Detecting Roofs with Potential for PV Solar Panels from Open Data Lidar

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We created a Proof of Concept with FME to detect roofs with the potential for installing solar parks from open data (Lidar). Roofs must have an area bigger than a certain threshold and have an appropriate orientation.

Technology

Detecting roofs with
potential for PV solar panels
from open data Lidar

FME
User
Conference
20
22
Jasper Wisbecq
FME Consultant at Nordend
FME Enthusiast since 2015
Working with Lidar since 2012

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FME
User
Conference
LiDAR
SystemCaller
PDAL
CloudCompare
Plane Segm
entation
Building Footprints
Open Data
N
orm
al Reconstruction
PythonCaller
Plane
Fitting

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User
Conference
Agenda
1. Introduce the problem
2. Propose a solution embedded in a single
FME Workspace
3. Future Work

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FME
User
Conference
Describing the Problem

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FME
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Conference
Describe the Problem
Proof of Concept for detecting roofs that are interesting for
operating solar parks using Open Data
Input:
• Lidar LAS ﬁles (open data)
• Building footprints (open data)
Output:
• Only Roofs with area >1000m²
• Orientation
• Slope
• Area
• Address
• Owner
• Company Number

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FME
User
Conference
Input Data
Building Footprints
Open Lidar data

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FME
User
Conference
Solution - Overview
General Steps:
1. Select all roofs with area > 1000m²
2. Clip the PointClouds
3. PointCloud ﬁltering – keep roof points
a) PointCloudStatistics based
b) Outlier detection with PDAL
4. Calculating Normals for roof points with CloudCompare
(command line)
5. RoofPlaneSegmentation (GitHub exe)
6. Plane reconstruction (Python)
7. SurfaceNormalExtractor (FME hub) - updated to Python 3
8. Slope and orientation from SurfaceNormal
9. Join other sources: owner, address, …

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Conference
Data Prep - Result

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Filter Roof Points from PointCloud
Filter 1 = PointCloudStatisticsCalculator (Z Threshold based on Z_median and Z_stdev)

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Conference
Filter Roof Points from PointCloud
Filter 2 = Outlier detection PDAL

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Filter Roof Points from PointCloud -
Result
Filter 1
Remaining
façade
points removed
Filter 2

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User
Conference
Creating Normals for Roof Points
Results with CloudCompare are better than with PDAL

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User
Conference
Roof Plane Segmentation
• Alternative: Open3D segment_plane() [future work]

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User
Conference
Roof Plane Segmentation- Result

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FME
User
Conference
Plane Fitting
z = Ax + By + C

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FME
User
Conference
Plane Fitting - Result

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FME
User
Conference
SurfaceNormal – Slope and Area
• Changed HUB transformer to Python 3

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User
Conference
Extracting Orientation

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User
Conference
Future Work
● Run on large datasets
● Analyse performance:
● Split large area into batches
● Hardware requirements
● …
● Analyse results for odd roof types
● Investigate Alternatives:
● Open3D for Roof Segmentation - segment_plane()
function
● Roof ﬁltering: necessary?
● Sarthaktum/ roofn3d?
● …

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FME
User
Conference
Complex 3D calculations
from start to end with
only one singe FME
workspace

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22
FME
User
Conference
Resources
• CloudCompare Command line mode
• PDAL – Outlier detection
• Li-Li-Whu/RoofPlaneSementation

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1. Detecting roofs with potential for PV solar panels from open data Lidar

2. FME User Conference 20 22 Jasper Wisbecq FME Consultant at Nordend FME Enthusiast since 2015 Working with Lidar since 2012

3. 20 22 FME User Conference LiDAR SystemCaller PDAL CloudCompare Plane Segm entation Building Footprints Open Data N orm al Reconstruction PythonCaller Plane Fitting

4. 20 22 FME User Conference Agenda 1. Introduce the problem 2. Propose a solution embedded in a single FME Workspace 3. Future Work

5. 20 22 FME User Conference Describing the Problem

6. 20 22 FME User Conference Describe the Problem Proof of Concept for detecting roofs that are interesting for operating solar parks using Open Data Input: • Lidar LAS ﬁles (open data) • Building footprints (open data) Output: • Only Roofs with area >1000m² • Orientation • Slope • Area • Address • Owner • Company Number

7. 20 22 FME User Conference Input Data Building Footprints Open Lidar data

8. 20 22 FME User Conference Solution

9. 20 22 FME User Conference Solution - Overview General Steps: 1. Select all roofs with area > 1000m² 2. Clip the PointClouds 3. PointCloud ﬁltering – keep roof points a) PointCloudStatistics based b) Outlier detection with PDAL 4. Calculating Normals for roof points with CloudCompare (command line) 5. RoofPlaneSegmentation (GitHub exe) 6. Plane reconstruction (Python) 7. SurfaceNormalExtractor (FME hub) - updated to Python 3 8. Slope and orientation from SurfaceNormal 9. Join other sources: owner, address, …

10. 20 22 FME User Conference Data Prep

11. 20 22 FME User Conference Data Prep - Result

12. 20 22 FME User Conference Filter Roof Points from PointCloud Filter 1 = PointCloudStatisticsCalculator (Z Threshold based on Z_median and Z_stdev)

13. 20 22 FME User Conference Filter Roof Points from PointCloud Filter 2 = Outlier detection PDAL

14. 20 22 FME User Conference Filter Roof Points from PointCloud - Result Filter 1 Remaining façade points removed Filter 2

15. 20 22 FME User Conference Creating Normals for Roof Points Results with CloudCompare are better than with PDAL

16. 20 22 FME User Conference Roof Plane Segmentation • Alternative: Open3D segment_plane() [future work]

17. 20 22 FME User Conference Roof Plane Segmentation- Result

18. 20 22 FME User Conference Plane Fitting z = Ax + By + C

19. 20 22 FME User Conference Plane Fitting - Result

20. 20 22 FME User Conference SurfaceNormal – Slope and Area • Changed HUB transformer to Python 3

21. 20 22 FME User Conference Extracting Orientation

22. 20 22 FME User Conference End Result

23. 20 22 FME User Conference Future Work

24. 20 22 FME User Conference Future Work ● Run on large datasets ● Analyse performance: ● Split large area into batches ● Hardware requirements ● … ● Analyse results for odd roof types ● Investigate Alternatives: ● Open3D for Roof Segmentation - segment_plane() function ● Roof ﬁltering: necessary? ● Sarthaktum/ roofn3d? ● …

25. 20 22 FME User Conference Complex 3D calculations from start to end with only one singe FME workspace

26. 20 22 FME User Conference Resources • CloudCompare Command line mode • PDAL – Outlier detection • Li-Li-Whu/RoofPlaneSementation

27. Thank You! jasper@nordend.eu

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