3D Building Roof Extraction From LiDAR Data

Transcription

1 3D Building Roof Extraction From LiDAR Data Amit A. Kokje Susan Jones NSG- NZ

2 Outline LiDAR: Basics LiDAR Feature Extraction (Features and Limitations) LiDAR Roof extraction (Workflow, parameters, results) LiDAR Roof extraction (Progress, and applications)

3 LiDAR Light Detection And Ranging Key Highlights Basics Active optical remote sensing technology, based on LASER Components GPS IMU (Inertial Measurement Unit) Scanner Functioning Scanner acquires information by emitting laser pulses to the target and receiving back the reflected signals.

4 LiDAR Basics Data LiDAR-LAS: (Standard data format) binary data with billions of points Larger areas, the data is broken up into multiple tiles for easy access and processing. The data contain : Coordinate information (X,Y) Elevation (Z) Intensity (i) RGB colour values, GPS time Classification Pulse Return number

5 LiDAR Basics Features First Return SecondReturn ThirdReturn LiDAR point cloud data is information rich and contains many attributes, not all the information is essential for a particular project Last Return DSM Various features can be extracted using a range of commercially available software Breakline DTM Last Return

6 LiDAR Feature Extraction Products Buildings Slope DSM Intensity Contours Classification

7 LiDAR Feature Extraction Buildings Most of the commercial LiDAR processing software can classify and extract buildings from raw point cloud data A set of sophisticated algorithms are used to generated building polygons based on LiDAR points classified as building

8 LiDAR Feature Extraction Limitations Building polygons extracted from raw point cloud data are Single polygons Unable to differentiate various Roof parameters (shape, orientation, slope etc.) Lack of roof information exhibits typical flat roof problem where all the extruded buildings display an identical flat roof, when rendered in 3D Single polygon buildings with Flat roof: suitable for general purpose 3D rendering and visualisation Might not be useful for precision, analysis and realistic modelling (as roof information is missing)

9 LiDAR Feature Extraction Solution Feature extraction procedure treats LiDAR points associated with a building as a single group (Base + Roof), ignoring roof parameters Breaking up the polygons into roof facades based on ridges, slope, orientation would bring up the missing roof information.

10 LiDAR Roof Identification Work Flow LiDAR point classification: LiDAR points were first classified and extracted as building points using commercially available tools. LiDAR Identification: Multiple parameters (Slope, Ridges, Contours, TIN) associated with roofs planes are identified for roof planes generation. Classification Scripting: Python is used as scripting language Building Points Integration: Individual or sets of parameters are integrated in the python script for roofs extraction and the accuracy is investigated. Generalisation*: Roof polygons are extracted and subjected to further generalisation in order to get realistic roof planes. Script *In progress Roofs

11 LiDAR Roof Parameters Min and MaX height points Script make use of existing building polygons (either user provided or extracted from LiDAR) and populate Min and Max height points within individual building Clustering and nearest neighbor methods are used.

12 LiDAR Roof Parameters Slope and Ridge lines Using Min and Max height, Slope is calculated within each cluster. Ridge lines are generated using height (Min- Max) and slope.

13 LiDAR Roof Parameters TIN and Contours Irregular spacing and low point density (about 1pt/meter 2 or less) of LiDAR points may yields less accurate ridge lines Script is modified to generate TIN and Contours using LiDAR points. TIN further processed to generate 3D features (TIN- Triangles)

14 LiDAR Roof Parameters TIN and Contours TIN features and counters provides additional reference points to generate the ridge lines Ridge lines based on Min-Max height points Ridge lines based on Min-Max, TIN features and Contours

15 LiDAR Roof Extraction Generalisation* Based on complexity of TIN, the script parameters can be modified to decimate TIN Nodes to generalise the TIN Simplified TIN Surface for a roof Ridge features generated from TIN *In progress

16 LiDAR Roof Extraction Features and Planes The script then converts Ridge features and TIN triangles then to Z-features with associated height and slope to yield Ridge features along with contours Z feature planes generated from generalised TIN and Ridge lines.

17 LiDAR Roof Extraction Planes Generalisation* The final stage of the 3D roofs extraction would be approximation of the ridge lines and planes, to generate realistic building roofs. Extracted Ridge lines Expected Shape approximation *In progress

18 LiDAR Roof Extraction What Next? The customized roof extraction tool is an on-going project and still evolving. Optimizing the Ridge line identification and TIN Decimation parameters to generate more complex roof surfaces accurately. Refining the planes generalisation / approximation algorithm by incorporating additional methods such as Polygonal Approximation, Plane adjacency matrix etc. Multi-format LiDAR data handling

19 LiDAR Roof Extraction Applications Variety of application can be benefitted by precision roof information. Planning Realistic visualisation Line of sight and coverage (for security agencies) Solar potential and available surface (For solar PV cell installation)

20 LiDAR Roof Extraction Questions?

21 Thank You