Improving accuracy of automated 3-D building models for smart cities

Figures & data

Figure 1. Study area.

Figure 2. Equipment used in this research.

Figure 3. Steps (a)–(c) for developing an automated 3-D city model.

Figure 4. DTM generation process: Filtering and DTM generation.

Figure 5. Developing algorithms for automated 3-D building models.

Figure 6. Demonstrating the processes for building 3-D objects.

Figure 7. Improving positional accuracy of the 3-D building model.

Figure 8. 3-D building reconstruction utilizing inflection points acquired by TS.

Figure 9. 3-D building model by the TS technique.

Figure 10. Photorealistic 3-D building model by a terrestrial laser scanner.

Figure 11. Accuracy assessment by measuring VRSs and control points.

Table 1. Comparing horizontal and vertical locations with VRS positions.

	Differences between the horizontal locations (cm)	Differences between the vertical locations (cm)
Mean	18.7	−2.4
Standard Deviation	10.0	11.2
Dispersion	1	1.3
Range	31.7	42.5
Minimum	4.5	−2.53
Maximum	36.2	17.2
Sample size	30 buildings	30 buildings

Table 2. t-Test statistic result.

	Matched samples			t	Freedom
	Mean	SD	Avg. standard error
Modeling result vs. VRS result	−0.024	0.112	0.020	−1.176	29

Figure 12. Normal distribution of the t-test statistics.

Figure 13. Area-based assessment for correctness.

Figure 14. Comparing 3-D buildings with LiDAR point clouds.

Figure 15. Developing a GIS database and a 3-D Web GIS Server for 3-D city model.

Figure 16. 3-D building modeling and texturing generated by aerial photos.