1. Object-Based Building Boundary Extraction from Lidar Data You Shao and Samsung Lim

2. Most filtering algorithms require rasterisation of lidar data Additional computing overhead Loss of information Increase of uncertainty Our method No rasterisation Adaptive window size Morphological filtering DTM generation and building detection Research Objectives

3. The UNSW Campus (1 km x 2 km) Small residential buildings, high-rise buildings, steep roads, tall trees and large green areas Lidar data (X, Y, Z, I) Airborne imagery (R, G, B) 2-year gap between the two datasets Study Area and Datasets

4. Aerial Ortho-photo

5. Lidar Intensity

6. Vertical Profile

7. Employ dilation and erosion to find the maximum or minimum measurements in lidar points An adaptive window size indicator is developed to detect building rooftops and modify the window size automatically An approximate size of a building can be detected by measuring the elevation rise and fall, and therefore the window size can be changed accordingly Proposed Adaptive Filtering

8. Adaptive Filtering (Workflow)

10. Normalised Difference Vegetation Index (NDVI) to remove vegetation Alpha-shape to form building outlines Grid-based algorithm Modified convex hull algorithm Fine-tuning with adjustable parameters to remove small residuals Approaches to Building Detection

11. Extracted Buildings

12. Unfiltered Classification Results in Residential Area

13. Filtered Classification Results in Residential Area

14. Accuracy Assessment

15. Alpha-shape algorithm

16. Grid-based algorithm

17. Modified convex hull algorithm

18. Alpha-shape Modified convex hull Boundary Extraction (1/2)

19. Grid-based Boundary Extraction (2/2)

20. B1B2B3B4B5B6B7B8Mean Alpha- shape 0.840.560.390.490.990.880.950.820.740 Modified convex hull 0.840.480.420.491.110.781.010.840.746 Grid- based 0.830.490.460.471.10.90.970.790.751 Horizontal RMSE (m, 1σ)

21. Building extraction in residential areas (Site 1)

22. Building extraction in residential areas (Site 2)

23. Building extraction accuracy

24. The proposed algorithm is suitable for steep urban areas with varying building sizes The required parameters of the proposed algorithm can be automatically determined The test results show that the proposed algorithm is able to classify ground points with a vertical accuracy of 36 cm, a horizontal accuracy of 75 cm and a commission error less than 6% As for multi-rooftop buildings, it is difficult to determine the actual size of the building; however, this problem can be solved by the proposed dual-direction process Concluding Remarks