1. URISA BeSpatial 2017 City of Toronto Ryan Garnett
Improving Topographic Mapping Currency Through a Hybrid Data Collection Method
URISA BeSpatial 2017
City of Toronto
Ryan Garnett

2. The Geospatial Competency Centre
Unit within I&T Division
Corporate resource for:
City’s 44 divisions
City’s Agencies, Boards and Commissions
City’s strategic partners
Staff compliment of approx. 40
Responsible for maintaining:
Enterprise Geospatial Environment
City’s foundation geospatial land base information
Promoting and assisting with implementing geo into City operations
Uphold and deliver on the City’s eCity strategy

3. City of Toronto’s Topo Mapping Program
Current specification originally created in 1999
~630 km²
921 index areas
Consists of 220+ layers
Dat/Em Summit Evolution
Collected and stored as .dgn

4. Topo data business usage
Topographic data is used by many City divisions
Internal City usage
External – supplied to consultants
80% of all supplied data is requested from five divisions
Engineering Construction Services
City Planning
Parks, Forestry & Recreation
Transportation Services
Toronto Water
Internal usage consists of:
Reference
Pre-design
Modelling
Data enhancement/creation
Business analysis
$20
million
approx.
est. annual operational
savings using topo data
*based on information associated to major road survey

5. Feature Collection Comparison Method
Four land classes
Suburban
Industrial
Rural
Urban
Two different data collection methods
Stereo compilation
6cm stereo imagery
Dat/Em Summit to DGN
Heads-up digitizing
8cm ortho images
ArcGIS to file GDB

6. Data collection results

7. Comparing spatial accuracy
Standard spatial statistics could not be performed
Spatial comparisons were performed on various features
Catch basins
Poles
Edge of road
Water course
Buildings
User interpretation greatly influenced the results

8. Comparing spatial accuracy – point geometry
Data cleansing was required
Trees were removed from the analysis
Only features within proximity
Proximity analysis on catchbasins and poles to 3D counter part
Within 12cm, 25cm , 50cm and 1m
12cm
25cm
50cm 1m
Suburban
catchbasin
48.2%
87.5%
99.1%
100.0%
pole
12.3%
33.8%
65.1%
86.2%
Industrial
56.2%
92.0%
97.1%
97.8%
18.9%
55.0%
75.6%
90.0%
Rural
---
8.3%
43.8%
66.7%
89.6%
Urban
43.5%
95.7%
9.0%
32.9%
67.7%
97%
2D point features
with 50cm

9. Comparing spatial accuracy – line geometry
Analyze roads, railway and watercourse
Compare feature length
Visually inspect completeness
Land Class
Feature
2D Length (metres)
3D Length (metres)
3D to 2D Diff (metres)
Diff%
Suburban
edge of road
15,635.32
15,646.77
11.45
0.07%
watercourse
555.76
631.80
76.04
12.04%
Industrial
4,512.08
4,650.97
138.89
2.99%
railway
2,124.01
2,133.35
9.34
0.44%
Rural
2,765.72
2,766.40
0.68
0.02%
825.30
825.29
-0.01
0.00%
2,939.66
2,481.82
-18.45%
Urban
12,885.54
13,610.64
725.10
5.33%
Land Class
Feature
Impact/Reason
Length (metres)
Suburban
watercourse
end of river closed off
23.59
island collected as edge
30.72
Industrial
edge of road
collection difference from trees
134.12
Urban
collection difference from shadows
752.85

10. Comparing spatial accuracy – polygon geometry
Analysis on buildings
Two spatial comparisons
Shape
Relative accuracy
Building shape compared area
Relative accuracy compared centroid locations
relative accuracy
shape

11. Impacts to building accuracy
User interpretation
Location, location, location
Source data

12. Feature Collection Time Analysis
Time was recorded based on:
Collection method (2D / 3D)
Land classification
Feature
City of Toronto topo land class
Suburban: 782 (84.9%)
Industrial: 9 (1.0%)
Urban: 71 (7.7%)
Rural: 59 (6.4%)
2D Collection
Suburban
Industrial
Urban
Rural
Building 23
3.25
4.75
0.25
Tree, Treed Area 3
1.5
0.5
0.75
Pole 1
1.25
Railway
Watercourse
Edge of Road 2
1.75
Catchbasin
Parking Lot
3.5
Sidewalk
TOTAL
31.75
13.25
11.75
3D Collection
Suburban
Industrial
Urban
Rural
Building 26
4.5 5
0.5
Tree,Treed Area 8 6
1.75
Pole
1.5
3.5
Railway
0.25
Watercourse 1
Edge of Road 2 13
Catchbasin
2.5
2.75
Parking Lot
14.25
Sidewalk 4 21
TOTAL 46
32.5
47.5
11.5
Full City Collection 10
years
2D collection 21
years
3D collection

13. Feature Update Cycle – Rate of Change
Evaluation of change between 1999 to 2016
Visual comparison of ortho images
Four change classes
0-25% change
26-50% change
51-75% change
76-100% change
217 topo index areas were evaluated
Suburban: 78 (10%)
Industrial: 9 (100%)
Urban: 71 (100%)
Rural: 59 (100%)

14. Service Improvement A hybrid approach would save effort and cost
2D collection for
Suburban
Industrial
Rural
3D collection for urban
Modified approach improve the update currency by a year
Resulting in a 2.5 to 3 year update period

15. Next Steps… Exploring methods for automated feature extraction
Image classification
LiDAR extraction
Initial results are promising
Buildings
Trees/treed area
Water
Buildings, trees and water represent ~73% of effort
Working with divisions on distributed content management
Move towards a location based specification
Land class and features influence the accuracy and collection

16. Take Away and Concluding results…
Feature interpretation has the greatest impact
Need for standardized feature “collection rules”
Great opportunities for increased efficiencies
Significant cost savings and improved service delivery

17. Thank You
Thank You
Questions?