RRepoGEO

REPOGEO REPORT · LITE

Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models

Default branch main · commit e6ea8d61 · scanned 5/26/2026, 6:52:49 PM

GitHub: 1,828 stars · 165 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise 'About' description for the repository

    Why:

    COPY-PASTE FIX
    A collection of papers, datasets, benchmarks, code, and pre-trained weights for Remote Sensing Foundation Models (RSFMs).
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT License) in the root of the repository.

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 Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenStreetMap (OSM)
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenStreetMap (OSM) · recommended 2×
  2. ultralytics/ultralytics · recommended 2×
  3. Hugging Face Transformers · recommended 1×
  4. Awesome Remote Sensing Foundation Models · recommended 1×
  5. Radiant Earth Foundation MLHub · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive collection of resources for remote sensing foundation models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Awesome Remote Sensing Foundation Models
    3. Radiant Earth Foundation MLHub
    4. Google Earth Engine (GEE)
    5. OpenStreetMap (OSM)
    6. Microsoft Planetary Computer

    AI recommended 6 alternatives but never named Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best pre-trained models and datasets available for remote sensing applications?
    you: not recommended
    AI recommended (in order):
    1. ResNet
    2. PyTorch
    3. torchvision.models
    4. TensorFlow
    5. tf.keras.applications
    6. U-Net
    7. U-Net++
    8. DeepLabV3+
    9. COCO
    10. Cityscapes
    11. segmentation_models.pytorch (qubvel/segmentation_models.pytorch)
    12. YOLO
    13. YOLOv5 (ultralytics/yolov5)
    14. YOLOv7 (WongKinYiu/yolov7)
    15. YOLOv8 (ultralytics/ultralytics)
    16. Ultralytics (ultralytics/ultralytics)
    17. Vision Transformers (ViT)
    18. Swin Transformer
    19. ImageNet-21K
    20. JFT-300M
    21. Hugging Face
    22. transformers (huggingface/transformers)
    23. DINO
    24. MAE
    25. SimCLR
    26. BigEarthNet
    27. EuroSAT
    28. NWPU-RESISC45
    29. DOTA (Dataset for Object Detection in Aerial Images)
    30. DeepGlobe Land Cover Classification Challenge Dataset
    31. xView
    32. OpenStreetMap (OSM)
    33. osmnx (gboeing/osmnx)

    AI recommended 33 alternatives but never named Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    Suggestion:

  • 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 Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models?
    pass
    AI did not name Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models — likely talking about a different project

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

  • If a team adopts Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models 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 Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models solve, and who is the primary audience?
    pass
    AI did not name Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models — likely talking about a different project

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

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite