Aerial Photo to Land-Cover Map
This prompt converts a reference aerial photograph into a false-color land-cover classification map for urban analysis or map-style visualization.
This infographic prompt is a strong starting point when you need a composed GPT Image 2 result with a clear subject, visual structure, and reusable style direction.
Aspect ratio
16:9 or 4:3
Quality pass
Standard first, then high detail
Workflow
Use image-to-image when matching an existing layout or subject matters.
Full GPT Image 2 prompt
English prompt
Using REFERENCE_0 as the source geography and urban layout, transform the monochrome historical aerial photo into a clean land-cover classification map using the false-color visual language implied by REFERENCE_1. Keep the same top-down viewpoint and street/block structure, but replace photographic texture with flat segmented regions and crisp boundaries. Classify the scene into exactly 6 visual categories: 1) buildings as red, 2) roads and major paved corridors as white, 3) water bodies and canals as blue, 4) dense vegetation/parks as dark green, 5) open grass or sparse green areas as light green, and 6) industrial or large paved/open facility areas as gray. Produce a dense urban thematic map with strong contrast, minimal shading, and no labels, legends, borders, or photographic film markings.
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