Harlow Quarry 再利用規劃流程：MicroStation、TopoExport、Meshy、GenAnywhere 與 CityEngine

A 240-acre quarry reuse plan built with a DGN survey base, TopoExport terrain, Meshy photo-to-mesh assets, GenAnywhere generated context, and CityEngine scenarios. The hard parts were coordinate checks, layer discipline, and not mistaking generated geometry for survey data.

The deliverable and the site

Harlow Quarry is a former limestone quarry and cement plant in upstate New York. The county asked for a planning-level package: existing conditions, a terrain surface, a context model, and three housing scenarios for the east bench.
The site is 240 acres, with 22 acres of water. The plant stopped in 2001 and the county bought the land in 2023. The only new survey data was a boundary file and 12 spot elevations. Everything else came from existing records or had to be labeled as an estimate.

MicroStation: the survey base

The county supplied a DGN in EPSG:2260 with the boundary, deeds, and check points. I kept the survey as the only writable file and attached everything else as references: 1998 orthoimagery, plant record drawings, and parcel polygons.
I traced the rim, rail spur, and building footprints onto separate levels. The level names were plain: SURVEY, RIM, RAIL, BLDG_EXIST, BLDG_INTERP, CTX_GENERATED. Units were set to feet at the start.
This file became the coordinate authority for every later import.

TopoExport: terrain mesh and checks

I outlined the property boundary in TopoExport and enabled contours, waterways, and cadastral parcels. The export gave me an OBJ mesh, a DXF of vectors, and an SVG for a quick exhibit.
The mesh arrived in its own local frame. I scaled and georeferenced it against two survey control points, then compared it with the 12 spot elevations. Most benches were within a foot. The highwall was not. A height field cannot represent a vertical face, so the rock wall read as a steep slope.
I clipped the terrain at the rim and kept the surveyed rim line for safety setbacks. The mesh worked for drainage and trail grading, but not for the rim condition visitors will stand next to.

Meshy: photo-to-mesh, with a label

The crusher house was demolished in 2009. I had 14 archival photographs and no measured drawings. Meshy converted the photographs into a textured GLB mesh. I imported it, scaled it to a known door height, and placed it on BLDG_INTERP.
The result was plausible, not accurate. The roof pitch and conveyor connection were wrong. It worked for an exhibit and a context view, and it did not go into any area or cost calculation. Photo-to-mesh assets are useful for communication, never for dimensions.

GenAnywhere: generated context

The northeast 18 acres had no survey. In GenAnywhere I boxed that corner on the map, and the AI filled it with ground and buildings. The result came back as a separate context model.
I placed it on CTX_GENERATED and used it only for view renders: no quantities, no parcel counts, no grading. The output was fast and coherent, but it invented a few driveways. That is fine for context, not for design; the layer name is part of the deliverable.

CityEngine: rules and scenarios

I wrote a CGA rule file for the east bench: lot width, front setback, height, street width, and a simple roof rule. Three scenario attributes produced 160 large lots, 210 small lots, and a 180-unit cluster. CityEngine rebuilt the masses and street network on each change.
I tracked road length, lot count, tree canopy removed, and walk distance to the trailhead. The 210-lot case had the shortest road and the worst tree loss; the cluster kept a central green.
The rule file mattered more than any render. It let the planning office rerun the scenarios without rebuilding geometry by hand.

Handoff notes that mattered

• Keep one master DGN and never edit a reference file.
• Record the EPSG code in the file name and in the document.
• Put generated geometry on its own level and explain it in the legend.
• Check terrain against at least a dozen surveyed spot elevations.
• Export the scenario rule file with the model, not just the geometry.

What broke

TopoExport's mesh at the vertical wall, Meshy's roof, GenAnywhere's invented driveways, and CityEngine's tendency to produce tidy lots on ground that is not tidy. None of these were fatal, because each output had a label and a job.
The pipeline worked when I stopped expecting one tool to be authoritative everywhere. Survey, terrain, context, assets, and scenarios are five different kinds of data. Treating them as one thing is how a planning model turns into a very confident mistake.