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Austriadownloader is a Python package for downloading Austrian Cadastral and corresponding Orthophoto data for Deep Learning applications.

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austriadownloader

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Getting Started

Clone this repository to start developing. All required datasets are available in austriadownloader/austria_data/ and can be created by executing austriadownloader/austria_data/metadata_creation.py.

To access and download Austrian Orthophoto and matching cadastral classes, execute the demo.py script. Provide POIs as a dataframe with the following scheme in the WGS84 format (EPSG:4326):

Column Type Description
id str Unique identifier for each location
lat float Latitude coordinate in decimal degrees
lon float Longitude coordinate in decimal degrees

An example for a config file:

id lat lon
0 47.6615683485 15.9040047148
1 47.6730783029 15.9045680914
2 47.6845882247 15.9051317152
... ... ...

Other input parameters are:

Column Type Description
data_path Path or str Input path for sampled POI table.
pixel_size float Pixel resolution in meters. Must be a predefined value from (0.2, 0.4, 0.8, ... 204.8)
shape tuple[int, int, int] Image dimensions as (channels, height, width). Channels must be 3 (RGB) or 4 (RGBN).
outpath Path or str Directory path where output files will be saved.
mask_label list, tuple[int] or int Cadastral mask(s) to be extracted. A single cadastral label will result in a binary mask, if several cadastral classes are provided a multi-label mask is generated.
mask_remapping Dict (default: None) Allows the selection and merging of several cadastral classes.
create_gpkg bool (default: False) Indicates whether vectorized but unclipped tiles should be saved as .GPKG.
nodata_mode str (default: 'flag') Mode for handling no-data values ('flag' or 'remove').
nodata_value int (default: 0) Value assigned to no-data pixels.
outfile_prefixes Dict Custom name assignement for ouput files: raster -> input, vector -> target
verbose bool (default: False) Providing verbose comments during script execution.

Available Classes

To select your class labels, select one or more from the following list:

Category Code Subcategory
Building areas 41 Buildings
83 Adjacent building areas
Water body 59 Flowing water
60 Standing water
61 Wetlands
64 Waterside areas
Agricultural 40 Permanent crops or gardens
48 Fields, meadows or pastures
53 Vineyards
57 Overgrown areas
Forest 55 Krummholz
56 Forests
58 Forest roads
Other 42 Car parks
62 Low vegetation areas
63 Operating area
65 Roadside areas
72 Cemetery
84 Mining areas, dumps and landfills
87 Rock and scree surfaces
88 Glaciers
92 Rail transport areas
95 Road traffic areas
96 Recreational area
Gardens 52 Gardens
Alps 54 Alps

Results

Multi-label mask with all available cadastral classes selected (not all are present in the selected sample):

RGB Orthophoto Multi-label mask

General overview of different cadastral classes merged into a binary mask:

Cadastral classes

Selection of unique cadastral classes:

Unique classes

Citation

This repository was created for a presentation at the AGIT 2025 conference.

Repository initiated with fpgmaas/cookiecutter-poetry.

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