Swiss Building Volume & Area Estimator

Estimates building volumes and gross floor areas using publicly available Swiss elevation models and cadastral data.

The solution is available in three variants:

Web App — Zero-install browser app. Upload a CSV with building EGIDs, get volumes and floor areas on a map with export to CSV/Excel/GeoJSON.
Python CLI — Open-source, requires Python >= 3.10 and free dependencies. Processes locally with exact LV95 areas and local elevation tiles.
FME — Requires a licensed copy of FME Form.

Web App

The browser-based version runs entirely client-side — no backend, no installation. Upload a CSV with id and egid columns and the app will:

Fetch building footprints from geodienste.ch WFS (ms:LCSF, filtered by GWR_EGID)
Load elevation data (DTM + DSM) from swisstopo COG tiles on-the-fly
Compute volume and heights using an orientation-aligned 2×2m grid
Look up building classification from GWR via swisstopo API
Estimate floor areas from building type and volume
Display results on an interactive map with table and summary panel

Features

Interactive Map — MapLibre GL JS with 3D building extrusions, orientation-aligned grid cell visualization, 4 basemaps, scale bar
Layer Panel — Toggle Gebäudegrundflächen, Gebäudevolumen, Rasterzellen, Beschriftungen, and AV cadastral overlay
Summary Panel — Collapsible sections for building status, volume/height aggregates, and floor area estimates
Table Widget — Sortable columns, search filter, pagination, resizable panel, row click → map highlight
Export — CSV, Excel (XLSX), and GeoJSON with timestamped filenames
Privacy — All data stays in the browser. Only EGID and coordinates are sent to public APIs

Limitations vs Python Version

	Web App	Python CLI
Data coverage	20 of 26 cantons via public WFS (JU, LU, NE, NW, OW, VD blocked)	All cantons via local GeoPackage
Elevation data	On-the-fly COG tile loading from swisstopo CDN	Local GeoTIFF tiles (faster, offline)
Grid resolution	2×2m (configurable)	1×1m (configurable)
Area calculation	Spherical (Turf.js), ~0.1–0.5% error for spatial matching	Exact planar (LV95/EPSG:2056)
Throughput	~5 buildings in parallel, limited by API rate	Bulk processing with local data
EGID lookup	Direct WFS filter by `GWR_EGID`	Local GeoPackage spatial join
Offline	Requires internet	Fully offline with local data

Data coverage note: The Web App uses the geodienste.ch WFS, which requires cantonal approval in 6 cantons (JU, LU, NE, NW, OW, VD). Buildings in these cantons will return "Kein Grundriss". Coverage is also incomplete in TI, VS, and NE.

Quick Start

Open index.html in a browser (requires a local server for ES modules):

cd area-estimator
python -m http.server 8080
# Open http://localhost:8080

Or deploy to any static hosting (GitHub Pages, Cloudflare Pages, etc.).

File Structure

index.html                   Entry point (GitHub Pages compatible)
css/
  tokens.css                 Design tokens (colors, spacing, typography)
  styles.css                 Component styles + responsive breakpoints
js/
  main.js                    State machine (upload → processing → results)
  upload.js                  CSV/XLSX parsing with auto-delimiter detection
  processor.js               EGID/coord lookup + WFS query + volume pipeline (5× parallel)
  elevation.js               COG tile loading, oriented grid, volume computation
  map.js                     MapLibre map, 3D extrusions, grid cell visualization
  table.js                   Sortable table with pagination and search
  config.js                  API endpoints, floor height lookup, map styles
  export.js                  CSV/XLSX/GeoJSON export
data/
  example.csv                Demo data (10 buildings with verified EGIDs)

APIs Used

API	Purpose	Auth
`geodienste.ch/db/av_0/{lang}` WFS	Building footprints by EGID or BBOX (`ms:LCSF`)	None (CORS)
`api3.geo.admin.ch/MapServer/find`	GWR building attributes by EGID	None (CORS)
`data.geo.admin.ch`	swissALTI3D + swissSURFACE3D COG tiles	None (CORS)

Model Overview

flowchart TD
    subgraph INPUT["Inputs"]
        A1A["🔴 --footprints<br>AV GeoPackage / Shapefile / GeoJSON<br><i>required</i>"]
        A1B["⚪ --coordinates<br>CSV: id, lon, lat<br><i>optional — enables spatial join mode</i>"]
        A2["🔴 --alti3d<br>swissALTI3D tiles — terrain DTM 0.5m<br><i>required</i>"]
        A3["🔴 --surface3d<br>swissSURFACE3D tiles — surface DSM 0.5m<br><i>required</i>"]
    end

    A1A --> S1
    A1B -.->|"--coordinates"| S1
    A2 --> S3
    A3 --> S3

    S1["<b>Step 1 — Read Footprints</b><br>Mode A: all AV buildings<br>Mode B: spatial join → one AV polygon per CSV point<br>Unmatched points → status: no_footprint"]
    S2["<b>Step 2 — Aligned Grid</b><br>Minimum rotated rectangle orientation<br>Grid points filtered to footprint"]
    S3["<b>Step 3 — Volume & Heights</b><br>Sample DTM + DSM at each point<br>Volume = Σ max(surface_i − min(terrain), 0) × cell_area"]
    S4["<b>Step 4 — Floor Areas</b><br>GWR classification → floor height<br>Floors = height_minimal / floor_height<br>GFA = footprint × floors"]

    S1 --> S2 --> S3
    S3 --> OUT
    S3 -.->|"--estimate-area"| S4
    S4 -.-> OUT

    OUT[/"<b>Output CSV</b><br>volume · heights · elevations<br>+ floor areas if --estimate-area"/]

    subgraph GWR["⚪ GWR Data — optional, only with --estimate-area"]
        G1["--gwr-csv<br>CSV bulk download<br><i>housing-stat.ch</i>"]
        G2["(default)<br>swisstopo API<br><i>per EGID, live lookup</i>"]
    end

    GWR -.-> S4

    classDef required fill:#fde8e8,stroke:#e02424,stroke-width:2px
    classDef optional fill:#f3f4f6,stroke:#9ca3af,stroke-width:1px
    classDef step fill:#eff6ff,stroke:#3b82f6,stroke-width:1.5px
    classDef optionalStep fill:#f3f4f6,stroke:#9ca3af,stroke-width:1px,stroke-dasharray:4 3

    class A1A,A2,A3 required
    class A1B optional
    class S1,S2,S3 step
    class S4 optionalStep

Note: The flowchart above describes the Python CLI pipeline. The Web App follows the same 4 steps but sources data from public APIs instead of local files.

Python CLI

Command-Line Reference

Argument	Required	Description
Input
`--footprints FILE`	yes	Geodata file with building polygons (GeoPackage, Shapefile, or GeoJSON from AV). Alone: processes all buildings in the file.
`--coordinates FILE`	no	CSV with `id`, `lon`, `lat` columns (WGS84); optionally `egid` (reference only). When provided, performs a strict spatial join — only AV buildings containing a CSV point are processed. Points with no matching polygon are reported as `no_footprint` and skipped. No fallbacks.
Elevation data
`--alti3d DIR`	yes	Directory with swissALTI3D GeoTIFF tiles
`--surface3d DIR`	yes	Directory with swissSURFACE3D GeoTIFF tiles
`--auto-fetch`		Automatically download missing tiles from swisstopo
Output
`-o, --output FILE`		Output CSV file path (default: `data/output/result_<timestamp>.csv`)
Filters
`-l, --limit N`		Process only the first N buildings
`-b, --bbox MIN_LON MIN_LAT MAX_LON MAX_LAT`		Bounding box filter in WGS84 (only in all-buildings mode, i.e. without `--coordinates`)
Area estimation (off by default)
`--estimate-area`		Enable Step 4: floor area estimation
`--gwr-csv FILE`		GWR CSV from housing-stat.ch; if omitted, uses swisstopo API

Setup

pip install -r python/requirements.txt

Examples

# Portfolio list against AV footprints (spatial join)
python python/main.py \
    --footprints "D:\AV_lv95\av_2056.gpkg" \
    --coordinates my_buildings.csv \
    --alti3d "D:\SwissAlti3D" \
    --surface3d "D:\swissSURFACE3D Raster" \
    --auto-fetch \
    -o portfolio_volumes.csv

# All buildings in Switzerland
python python/main.py \
    --footprints data/av/ch_av_2056.gpkg \
    --alti3d /data/swisstopo/swissalti3d \
    --surface3d /data/swisstopo/swisssurface3d \
    --estimate-area --gwr-csv data/gwr/buildings.csv \
    -o results/ch_all_buildings.csv

# Quick test with auto-fetch (no local data needed)
python python/main.py \
    --footprints data/av/ch_av_2056.gpkg \
    --coordinates my_buildings.csv \
    --alti3d data/swissalti3d \
    --surface3d data/swisssurface3d \
    --auto-fetch \
    --limit 10

Outputs

All results are written to a single CSV file (result_<timestamp>.csv).

Step 1 — Footprints

Two modes, automatically selected based on which flags are provided:

AV only (--footprints): Loads all building polygons from the geodata file and filters to buildings (Art = Gebaeude). Converts to LV95 if needed. The egid column is renamed to av_egid; each feature gets a fid.
AV + CSV (--footprints + --coordinates): Strict spatial join — each CSV point must fall within an AV building polygon. No fallbacks. Rows with no match get status_step1 = no_footprint and are skipped.

Step 2 — Aligned Grid

Fills each building footprint with a grid of sample points. The grid is rotated to align with the building's longest edge (using the minimum area bounding rectangle), so it fits tightly even for angled buildings. Grid resolution is 1×1m (Python) or 2×2m (Web App).

Step 3 — Volume & Heights

At each grid point, the tool reads two elevations: the ground level (DTM) and the surface level including buildings/trees (DSM). The above-ground height at each point is measured from the lowest terrain elevation under the building (elevation_base_min) as a flat horizontal datum — max(surface_i − min(terrain), 0). Volume is the sum of all those heights, each multiplied by the cell area.

Column	Description
`area_footprint_m2`	Footprint area from AV polygon geometry (m²)
`volume_m3`	Total above-ground volume: `Σ max(surface_i − min(terrain), 0) × cell_area`
`elevation_base_min`	Lowest ground elevation — volume base datum (m a.s.l.)
`elevation_base_mean`	Mean ground elevation (m a.s.l.)
`elevation_base_max`	Highest ground elevation (m a.s.l.)
`elevation_roof_min`	Lowest surface elevation — typically the eave (m a.s.l.)
`elevation_roof_mean`	Mean surface elevation (m a.s.l.)
`elevation_roof_max`	Highest surface elevation — typically the ridge (m a.s.l.)
`height_mean`	Average above-ground height from `elevation_base_min` (m)
`height_max`	Tallest above-ground point (m)
`height_minimal`	`volume ÷ footprint area` — equivalent uniform box height (m)
`grid_points`	Number of grid points with valid DTM + DSM data

Step 4 — Floor Areas (optional)

Estimates gross floor area by dividing building height by a typical floor height for that building type. The building type comes from the GWR (Federal Register of Buildings). The tool looks up the floor height in this order: first by the specific building class (GKLAS, e.g. "Office building" → 3.80 m), then by the broader category (GKAT, e.g. "Non-residential" → 4.15 m), and falls back to a default of 3.00 m. Based on the Canton Zurich methodology (Seiler & Seiler, 2020).

Column	Description
`gkat`	GWR building category code (e.g. 1020 = Residential)
`gklas`	GWR building class code (e.g. 1110 = Single-family house)
`gbauj`	Construction year
`gastw`	Number of stories (from register)
`floor_height_used`	Floor height used for estimation (m)
`floors_estimated`	Estimated floors: `height_minimal ÷ floor_height`, capped at GWR `gastw` if available
`area_floor_total_m2`	Gross floor area: `footprint × estimated floors` (m²)
`area_accuracy`	`high` (±10–15%) / `medium` (±15–25%) / `low` (±25–40%)
`building_type`	Human-readable building type

Limitations

Limitation	Detail
No underground estimation	LIDAR only sees above ground — basements and underground floors are not included
Trees over buildings	The surface model doesn't distinguish roofs from foliage — tall trees over small buildings inflate the measured height and volume
Surface model merging	swissSURFACE3D combines ground, vegetation, and buildings into one surface; this can cause overestimation near vegetation
Small buildings	Footprints smaller than the grid cell size produce no grid points and can't be measured
Mixed-use buildings	A single floor height is applied per building; actual floor heights may vary (e.g. retail ground floor + residential upper floors)
Industrial / special buildings	Floor height ranges are wide (4–7 m), so floor count estimates are less reliable
Data timing	The elevation model may have been captured before or after the building was constructed or modified
Sloped terrain	Volume is measured from `elevation_base_min` (lowest terrain point) as a flat datum. On steeply sloped sites, this includes terrain undulation.
Web App coverage	6 cantons (JU, LU, NE, NW, OW, VD) block the geodienste.ch WFS — use the Python CLI with a local GeoPackage for full coverage

Project Structure

area-estimator/
├── index.html                        ← Web App entry point (GitHub Pages)
├── css/                              ← Stylesheets
│   ├── tokens.css                       Design tokens
│   └── styles.css                       Component styles
├── js/                               ← Web App modules
│   ├── main.js                          State machine + UI
│   ├── upload.js                        CSV/XLSX parsing
│   ├── processor.js                     EGID lookup + WFS + volume pipeline
│   ├── elevation.js                     COG tiles, oriented grid, volume
│   ├── map.js                           MapLibre map + 3D visualization
│   ├── table.js                         Results table
│   ├── config.js                        Endpoints, floor heights, constants
│   └── export.js                        CSV/XLSX/GeoJSON export
├── python/                           ← Python CLI (Steps 1–4)
│   ├── main.py                          CLI entry point
│   ├── footprints.py                    Step 1: load footprints / coordinates
│   ├── grid.py                          Step 2: aligned grid
│   ├── volume.py                        Step 3: elevation sampling & volume
│   ├── tile_fetcher.py                  On-demand tile download from swisstopo
│   ├── gwr.py                           GWR lookup (CSV + API)
│   ├── area.py                          Step 4: floor area estimation
│   └── requirements.txt
├── fme/                              ← FME workbench (requires license)
├── tools/
│   ├── roof-estimator/               ← Roof shape analysis from 3D meshes
│   └── green-roof-eval/              ← Green roof detection (FME-based)
├── legacy/                           ← Original implementations (reference)
├── data/                             ← .gitignored (except example.csv)
│   ├── example.csv                      Demo data for Web App
│   ├── output/                          Pipeline results
│   ├── gwr/                             GWR CSV download
│   ├── swissalti3d/                     Terrain tiles
│   └── swisssurface3d/                  Surface tiles
└── images/

Floor Height Lookup

Code	Building Type	Schema	GF (m)	UF (m)
1010	Provisional shelter	GKAT	2.70–3.30	2.70–3.30
1030	Residential with secondary use	GKAT	2.70–3.30	2.70–3.30
1040	Partially residential	GKAT	3.30–3.70	2.70–3.70
1060	Non-residential	GKAT	3.30–5.00	3.00–5.00
1080	Special-purpose	GKAT	3.00–4.00	3.00–4.00
1110	Single-family house	GKLAS	2.70–3.30	2.70–3.30
1121	Two-family house	GKLAS	2.70–3.30	2.70–3.30
1122	Multi-family house	GKLAS	2.70–3.30	2.70–3.30
1130	Community residential	GKLAS	2.70–3.30	2.70–3.30
1211	Hotel	GKLAS	3.30–3.70	3.00–3.50
1212	Short-term accommodation	GKLAS	3.00–3.50	3.00–3.50
1220	Office building	GKLAS	3.40–4.20	3.40–4.20
1230	Wholesale and retail	GKLAS	3.40–5.00	3.40–5.00
1231	Restaurants and bars	GKLAS	3.30–4.00	3.30–4.00
1241	Stations and terminals	GKLAS	4.00–6.00	4.00–6.00
1242	Parking garages	GKLAS	2.80–3.20	2.80–3.20
1251	Industrial building	GKLAS	4.00–7.00	4.00–7.00
1252	Tanks, silos, warehouses	GKLAS	3.50–6.00	3.50–6.00
1261	Culture and leisure	GKLAS	3.50–5.00	3.50–5.00
1262	Museums and libraries	GKLAS	3.50–5.00	3.50–5.00
1263	Schools and universities	GKLAS	3.30–4.00	3.30–4.00
1264	Hospitals and clinics	GKLAS	3.30–4.00	3.30–4.00
1265	Sports halls	GKLAS	3.00–6.00	3.00–6.00
1271	Agricultural buildings	GKLAS	3.50–5.00	3.50–5.00
1272	Churches and religious buildings	GKLAS	3.00–6.00	3.00–6.00
1273	Monuments and protected buildings	GKLAS	3.00–4.00	3.00–4.00
1274	Other structures	GKLAS	3.00–4.00	3.00–4.00
—	Default (unknown)	—	2.70–3.30	2.70–3.30

