Copy-ready prompt
PYTHON_SCENE_GRAPH :: PARAMETRIC_CITY_RELIEF class Variables: city = "{argument name="city" default="[CITY]"} "city_name_text = "{argument name="city name" default="literal city name from input"} "region_context = "Infers country, topography, climate, culture, and city characteristics" topography = "Infers mountains, rivers, coastlines, plains, islands, deserts, and hills" urban_grid = "Infers region density, roads, transportation corridors, and city layout" landmarks = "Infers landmark_set(city)" signature_core = "Infers the most symbolic central landmark or public space" style = "Luxury 3D map city model" class TerrainSlab: form = "Thick, raised, perforated map base" surface = Variables.topography edges = "Engraved title panel, legend, compass, scale bar, and illustrated area map" material = "Matte stone/plaster/map model material" class CityTypography: text = Variables.city_name_text form = "Monumental 3D font" function = "Each letter is a habitable building volume" placement = "Integrated into the city map, not floating" rule = "Text must remain readable from an overhead view" class UrbanLayer: roads = Variables.urban_grid districts = "Inferring community and density zones" landmarks = Variables.landmarks core = Variables.signature_core labels = "Derived location labels based on city geography" class Atmosphere: camera = "High-angle three-quarter macro view" lighting = "Soft, high-end studio daylight" details = "Vehicles, clouds, airplanes, trees, people only shown where appropriate" def render(): return """{argument name="target city" default="[CITY]"} Rendered as a raised, 3D topographical map model, where city names are transformed into monumental buildings, supplemented with inferred geography, landmarks, labels, roads, and atlas-style cartographic details.
Prompt breakdown
PYTHON_SCENE_GRAPH :: PARAMETRIC_CITY_RELIEF class Variables: city = "{argument name="city" default="[CITY]"} "city_name_text = "{argument name="city name" default="literal city name from input"} "region_context = "Infers country, topography, climate, culture, and city characteristics" topography = "Infers mountains, rivers, coastlines, plains, islands, deserts, and hills" urban_grid = "Infers region density, roads, transportation corridors, and city layout" landmarks = "Infers landmark_set(city)" signature_core = "Infers the most symbolic central landmark or public space" style = "Luxury 3D map city model" class TerrainSlab: form = "Thick, raised, perforated map base" surface = Variables.topography edges = "Engraved title panel, legend, compass, scale bar, and illustrated area map" material = "Matte stone/plaster/map model material" class CityTypography: text = Variables.city_name_text form = "Monumental 3D font" function = "Each letter is a habitable building volume" placement = "Integrated into the city map, not floating" rule = "Text must remain readable from an overhead view" class UrbanLayer: roads = Variables.urban_grid districts = "Inferring community and density zones" landmarks = Variables.landmarks core = Variables.signature_core labels = "Derived location labels based on city geography" class Atmosphere: camera = "High-angle three-quarter macro view" lighting = "Soft, high-end studio daylight" details = "Vehicles, clouds, airplanes, trees, people only shown where appropriate" def render(): return """{argument name="target city" default="[CITY]"} Rendered as a raised, 3D topographical map model, where city names are transformed into monumental buildings, supplemented with inferred geography, landmarks, labels, roads, and atlas-style cartographic details.
Use clear style, lighting, and composition cues.
Explain where the generated visual will be used.
Avoid brand logos and review licensing before commercial use.










