite their strengths, challenges remain, including the inherent difficulties of inter-annual consistency and the reliance on specific data sources, which may limit performance. Future work should focus on improving model architectures, incorporating intra-annual information, and enhancing forecasting methods. Ultimately, this research demonstrates the significant potential of deep learning in automating land cover analysis, paving the way for scalable solutions in environmental monitoring and resource sustainability.

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