References

Resource	Link
Amtliche Vermessung (AV)	geodienste.ch/services/av
swissALTI3D	swisstopo.admin.ch
swissSURFACE3D Raster	swisstopo.admin.ch
swisstopo Search API	docs.geo.admin.ch
swisstopo Find API	docs.geo.admin.ch
GWR	housing-stat.ch
GWR Public Data	housing-stat.ch/data
GWR Catalog v4.3	housing-stat.ch/catalog
Canton Zurich Methodology	Seiler & Seiler GmbH, Dec 2020 — are.zh.ch
DM.01-AV-CH Data Model	cadastre-manual.admin.ch

Future Development

Feature	Description
Watertight 3D mesh	Generate closed building geometry from elevation data
Roof geometry estimation	Classify roof shapes (flat, gable, hip) and estimate roof surface areas
Outer wall quantities	Estimate exterior wall areas from footprint perimeter and height metrics
Material classification	Building material detection from imagery or other data sources
International buildings	Extend beyond Switzerland using alternative elevation and cadastral data

Tech Stack & Credits

Web App

Library	Version	Purpose
MapLibre GL JS	4.7	Interactive map with 3D fill-extrusion rendering
GeoTIFF.js	2.1	Cloud Optimized GeoTIFF (COG) reading in-browser
Turf.js	7	Spatial operations (point-in-polygon, distance, centroid)
proj4js	2.12	Coordinate transforms (WGS84 ↔ LV95/EPSG:2056)
SheetJS (XLSX)	0.18	Excel import/export (lazy-loaded)
Source Sans 3	—	Typography
Material Symbols	—	UI icons

Python CLI

Library	Purpose
GeoPandas	Vector geodata processing
Rasterio	GeoTIFF reading with windowed access
Shapely	Geometry operations, minimum rotated rectangle
NumPy	Vectorized grid creation and elevation sampling
pyproj	CRS transforms

Data Sources

Provider	Dataset	Usage
swisstopo	swissALTI3D, swissSURFACE3D	Terrain (DTM) and surface (DSM) elevation models at 0.5m resolution
geodienste.ch	Amtliche Vermessung (AV) WFS	Building footprints from official cadastral survey
BFS	GWR (Gebäude- und Wohnungsregister)	Building classification, construction year, floor count
CARTO	Positron, Dark Matter	Basemap tiles

Methodology

Floor area estimation is based on the methodology developed by Seiler & Seiler GmbH (Dec 2020) for the Canton of Zurich ARE.

License

MIT License — see LICENSE.

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Swiss Building Volume & Area Estimator

Web App

Features

Limitations vs Python Version

Quick Start

File Structure

APIs Used

Model Overview

Python CLI

Command-Line Reference

Setup

Examples

Outputs

Step 1 — Footprints

Step 2 — Aligned Grid

Step 3 — Volume & Heights

Step 4 — Floor Areas (optional)

Limitations

Project Structure

Floor Height Lookup

References

Future Development

Tech Stack & Credits

Web App

Python CLI

Data Sources

Methodology

License

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Name		Name	Last commit message	Last commit date
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.claude		.claude
assets		assets
css		css
data		data
fme		fme
images		images
js		js
legacy		legacy
python		python
tools		tools
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
index.html		index.html

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Swiss Building Volume & Area Estimator

Web App

Features

Limitations vs Python Version

Quick Start

File Structure

APIs Used

Model Overview

Python CLI

Command-Line Reference

Setup

Examples

Outputs

Step 1 — Footprints

Step 2 — Aligned Grid

Step 3 — Volume & Heights

Step 4 — Floor Areas (optional)

Limitations

Project Structure

Floor Height Lookup

References

Future Development

Tech Stack & Credits

Web App

Python CLI

Data Sources

Methodology

License

About

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License

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