QALIDAR

Notes on the Dataset

  • The first epoch exhibits semantic labels for 4 different classes, the second epoch for 19 different classes.
  • Both epochs are named exactly the same, only the file extension differs. The first epoch is stored as LAS file and the second one as LAZ file.

Detailed Information

General Information
NameQALIDAR
Release Year2024
Terms of UseOpen government data
Access RequirementsNone
Dataset Size254.3 MB | Partial download is possible, i.e. the data is split into several files (e.g., epochs or data types)
DocumentationMulti-paragraph description of the dataset, e.g., in the form of a paper section or a comprehensive readme file
CodeYes
ApplicationsBuilt environment change detection and classification
Detailed ApplicationsPoint cloud label change detection/quality control
Acquisition
Number of Scenes1
Number of Epochs per Scene (minimum)2
Number of Epochs per Scene (median)2
Number of Epochs per Scene (maximum)2
General Scene TypeUrban
Specific Scene TypeCity tile
LocationLe Locle (Swiss)
Acquisition TypeAirborne laser scanning
Acquisition DeviceUnspecified aerial LiDAR
Acquisition PlatformAircraft (plane)
Scan IntervalYears
Acquisition Months
Representation
Data RepresentationUnstructured, globally aligned (e.g., point cloud or ray cloud)
Specific Data RepresentationPoint cloud
File Format/EncodingLAS | LAZ
Raw Data-
Additional Data-
Coordinate System2056
Quality and Usability
RegistrationFinely registered
Number of Partial Epochs-
Unusable Data Reason-
SplitsNo
Per-Point Attributes
Intensity/ReflectivityYes
ColorYes | Only some of the scans are colored
Semantic Labels19
Instance LabelsNo
Change Labels-
Statistics
Number of Points per Epoch (minimum)2M
Number of Points per Epoch (median)-
Number of Points per Epoch (maximum)27M
Avg. Point Spacing (minimum)7.9cm
Avg. Point Spacing (median)-
Avg. Point Spacing (maximum)2.9dm
Avg. Change Points per Epoch-

Paper Reference 1

@techreport{munger2024datasetqalidar,
    author = {Nicolas Münger and
             Gwenaëlle Salamin and
             Alessandro Cerioni and
             Roxane Pott},
    title = {Cross-generational change detection in classified LiDAR point clouds for a semi-automated quality control},
    institution = {Federal Office of Topography swisstopo},
    year = {2024},
}