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

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

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

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

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

Data collection results

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

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

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 -457.84 -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

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

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

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

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%)

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

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

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

Thank You Thank You Questions?