Pixel-Level Transformers in Remote Sensing: A Canopy Height Case Study

arXiv:2609.37809 · cs.CV, cs.AI · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/SvenLigensa/pixel-level-transformers

Terminology

• Transformer — 70× in this paper · explained in 2 episodes
• Attention Mechanism — 24× in this paper · explained in 3 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Global Ecosystem Dynamics Investigation — 3× in this paper
• Scaling Laws — 3× in this paper · explained in 4 episodes
• Sparse Attention — 3× in this paper · explained in 5 episodes
• Transformer Architecture — 3× in this paper · explained in 2 episodes
• Airborne Laser Scanning — 2× in this paper
• Benchmark — 2× in this paper · explained in 2 episodes
• Pyramid Vision Transformer — 2× in this paper
• AGN — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Multi-Head Attention — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Training Deep Nets with Sublinear Memory Cost
• Axial Attention in Multidimensional Transformers
• Scaling Laws for Neural Language Models
• ECHOSAT: Estimating Canopy Height Over Space And Time
• High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model
• Linformer: Self-Attention with Linear Complexity

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