config.toml

## Overview ##
# This example configuration file describes the available parameters for the LoD1.3 building-reconstruction flowchart for the geoflow lod13tool

### I/O ###

# the input point cloud files. In LAS or LAZ format. Can be a list of files separated by a space.
INPUT_LAS_FILES = "/data/file1.laz /data/file2.las /data/file3.las"

# input dataset with footprints. Can be a vector file or a postgresql database [connection string](https://gdal.org/drivers/vector/pg.html)
INPUT_FOOTPRINT_SOURCE = "/data/footprint.gpkg"

# output directory for CityJSON files
OUTPUT_CITYJSON_DIR = "/output/cityjson"

# output directory for OBJ files
OUTPUT_OBJ_DIR = "/output/obj"

# output to postgreSQL DB
OUTPUT_DB_CONNECTION = "PG:dbname=databasename port=5432 host=localhost user=username password=password1234"

# DB table names to outpur to
OUTPUT_LAYERNAME_LOD11_2D = "LOD12_2D"
OUTPUT_LAYERNAME_LOD12_2D = "LOD12_2D"
OUTPUT_LAYERNAME_LOD12_3D = "LOD12_3D"
OUTPUT_LAYERNAME_LOD13_2D = "LOD13_2D"
OUTPUT_LAYERNAME_LOD13_3D = "LOD13_3D"

# in case of writing to PostgreSQL, the connection string would be like this:
# OUTPUT_SOURCE_LOD12_2D = "PG:dbname=databasename port=5432 host=localhost user=username password=password1234"
# specify the schema to write to as:
# OUTPUT_LAYERNAME_LOD12_2D = myschema.LOD12_2D

# overwriting output. If set to false the output features will be appended to the (existing) output source.
overwrite_output = true

# output vector format. See [OGR documentation](https://gdal.org/drivers/vector/index.html). For example `GPKG` or `PostgreSQL`.
OUTPUT_FORMAT = "PostgreSQL"

# name of boolean attribute in input footprints that tells if the building should be skipped for full LoD1.3+ reconstruction. Eg for greenhouses and very big industrial buildings, since these are difficult/slow to process due to glass roofs and large footprint area.
skip_attribute_name = "kas_warenhuis"

### Reconstruction parameters ###

# the values below are tuned for AHN3

# number of neighbors used for normal estimation
normal_estimate__k = 8


# number of neighbors used for region growing plane detection
plane_detect__k = 15

# maximum distance between plane and its inliers
plane_detect__epsilon = 0.2

# minimum number of inliers in each plane
plane_detect__min_points = 15

# maximum angle between plane normal and inlier normal, expressed as the dot product between the two normals. Eg 1.0 is a 0 degree angle and lower values are larger angles.
plane_detect__normal_angle = 0.75

# maximum distance between detected line and its inliers
line_detect__epsilon = 0.4

# Minimum number of points in a line (algorithm will first try to find lines with this `upper` minimum)
line_detect__min_points_upper = 10

# Absolute minimum number of points in a line (if it fails with the `upper` minimum algortihm wil lower the minimum gradually to this `lower` minimum)
line_detect__min_points_lower = 5


# Elevation percentile to determin the elevation of roof parts. NOTE: only the detected planes on the roof are considered here (no facade points, no points on small surfaces such as chimneys)
roof_elevation__percentile = 0.7

# Step height used in LoD1.3 generalisation; roofparts with a height difference smaller than this value are merged.
lod13__step_height = 3.0

### CityJSON settings and metadata ###
# name of attribute to use for the cityobject ID in the CityJSON output. Should be unique.
id_attribute = "identificatie"

CJ_DATASET_TITLE = "3D basisbestand"

# Run reference that will be put in the CityJSON citymodelIdentifier field
RUN_REFERENCE = "v2022"

# Tile identifier that will be used to name the OBJ and CityJSON output
TILE_ID = 1