Cropland PAtteRNS: Parallel Dimensional Attention Networks and Attention to Dataset Disparity for Crop Segmentation in Satellite Imagery Time Series Data

arXiv:2609.38165 · cs.CV, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/JoeMetc/CroplandPAtteRNS

Project page: https://www.esa-sen4cap.org

Terminology

• Transformer — 91× in this paper · explained in 2 episodes
• Ablation — 11× in this paper · explained in 1 episode
• Accuracy — 11× in this paper · explained in 1 episode
• Attention Mechanism — 9× in this paper · explained in 3 episodes
• Ablation study — 6× in this paper · explained in 1 episode
• Aggregation — 6× in this paper · explained in 1 episode
• Convolutional Refinement Head — 5× in this paper
• Transformer Architecture — 5× in this paper · explained in 2 episodes
• Adam — 4× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 4× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Overall Accuracy — 2× in this paper
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition Workshops — 1× in this paper
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Day of Year — 1× in this paper
• F1 Score — 1× in this paper · explained in 3 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Recurrent Neural Network — 1× in this paper
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Rethinking Atrous Convolution for Semantic Image Segmentation
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Adam: A Method for Stochastic Optimization

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