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HLS-GPT Transformer reconstructs NASA satellite reflectance data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed HLS-GPT, a large-scale generative pretrained Transformer model designed to reconstruct NASA's Harmonized Landsat and Sentinel-2 (HLS) surface reflectance data. This model utilizes a hierarchical Transformer architecture to process varying spectral band configurations and operates on single-pixel time series. Trained on extensive data from the conterminous United States, HLS-GPT demonstrates robust reconstruction capabilities across diverse land surface conditions and outperforms conventional methods and the NASA-IBM Prithvi model in evaluations. AI

IMPACT This model advances AI's capability in processing and reconstructing complex satellite imagery for environmental monitoring.

RANK_REASON The cluster describes a new research paper detailing a novel AI model for satellite data reconstruction.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

HLS-GPT Transformer reconstructs NASA satellite reflectance data

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The cluster describes a new research paper detailing a novel AI model for satellite data reconstruction.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Junjie Li, Hankui K. Zhang, David P. Roy ·

    HLS-GPT: A Generative Pretrained Transformer (GPT) for Continental-Scale NASA Harmonized Landsat and Sentinel-2 (HLS) Reflectance Reconstruction Across All Bands on Arbitrary Dates

    arXiv:2606.18115v1 Announce Type: new Abstract: Recent deep learning methods for Landsat and Sentinel-2 reflectance time series reconstruction remain limited by restricted spectral coverage, limited geographic scalability, or patch-based designs with short temporal contexts. We p…

  2. arXiv cs.CV TIER_1 English(EN) · David P. Roy ·

    HLS-GPT: A Generative Pretrained Transformer (GPT) for Continental-Scale NASA Harmonized Landsat and Sentinel-2 (HLS) Reflectance Reconstruction Across All Bands on Arbitrary Dates

    Recent deep learning methods for Landsat and Sentinel-2 reflectance time series reconstruction remain limited by restricted spectral coverage, limited geographic scalability, or patch-based designs with short temporal contexts. We present HLS-GPT, a large-scale generative pretrai…

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