arXiv AI
Pixel-Level Transformers in Remote Sensing: A Canopy Height Case Study
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SERA-H: Super-Resolution of Sentinel Time Series for Fine-Scale Canopy Height Mapping
arXiv:2512.18128v4 Announce Type: replace Abstract: High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the ad...
arXiv AI
Jun 2
LALE: Lightweight-Transformer Architecture for Land-Cover Estimation
arXiv:2606. 02092v1 Announce Type: cross Abstract: Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets.
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LALE: Lightweight-Transformer Architecture for Land-Cover Estimation
Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets. Prior work typically optimizes for one of these axes: attention for global context, convolution for local detail, or compactness for efficiency.
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Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision
arXiv:2606. 20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes.
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Flood Mapping from RGB imagery using a Vision Foundation Model
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.
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PBLH Estimation from Satellite Radiances via a Dual-Encoder Transformer
The paper presents a dual‑encoder Transformer model for estimating Planetary Boundary Layer Height (PBLH) from satellite radiances, addressing challenges of multimodal, spatially incomplete data. It benchmarks eight different approaches, analyzes model reliance via grouped Shapley decomposition, and demonstrates that the proposed architecture achieves a mean absolute error of 155.8 m on a global test set, outperforming all baselines. On out‑of‑distribution data from the TEAMx campaign, the model attains 165.3 m MAE, better than a pixel‑wise baseline trained on the same data.