Stationary traffic takes up a large proportion of the space in urban areas. Against the background of the enormous increase in space requirements for housing, jobs and infrastructure in recent years, which conflicts with the demand for more public open spaces and local recreation areas, the question arises as to how to deal with the high land consumption of stationary traffic from parking areas.
As a first step towards a comprehensive change in the situation, it is crucial to take stock of the current situation. An individual parking space analysis based on remote sensing and geodata provides the important basis for all urban planning considerations.
Our data package “Parking Space Analysis” supports well-founded decision-making in urban planning and environmental assessment.
The precise and comprehensive data analyses provide our customers with a solid basis for planning and environmental protection measures and enable complex environmental challenges to be tackled more efficiently.
Do you need parking space analyses for your city? Talk to us!
Other Information and articles (German):
FOSSGIS2024 presentation
Information of BBSR
Information of multiplicities
News from city of Dortmund
Article from stadtvonmorgen.de
Article from welt.de
Our parking space analysis includes data processing and analysis in four central aspects:
1. Identification of large-scale parking areas
Detection of parking areas: All relevant parking spaces are automatically detected and identified as polygons.
Combination with cadastral data: Integration of ALKIS and OpenStreetMap for comprehensive coverage.
Parking area characteristics: Information on size, compactness, and number of parking spaces is provided.
Available extensions: On-street parking areas can also be considered upon request.
2. Classification of parking surface types
Sealed surfaces: Roads, paths, asphalt, and other sealed surfaces are classified.
Unsealed surfaces
Vegetation: Classification of vegetation into “tall vegetation” (trees) and “low vegetation” (non-tree vegetation).
Buildings and infrastructure
Water bodies: Detection of water bodies within the parking area (e.g., ponds or lakes).
Data format: Surface classification is provided as GeoTIFF or in vector format (GeoPackage).
3. Tree detection with tree parameters
Tree positions: Individual trees are automatically detected and provided as point vectors.
Tree height and canopy data: Calculation of tree height (maximum and 95th percentile) as well as canopy diameter, area, and volume (assuming a spherical canopy).
Data basis: Use of high-resolution point cloud and RGBI aerial survey data.
4. Geodata processing
Greening level and environmental factors: Analysis of greening level, sealing, and other environmental factors.
Surrounding information: Consideration of surrounding building structures and proximity to nature and landscape conservation areas.
Standard land values: Consideration of standard land values (if available) to evaluate economic potential.
