菜单
Cross-reference OSM data against CAGIS quarterly road centerlines and ODOT TIMS Road Inventory for ground-truth validation of defect classifications.
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基于 SOC 职业分类
| name | cagis-conflate |
| description | Cross-reference OSM data against CAGIS quarterly road centerlines and ODOT TIMS Road Inventory for ground-truth validation of defect classifications. |
| when_to_use | User mentions CAGIS, ODOT, ground truth, cross-reference, validate classification, compare centerlines, or check highway type |
| allowed-tools | Read Grep Glob Bash(python *) Bash(curl *) |
| argument-hint | [zone or way-id] |
| arguments | ["target"] |
Cross-reference OSM against authoritative sources for: $target
data-cagisportal.opendata.arcgis.comsource= changeset tagtims.dot.state.oh.ushttps://tiles.mblaine.com/ohio/{z}/{x}/{y}.png (mblaine's Mapnik render of ODOT data)gis.dot.state.oh.us
paths. Until a working endpoint is wired in src/osm/, do NOT include
"ODOT TIMS" in the source= tag of any submitted changeset.tl_2024_39061_roads.zip (Census Bureau, FIPS 39061 = Hamilton County)highway=residential but CAGIS says connector/arterialThe 2007 TIGER import mapped CFCC A4x codes to highway=residential by default (RESEARCH-FINDINGS Item 3). Many should be:
highway=unclassified (rural connectors)highway=tertiary (suburban arterials)highway=service (driveways/alleys)CAGIS and ODOT TIMS are the authoritative sources to determine the correct classification.
Every changeset that uses CAGIS data must include:
source=CAGIS Open Data Hub (data-cagisportal.opendata.arcgis.com)
with the required disclaimer and data creator credit per CAGIS terms.
Run the full TIGER defect audit pipeline for a MetroNow service zone — fetch from Overpass, classify defects, analyze history, generate reports.
Diff OSM geometry and attributes against TIGER/Line 2024 to identify import drift, new roads, and name-field artifacts in MetroNow zones.
Generate a MapRoulette challenge from scan results for defect classes with high false-positive rates. Creates GeoJSON tasks constrained to MetroNow zone polygons.
Deep revision history analysis for specific OSM ways — fetches full version history from OSM API v0.6, identifies import bot vs human edits, classifies review status with confidence scores.
Submit corrections to OSM API v0.6 with full community compliance — proper changeset tags, size limits, rate-limit awareness, and dry-run support.
Decompress MetroNow docs so future-you (and fresh AI sessions) can pick the project back up cold without re-deriving everything. Use when the user says docs are confusing, plain, dense, hard to follow, missing context, jargon-heavy, jump straight to conclusions, or are hard to come back to after time away; asks why something is the way it is; says they can't tell what a term means; asks for a walkthrough, explainer, primer, or context refresher; says a section assumes too much; or wants visual aids. Diagnoses missing-middle gaps (undefined jargon, unstated WHY, skipped bridge steps, prose where a picture is needed) and rewrites sections using a fixed template — definition first, then bridge steps, then a load-bearing diagram, then code citations. Outputs to docs/explainers/<topic>.md or in-place edits. No VitePress, no static-site generator — just markdown that GitHub renders.