implementation

YOLOv8 Data YAML Setup

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Linked challenge: Commercial Roof Wind Damage Classification with YOLOv8 Aerial Imagery

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Linked challenge

Commercial Roof Wind Damage Classification with YOLOv8 Aerial Imagery

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Write a `dataset.yaml` file defining path references, train/val splits, and class names (`membrane_uplift`, `missing_shingles`, `pooling_water`, `structural_collapse`).

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

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implementation

This prompt already mixes executable detail with instructions, so tune examples and interfaces before rewriting the scaffold.

Linked challenge

Commercial Roof Wind Damage Classification with YOLOv8 Aerial Imagery

Severe atmospheric storms and El Niño cyclone landfalls cause widespread structural wind damage to commercial real estate roofs, tearing off membrane layers and exposing interiors to secondary water loss. Manual roof inspections across large corporate portfolios are slow, hazardous, and subject to adjuster variance. In this challenge, you will fine-tune a YOLOv8 object detection vision model using Ultralytics. The model will analyze post-storm high-resolution aerial drone imagery to detect and classify commercial roof damage types: membrane uplift, missing shingles, pooling water, and structural collapse. Your vision pipeline will output bounding box coordinates, severity ratings, and estimated repair square footage to automate preliminary loss adjustments.

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