RRepoGEO

REPOGEO REPORT · LITE

KyanChen/RSPrompter

Default branch release · commit 7c676fec · scanned 6/13/2026, 11:03:32 AM

GitHub: 657 stars · 43 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 KyanChen/RSPrompter, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    remote-sensing, instance-segmentation, foundation-models, pytorch, computer-vision, deep-learning, mmdetection
  • highreadme#2
    Strengthen README introduction to emphasize remote sensing solution

    Why:

    CURRENT
    This repository is the code implementation of the paper RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model, which is based on the MMDetection project.
    COPY-PASTE FIX
    This repository implements RSPrompter, a PyTorch solution for **remote sensing instance segmentation** that **learns to prompt visual foundation models** for optimal adaptation. While based on MMDetection, RSPrompter focuses on specialized applications within remote sensing imagery.
  • mediumabout#3
    Refine the repository's 'About' description for conciseness

    Why:

    CURRENT
    This is the pytorch implement of our paper "RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model"
    COPY-PASTE FIX
    PyTorch implementation of RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Models. Adapts foundation models for specialized RS tasks.

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 KyanChen/RSPrompter
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. MMSegmentation · recommended 2×
  3. Detectron2 · recommended 1×
  4. Timm · recommended 1×
  5. MMDetection · recommended 1×
  • CATEGORY QUERY
    What frameworks enable instance segmentation for remote sensing data using visual foundation models?
    you: not recommended
    AI recommended (in order):
    1. Detectron2
    2. Hugging Face Transformers
    3. Timm
    4. MMSegmentation
    5. MMDetection
    6. OpenMMLab
    7. Hugging Face Transformers
    8. YOLOv8
    9. SAM (Segment Anything Model)
    10. MobileSAM
    11. FastSAM

    AI recommended 11 alternatives but never named KyanChen/RSPrompter. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for PyTorch implementations to adapt foundation models for remote sensing segmentation tasks.
    you: not recommended
    AI recommended (in order):
    1. segment-anything (SAM)
    2. MMSegmentation
    3. timm (PyTorch Image Models)
    4. DeepLabV3+
    5. U-Net
    6. Awesome-Remote-Sensing-Foundation-Models

    AI recommended 6 alternatives but never named KyanChen/RSPrompter. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 KyanChen/RSPrompter?
    pass
    AI named KyanChen/RSPrompter explicitly

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

  • If a team adopts KyanChen/RSPrompter in production, what risks or prerequisites should they evaluate first?
    pass
    AI named KyanChen/RSPrompter 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 KyanChen/RSPrompter solve, and who is the primary audience?
    pass
    AI named KyanChen/RSPrompter explicitly

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

Embed your GEO score

Drop this badge into the README of KyanChen/RSPrompter. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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<a href="https://repogeo.com/en/r/KyanChen/RSPrompter"><img src="https://repogeo.com/badge/KyanChen/RSPrompter.svg" alt="RepoGEO" /></a>
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KyanChen/RSPrompter — 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