RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/earth2studio

Default branch main · commit 530a3dba · scanned 6/12/2026, 10:27:15 AM

GitHub: 980 stars · 218 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface NVIDIA/earth2studio, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's H1 to clearly state its specialized domain

    Why:

    CURRENT
    # NVIDIA Earth2Studio
    COPY-PASTE FIX
    # NVIDIA Earth2Studio: Deep Learning Framework for AI Weather & Climate Workflows
  • hightopics#2
    Add more specific topics to emphasize the application domain

    Why:

    CURRENT
    ai, climate-science, deep-learning, weather
    COPY-PASTE FIX
    ai, climate-science, deep-learning, weather, weather-prediction, climate-modeling, earth-system-models, ai-workflows, scientific-computing
  • mediumreadme#3
    Add a 'Why Earth2Studio?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., `## Why Earth2Studio?` or `## Earth2Studio's Unique Focus` that explicitly contrasts its end-to-end AI weather/climate workflow capabilities with general-purpose deep learning libraries (like PyTorch/TensorFlow) and general climate data tools (like Pangeo/Xarray).

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface NVIDIA/earth2studio
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 1×
  2. tensorflow/tensorflow · recommended 1×
  3. tensorflow/tfx · recommended 1×
  4. keras-team/keras · recommended 1×
  5. google/jax · recommended 1×
  • CATEGORY QUERY
    What open-source deep learning frameworks are available for building AI weather prediction models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. TensorFlow Extended (TFX) (tensorflow/tfx)
    4. Keras (keras-team/keras)
    5. JAX (google/jax)
    6. MXNet (apache/mxnet)

    AI recommended 6 alternatives but never named NVIDIA/earth2studio. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I deploy and explore AI-driven climate science workflows using Python?
    you: not recommended
    AI recommended (in order):
    1. Pangeo Ecosystem
    2. Xarray
    3. Dask
    4. Zarr
    5. Pangeo Forge
    6. Pangeo Gallery
    7. Kubernetes
    8. JupyterHub
    9. Google Earth Engine (GEE)
    10. Hugging Face Transformers
    11. 🤗 Accelerate
    12. Optimum
    13. PyTorch Lightning
    14. Keras
    15. TorchServe
    16. TensorFlow Serving
    17. MLflow
    18. FastAPI
    19. Flask
    20. Gunicorn
    21. Uvicorn

    AI recommended 21 alternatives but never named NVIDIA/earth2studio. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of NVIDIA/earth2studio?
    pass
    AI named NVIDIA/earth2studio explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts NVIDIA/earth2studio in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVIDIA/earth2studio explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo NVIDIA/earth2studio solve, and who is the primary audience?
    pass
    AI named NVIDIA/earth2studio explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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NVIDIA/earth2studio — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite