Swiss Building Volume & Area Estimator

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

Two implementations available: This workflow is provided as a Python CLI (python/) and as an FME workbench (fme/). Both can be downloaded and used independently. The FME version requires a licensed copy of FME Desktop.

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 1×1m 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) × 1m²"]
    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

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

Examples

Setup

pip install -r python/requirements.txt

Example 1 — Portfolio list against AV footprints (spatial join)

You have a CSV of buildings (e.g. exported from a portfolio system) and a local copy of the Amtliche Vermessung GeoPackage. The tool does a strict spatial join: each CSV point must fall within an AV building polygon. Points with no match are skipped — fix the coordinates in the source data, not in the code.

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

Example 2 — Process all buildings in Switzerland (AV footprints)

Download the full Amtliche Vermessung dataset from geodienste.ch as a GeoPackage, and the complete swissALTI3D + swissSURFACE3D tile sets from swisstopo. Then run:

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

This processes ~2.5 million buildings. For a first test run, limit to a small batch:

python python/main.py \
    --footprints data/av/ch_av_2056.gpkg \
    --alti3d /data/swisstopo/swissalti3d \
    --surface3d /data/swisstopo/swisssurface3d \
    --limit 100

To process a specific region (e.g. City of Bern), use a bounding box filter:

python python/main.py \
    --footprints data/av/ch_av_2056.gpkg \
    --alti3d /data/swisstopo/swissalti3d \
    --surface3d /data/swisstopo/swisssurface3d \
    --bbox 7.40 46.93 7.48 46.97 \
    --estimate-area --gwr-csv data/gwr/buildings.csv \
    -o results/bern_buildings.csv

Example 3 — Quick test with auto-fetch

No local elevation data needed — the tool downloads tiles on-the-fly from swisstopo:

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

Inputs

Required Data

Data	Format	Required	Download	Description
AV footprints	GeoPackage	yes	geodienste.ch/services/av	Building polygons from Amtliche Vermessung (`--footprints`). Layer `lcsf`, filtered to `Art = Gebaeude`.
Building coordinates	`.csv`	no	—	List of points for spatial join against AV (`--coordinates`); omit to process all buildings in the AV file
swissALTI3D	GeoTIFF tiles (0.5 m)	yes	swisstopo.admin.ch	Terrain elevation model (DTM). Can be auto-downloaded with `--auto-fetch`.
swissSURFACE3D Raster	GeoTIFF tiles (0.5 m)	yes	swisstopo.admin.ch	Surface elevation model (DSM). Can be auto-downloaded with `--auto-fetch`.
GWR (Federal Register of Buildings)	`.csv`	with `--estimate-area`	housing-stat.ch/data	Building classification for floor height lookup. Falls back to swisstopo API per EGID if omitted.

Input Columns (coordinates CSV, `--coordinates`)

Column	Required	Description
`id`	yes	Building reference ID — preserved as `input_id` in output
`lon`	yes	WGS84 longitude
`lat`	yes	WGS84 latitude
`egid`	no	Federal building ID — preserved as `input_egid` for reference only; not used for matching

Points that do not fall within any AV building polygon result in status_step1 = no_footprint and are skipped. Coordinate accuracy must be sufficient for the point to lie inside the polygon — no tolerance or fallback is applied.

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 (looks for Gebaeude / building in the type column). 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; fix the coordinates in the source data.

Column	Format	Required	Source	Description
`area_official_m2`	float	no	AV source	Official area from AV source data (m²); `null` in CSV-only mode
`status_step1`	string	yes	Computed	`ok` / `no_footprint`

Step 2 — Grid

Fills each building footprint with a 1×1 m grid of sample points. The grid is rotated to align with the building's longest edge (using the minimum bounding rectangle), so it fits tightly even for angled buildings. These grid points are used internally by Step 3 — no columns are added to the output CSV.

Step 3 — Volume & Heights

At each 1×1 m 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_m) as a flat horizontal datum — max(surface_i − min(terrain), 0). This keeps the base plane consistent across sloped sites. Volume is the sum of all those heights, each representing 1 m².

Column	Format	Required	Source	Description
`area_footprint_m2`	float	yes	Computed	Footprint area computed from AV polygon geometry (m²)
`volume_above_ground_m3`	float	yes	DTM + DSM	Total above-ground volume: `Σ max(surface_i − min(terrain), 0) × 1 m²` — measured from the lowest ground point as a flat base datum
`elevation_base_min_m`	float	yes	DTM	Lowest ground elevation under the building — used as the volume base datum (m a.s.l.)
`elevation_base_mean_m`	float	yes	DTM	Mean ground elevation under the building (m a.s.l.)
`elevation_base_max_m`	float	yes	DTM	Highest ground elevation under the building (m a.s.l.)
`elevation_roof_min_m`	float	yes	DSM	Lowest surface elevation within footprint — typically the eave / gutter (m a.s.l.)
`elevation_roof_mean_m`	float	yes	DSM	Mean surface elevation within footprint (m a.s.l.)
`elevation_roof_max_m`	float	yes	DSM	Highest surface elevation within footprint — typically the ridge (m a.s.l.)
`height_mean_m`	float	yes	DTM + DSM	Average above-ground height across all grid points, measured from `elevation_base_min_m` (m)
`height_max_m`	float	yes	DTM + DSM	Tallest above-ground point — usually the roof ridge (m)
`height_minimal_m`	float	yes	Computed	`volume ÷ footprint area` — the height a simple box with the same footprint would need to match the building's volume (m)
`grid_points_count`	integer	yes	Computed	Number of grid points where both DTM and DSM data were available
`status_step3`	string	yes	Computed	`success` / `skipped:<status_step1>` (e.g. `skipped:no_footprint`) / `no_grid_points` / `no_height_data` / `error`

Step 4 — Floor Areas (optional, `--estimate-area`)

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), which classifies every Swiss building. 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 if no classification is available. Based on the Canton Zurich methodology (Seiler & Seiler, 2020).

Column	Format	Required	Source	Description
`gkat`	integer	no, from GWR	GWR	Building category code (broad, e.g. 1020 = Residential)
`gklas`	integer	no, from GWR	GWR	Building class code (specific, e.g. 1110 = Single-family house)
`gbauj`	integer	no, from GWR	GWR	Construction year
`gastw`	integer	no, from GWR	GWR	Number of stories (from register, not estimated)
`floor_height_used_m`	float	yes	Lookup	Floor height used for estimation — average of the min/max range for this building type (m)
`floors_estimated`	integer	yes	Computed	Estimated number of floors: `height_minimal ÷ floor_height`, rounded
`area_floor_total_m2`	float	yes	Computed	Gross floor area: `footprint area × estimated floors` (m²)
`area_accuracy`	string	yes	Computed	`high` (±10–15%) / `medium` (±15–25%) / `low` (±25–40%) — based on how well-defined the floor height is for this building type
`building_type`	string	yes	Lookup	Human-readable building type from floor height lookup (e.g. `Single-family house`)
`status_step4`	string	yes	Computed	`success` / `skipped` / `no_volume` / `height_exceeds_200m`

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 1 m² 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
Roof eave estimation	`elevation_roof_min_m` picks the lowest surface point, which may hit ground-level features (overhangs, passages) rather than the actual roof edge
Sloped terrain	Volume is measured from `elevation_base_min_m` (the lowest terrain point) as a flat datum. On steeply sloped sites, this includes terrain undulation between the low and high sides of the building.
Duplicate footprints (CSV mode)	If multiple CSV coordinates fall within the same AV building polygon, each match produces a separate output row with the same volume. The total volume cannot be derived by simply summing the output — deduplicate by `av_egid` or `fid` first if you need building-level aggregation.

Future Development

Feature	Description
Watertight 3D mesh	Generate closed building geometry from elevation data. swisstopo provides an official 3D buildings dataset (swissBUILDINGS3D), but quality varies significantly between buildings.
Roof geometry estimation	Classify roof shapes (flat, gable, hip, etc.) and estimate roof surface areas from 3D mesh or elevation profiles.
Outer wall quantities	Estimate exterior wall areas from building footprint perimeter and height metrics.
Material classification	Investigate building material detection from imagery or other data sources — expected to be challenging.
International buildings	Extend support beyond Switzerland. The Swiss federal real estate portfolio includes buildings worldwide, requiring alternative elevation and cadastral data sources.

Project Structure

area-estimator/
├── python/                            ← unified pipeline (Steps 1–4)
│   ├── main.py                           CLI entry point
│   ├── footprints.py                     Step 1: load footprints / coordinates
│   ├── grid.py                           Step 2: aligned 1×1m 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 (same as python, requires license)
├── tools/
│   ├── roof-estimator/               ← roof shape analysis from 3D meshes
│   └── green-roof-eval/              ← green roof detection (FME-based)
├── legacy/                            ← original implementations (reference)
│   ├── volume-estimator/
│   ├── area-estimator/
│   ├── base-worker/
│   └── swisstopo3d-volume_DEPRECATED/
├── data/                              ← .gitignored
│   ├── output/                           pipeline results CSV + logs
│   ├── 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 STAC API	data.geo.admin.ch/api/stac/v1
STAC API Docs	geo.admin.ch/de/rest-schnittstelle-stac-api
swisstopo Search 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

License

MIT License — see LICENSE.

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Swiss Building Volume & Area Estimator

Model Overview

Command-Line Reference

Examples

Setup

Example 1 — Portfolio list against AV footprints (spatial join)

Example 2 — Process all buildings in Switzerland (AV footprints)

Example 3 — Quick test with auto-fetch

Inputs

Required Data

Input Columns (coordinates CSV, `--coordinates`)

Outputs

Step 1 — Footprints

Step 2 — Grid

Step 3 — Volume & Heights

Step 4 — Floor Areas (optional, `--estimate-area`)

Limitations

Future Development

Project Structure

Floor Height Lookup

References

License

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images		images
legacy		legacy
python		python
tools		tools
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LICENSE		LICENSE
README.md		README.md

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Swiss Building Volume & Area Estimator

Model Overview

Command-Line Reference

Examples

Setup

Example 1 — Portfolio list against AV footprints (spatial join)

Example 2 — Process all buildings in Switzerland (AV footprints)

Example 3 — Quick test with auto-fetch

Inputs

Required Data

Input Columns (coordinates CSV, --coordinates)

Outputs

Step 1 — Footprints

Step 2 — Grid

Step 3 — Volume & Heights

Step 4 — Floor Areas (optional, --estimate-area)

Limitations

Future Development

Project Structure

Floor Height Lookup

References

License

About

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Input Columns (coordinates CSV, `--coordinates`)

Step 4 — Floor Areas (optional, `--estimate-area`)

Packages