RRepoGEO

REPOGEO REPORT · LITE

TRI-ML/DDAD

Default branch master · commit 0c3f814d · scanned 6/5/2026, 9:51:54 PM

GitHub: 554 stars · 54 forks

AI VISIBILITY SCORE
60 /100
Needs work
Category recall
1 / 2
Avg rank #4.0 when recommended
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 TRI-ML/DDAD, 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
  • highreadme#1
    Reposition README opening to explicitly state dataset purpose

    Why:

    CURRENT
    DDAD is a new autonomous driving benchmark from TRI (Toyota Research Institute) for long range (up to 250m) and dense depth estimation in challenging and diverse urban conditions.
    COPY-PASTE FIX
    This repository provides the DDAD (Dense Depth for Autonomous Driving) dataset, a new autonomous driving benchmark from TRI (Toyota Research Institute). It is designed for long-range (up to 250m) and dense depth estimation in challenging and diverse urban conditions.
  • mediumreadme#2
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    The DDAD dataset is released under [insert specific license name(s) from the LICENSE file, e.g., a custom research license]. Please refer to the `LICENSE` file in this repository for full details regarding usage and distribution.

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
1 / 2
50% of queries surface TRI-ML/DDAD
Avg rank
#4.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
Waymo Open Dataset
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Waymo Open Dataset · recommended 2×
  2. nuScenes · recommended 2×
  3. Cityscapes 3D · recommended 2×
  4. KITTI · recommended 1×
  5. Argoverse 2 · recommended 1×
  • CATEGORY QUERY
    What datasets provide ground truth depth for training self-driving perception models?
    you: #4
    AI recommended (in order):
    1. KITTI
    2. Waymo Open Dataset
    3. nuScenes
    4. DDAD ← you
    5. Argoverse 2
    6. Virtual KITTI 2
    7. Cityscapes 3D
    Show full AI answer
  • CATEGORY QUERY
    Which benchmarks are available for evaluating long-range dense depth prediction in urban settings?
    you: not recommended
    AI recommended (in order):
    1. KITTI Vision Benchmark Suite
    2. KITTI Stereo 2012/2015
    3. Waymo Open Dataset
    4. nuScenes
    5. DDAD (Dense Depth for Autonomous Driving)
    6. Cityscapes 3D
    7. ApolloScape
    8. ETH3D

    AI recommended 8 alternatives but never named TRI-ML/DDAD. 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 TRI-ML/DDAD?
    pass
    AI named TRI-ML/DDAD explicitly

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

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

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

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TRI-ML/DDAD — 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