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Docs/Guides/Elevation Detection

Elevation Detection

How the AnchorGrid API locates interior elevation regions inside architectural drawing PDFs, and what you can build with the resulting bounding boxes.

The basics

An interior elevation is a vertical slice drawing that shows one wall of a room — what you'd see standing in the space looking straight at that wall. Architects use them to communicate finish materials, millwork heights, fixture placement, and accessory locations that can't be read from a floor plan alone.

A single sheet in a drawing set can contain 2–6 individual elevation views. Standard document management tools (Procore, ACC) parse the sheet title block — they don't know how many elevations are on the sheet or which room each shows.

ℹ

POST /v1/drawings/detection/elevation returns a separate bounding box for every elevation region on every page, so downstream tools can address each view independently instead of treating the whole sheet as one object.

The anatomy

Each item in the elevations[] array describes one detected region:

id
"ele_a1b2c3d4e5f6"
Stable identifier — "ele_" + 12 hex chars. Useful for referencing a specific view across jobs.
page
2
1-based PDF page index. Multiple elevations on the same sheet share the same page number.
bbox.x1 / y1
88.0 / 412.0
Top-left corner of the bounding box in PDF coordinate space (origin at bottom-left of the page).
bbox.x2 / y2
312.5 / 652.0
Bottom-right corner. Subtract x1/y1 to get width/height in PDF units.
elevation_object.json
{
  "id": "ele_a1b2c3d4e5f6",
  "page": 2,
  "bbox": {
    "x1": 88.0,
    "y1": 412.0,
    "x2": 312.5,
    "y2": 652.0
  }
}

Why it matters for APIs

Once you have per-elevation bounding boxes, several workflows become mechanical:

Targeted OCR / LLM extraction

Crop exactly the elevation region from the rendered page before sending to a vision model. Reduces noise and token cost versus sending the entire sheet.

Room-level indexing

Group detected elevations by page and spatial proximity to infer which views belong to the same room — buildable without any title-block parsing.

Scope gap detection

Cross-reference elevation count per sheet against the drawing index. Sheets with fewer elevations than expected flag missing or incomplete documentation.

The CSI connection

CSI (Construction Specifications Institute) organizes construction work into 50 divisions. Interior elevation drawings frequently show work from multiple divisions on a single view:

Division 06Wood, Plastics, Composites(cabinets, millwork)
Division 09Finishes(tile, paint, wall coverings)
Division 10Specialties(toilet accessories, grab bars, signage)
Division 12Furnishings(window treatments, furniture)

Restroom elevations are particularly rich in Division 10 content: grab bars, paper towel dispensers, soap dispensers, mirrors, and toilet accessories all appear on a single view. Detecting the elevation region first, then running spec extraction on the relevant CSI sections, lets you validate fixture compliance without manual sheet review.

Code example

The example below uploads a drawing set, submits elevation detection across all pages, polls until complete, and prints each detected region with its page and bounding box.

detect_elevations.py
import time
import httpx

API_KEY = "<your-api-key>"
BASE    = "https://api.anchorgrid.ai"
HEADERS = {"X-API-Key": API_KEY}

# 1. Upload the PDF
with open("floor-plans.pdf", "rb") as f:
    upload = httpx.post(
        f"{BASE}/v1/documents",
        headers=HEADERS,
        files={"file": ("floor-plans.pdf", f, "application/pdf")},
    )
upload.raise_for_status()
document_id = upload.json()["document_id"]
print(f"Uploaded: {document_id}")

# 2. Submit elevation detection (all pages)
job = httpx.post(
    f"{BASE}/v1/drawings/detection/elevation",
    headers={**HEADERS, "Content-Type": "application/json"},
    json={"document_id": document_id},
)
job.raise_for_status()
job_id   = job.json()["job_id"]
poll_url = BASE + job.json()["poll_url"]
print(f"Job queued: {job_id}")

# 3. Poll until complete
while True:
    result = httpx.get(poll_url, headers=HEADERS).json()
    status = result["status"]
    print(f"  status: {status}")
    if status == "complete":
        break
    if status == "failed":
        raise RuntimeError(result.get("error_code"))
    time.sleep(3)

# 4. Print detections
data = result["result"]
print(f"\nFound {data['elevations_found']} elevations "
      f"across {data['pages_analyzed']} pages\n")

for e in data["elevations"]:
    b = e["bbox"]
    print(
        f"  [{e['id']}]  page {e['page']}  "
        f"({b['x1']:.1f},{b['y1']:.1f}) → ({b['x2']:.1f},{b['y2']:.1f})"
    )

To scan specific pages only, pass page_numbers in the detection request:

# Scan only pages 3–7
json={"document_id": document_id, "page_numbers": [3, 4, 5, 6, 7]}

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