RRepoGEO

REPOGEO REPORT · LITE

uzh-rpg/event-based_vision_resources

Default branch master · commit 138b8b4d · scanned 5/12/2026, 5:43:07 AM

GitHub: 3,540 stars · 733 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 uzh-rpg/event-based_vision_resources, 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
    ["event-based-vision", "neuromorphic-vision", "event-cameras", "computer-vision", "research-resources", "datasets", "papers", "workshops", "code", "robotics"]
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with the project's intent for community resource sharing.
  • mediumreadme#3
    Add a clear introductory sentence to the README

    Why:

    CURRENT
    # Event-based Vision Resources
    COPY-PASTE FIX
    # Event-based Vision Resources

This repository is a community-driven collection of essential resources for event-based vision technology, including papers, workshops, datasets, code, and videos.

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 uzh-rpg/event-based_vision_resources
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome Event-based Vision
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome Event-based Vision · recommended 2×
  2. Event-based Vision Resources · recommended 1×
  3. neuromorphic.ai · recommended 1×
  4. IEEE Xplore / ACM Digital Library · recommended 1×
  5. Google Scholar · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive collection of resources for event-based vision research?
    you: not recommended
    AI recommended (in order):
    1. Event-based Vision Resources
    2. Awesome Event-based Vision
    3. neuromorphic.ai
    4. IEEE Xplore / ACM Digital Library
    5. Google Scholar
    6. CVF Open Access

    AI recommended 6 alternatives but never named uzh-rpg/event-based_vision_resources. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best open-source datasets and code examples for event camera processing?
    you: not recommended
    AI recommended (in order):
    1. DSEC
    2. Event Camera Dataset (ECDC) by M. Mueggler et al.
    3. MVSEC
    4. Prophesee Datasets
    5. Metavision SDK
    6. DDD17
    7. Awesome Event-based Vision

    AI recommended 7 alternatives but never named uzh-rpg/event-based_vision_resources. 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 uzh-rpg/event-based_vision_resources?
    pass
    AI did not name uzh-rpg/event-based_vision_resources — 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 uzh-rpg/event-based_vision_resources in production, what risks or prerequisites should they evaluate first?
    pass
    AI named uzh-rpg/event-based_vision_resources 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 uzh-rpg/event-based_vision_resources solve, and who is the primary audience?
    pass
    AI did not name uzh-rpg/event-based_vision_resources — 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

Drop this badge into the README of uzh-rpg/event-based_vision_resources. 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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uzh-rpg/event-based_vision_resources — 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