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• The proposed method can automatically associate 312.4 million individual LiDAR beams from four campaigns using five sensors with 6368 individual objects in a semantic 3D city model.method 95%
• The semantic 3D city model used in the study has centimeter-level accuracy and represents four inner-city streets at Level of Detail (LOD) 3.technical 90%
• Extracted radiometric fingerprints for object surfaces reveal recurring intra-class patterns that indicate class-dominant materials.result 85%
• The method groups LiDAR observations reflected from the same semantic object under varying distances, incident angles, environmental conditions, sensors, and scanning campaigns.method 90%
• The semantic 3D city model is based on the CityGML 3.0 standard and enables fine-grained sub-differentiation of objects.technical 90%
• The incorporation of material information in semantic 3D city models remains largely untapped despite their increasing availability and detail.limitation 85%
• A structured representation of materials and their physical properties could substantially broaden the application spectrum and analytical capabilities for urban digital twins.market 80%
• The semantic model, method implementations, and geodatabase solution 3DSensorDB are publicly released under a GitHub repository.method 95%

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