QUALITY ASSURANCE and POTENTIAL APPLICATIONS of a HIGH DENSITY LiDAR DATA SET for the CITY of NEW YORK

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1 QUALITY ASSURANCE and POTENTIAL APPLICATIONS of a HIGH DENSITY LiDAR DATA SET for the CITY of NEW YORK Sean C. Ahearn, Director and Professor Hyo Jin Ahn, Ph.D. Center for Analysis and Research of Spatial Information (CARSI) Hunter College-The City University of New York ASPRS Annual Conference May 4, 2011

2 NYC LiDAR 2010 Collections (April 14 May1)

3 NYC LiDAR 2010 (4/14-5/1) Collection Parameters Sanborn Inc. Aircraft altitude:1,100m, Airspeed:120 knots Pulse rate: 94,700 Hertz Scan angle: ~28 degrees Laser light range: 1,064 nm(near-infrared) (system: Airborne Laser Scanner (ALS)-50) *Overall Data Collection for NYC 362 sq miles (937 sq km): 15 billion points, point density 8-12 pts per sq m

4 OBJECTIVE To provide the quality assurance techniques for evaluating the quality of LiDAR data Applications: DSM for Solar New York Application etc.

5 Quality Assurance for LiDAR Data Minimum density of 8 points per m 2 Accuracy: vertical 9.24 cm, horizontal 10 cm RMSE Classification (ASPRS) 1 Unclassified 2 Bare Earth 7 Noise 9 Water 12 Overlap

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7 LiDAR Density of NYC

8 GCPs for VERTICAL ACCURACY ASSESSMENT

9 Manhattan dz(cm) Queens dz(cm) Staten Island dz(cm) Linear (dz(cm)) y = x y = x y = x Mean dz = cm SQRT = 6.69 cm # GCPs: Mean dz= cm SQRT= 6.80 cm # GCPs: Mean dz = 2.02 (cm) SQRT= 7.64 (cm) # GCPs: 100 Bronx Brooklyn dz (cm) dz(cm) y = x y = x Mean dz =-3.27 (cm) SQRT= 7.30 # GCPs: Mean dz= SQRT=8.95 #GCPs: 721

10 VERTICAL ACCURACY NYC 5 Boroughs RMSE (cm) Mean of Elevation Differences (cm) # of Ground Checkpoints Status Manhattan PASS Queens PASS Staten Island PASS Bronx PASS Brooklyn PASS MEAN=7.48 MEAN =-2.41 Total =1,722 PASS

11 HORIZONTAL ACCURACY Ten Tiles Selected From Each Borough Four Points Per Tile Checked

12 (a) Roof top with Flat & Single layer Better choice: High density building corner Low density building corner (b) Roof top with Multi layers & extruded deco in the side of building: poor choice

13 RMSE and Mean of Distance Differences between Building Footprint and LiDAR Data for Each Borough NYC 5 Boroughs RMSE (cm) Mean of Distance Differences (cm) Manhattan Queens Staten Island Bronx Brooklyn MEAN=33.08 (1.09 ft) MEAN =27.75 (0.91ft)

14 CLASSIFICATION QUALITY ASSESSMENT (~10% sample or 120/1109 tiles) Status of Class # of Tiles Percent (%) (a) No significant error found in all classes (b)class 9 (water) with omitted areas (c)class 2 with commission & Class 9 with omission areas (d)tile does not have Class 9 although the tile contains water body (e)class 2 (Ground) omitted (misclassified as Class 9) 9 8 (f)tile contains Class 0=Not Classified 3 3 (g)tile contains an area with missing data points for Class 1, and 2 (does contain overlap data) 1 0.9

15 Examples : Class 9 with omission error (Tile # ): Class1 Class2 Class9 Class 2 with commission error and Class 9 with omission error Class1 Class2 Class9

16 LiDAR as input to Solar Insolation Model Create surface model for City Testing of resolution 1 meter vs 1 foot Use building footprints to cut out rooftops Use ESRI insolation tool Calibrate with installation data

17 Collaborations SOLAR NYC: The Players These are the four project partners that are working on the SOLAR NYC Project. These partners have signed an MOU outlining specific roles and responsibilities. Sustainable CUNY on behalf of the City University of New York Mayor s Office of Long-Term Planning and Sustainability Hunter College - CUNY Our utility and state partners. These groups provide financial and advisory support. NYC Solar America Cities Advisory Board Eight industry representatives Ten non-profit solar/energy advocacy organizations Six additional government agencies We work with these City agencies on various solar initiatives. They provide expertise on how to integrate solar into their processes and programs. 17

18 CUNY Players CARSI Team Sean C. Ahearn Hyo Jin Ahn Jake Garcia Gordon Green Ylli Kellici Sustainable CUNY Tria Case Alison Klein Noah Ginsburg

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20 DSM Creation (QT Modeler) Technique 1: Hole filling by adapative triangulation, Antialiasing

21 Technique 2: Adaptive triangulation with smoothing filter by 1 bin size radius and maximum g tollerance 3 meter, spike and well removal

22 DSM

23 ESRI Solar Analyst tool Solar animation: one day The Solar Analysis calculates incoming direct and diffuse solar radiation over time for any point in the landscape based on the local topography, including obstructions like buildings and trees.

24 Solar Animation: one year

25 Usable Roof Area Estimates Criteria Minimum insolation threshold Height difference < 2 Contiguous area of > 10 m 2 Slope sd < x

26 1 Meter Resolution Insolation Calculations 24-Hour Insolation Per Building in Watt-Hours Per Square Meter 6000 y = 1.007x R 2 = RMSE = n= meter 1 foot Comparison Foot Resolution

27 Solar Panel Tilt and Azimuth Angle Adjustment

28 Solar Model Calibration

29 Model Validation

30 Model Validation Solar Pathfinder Site Analyses Multiple sites on two buildings were estimated in site analyses using the Solar Pathfinder system, which estimates shading and incoming solar radiation for a given location. The model tended to underestimate the Solar Pathfinder results. The model assumes a flat surface, whereas solar pathfinder results include a 10 degree tilt, which may explain the difference.

31 Solar NYC Portal

32 Solar NYC Portal

33 Future Phases? Google Maps/Earth, Obtaining, processing, and comparing real-time data (to incorporate factors like daily weather changes) Allowing Con Ed customers to access their individual account information while looking at their building s solar energy potential, to help calculate energy & cost savings more precisely.