The document summarizes a presentation from the Swiss Territorial Data Lab (STDL) about applying data science techniques to geospatial challenges in Switzerland. STDL introduced their 4D platform for analyzing temporal geospatial data, described projects using machine learning to detect buildings and swimming pools from aerial imagery to update government registries, and discussed opportunities for cantons to use STDL's tools and methods. A representative from the canton of Thurgau provided their perspective on how STDL's work could help complete data migrations, identify unauthorized construction, and enable new applications beyond official surveys.

1. FHNW COLLOQUIUM
March 16, 2021
SWISS TERRITORIAL DATA LAB
APPLIED DATA SCIENCE
Raphael Rollier - Nils Hamel – Huriel Reichel
Adrian Meyer - Christian Dettwiler

2.  Introduction about the Swiss Territorial Data Lab
 4D Platform
 Register of Buildings and Dwellings
 Objects Detection
 A Cantonal perspective
 Q&A
Program

3. A co-creation
project
A way to share
and replicate
A space for
experimentation
Swiss Territorial Data Lab (STDL) is...

4. If you want to go fast, go alone. If you want
to go far, go together
STDL partners are…

5. Solving concrete problems in public
administrations with Geo Data Science
STDL mission is…

6. What is the most effective way to find out the construction
date of buildings to complete the Building Register ?
How can I detect automatically changes in the field
in order to update the Land Register more rapidly ?
How can I improve the monitoring of solar energy
usage by detecting automatically panel installations ?
How can I monitor more effectively the
development of mining ?
The type of challenges we are exploring…

7. FHNW COLLOQUIUM
March 16, 2021
THE TIME DIMENSION
RBD COMPLETION RESEARCH PROJECT
Nils Hamel – Huriel Reichel

8. STDL 4D PLATFORM
SRTM EXAMPLE – 470 GB (ASC)

9. 2009-10 2013-04 2017-04
THE TIME DIMENSION
EXAMPLE OF GENEVA LIDAR – 250 GB (LAS)

10. THURGAU – 2020-10-17
INTERLIS – ITF
THURGAU – 2020-10-13
INTERLIS – ITF

11. TEMPORAL DERIVATIVE
2020-10-13 & 2020-10-17

12. REGISTER OF BUILDINGS & DWELLINGS
Federal Statistical Office (OFS/BFS) & STDL
●
Federal Register
●
Missing buildings construction years
●
Automating construction years gathering
Two complementary research approaches
PROJECT

13. NATIONAL MAPS
Using swiss 1:25’000 national maps
●
Tracking the buildings on the maps
●
Detection of their appearence
●
Covering 2020 to 1950
APPROACH

14. SWISS NATIONAL MAPS
HOMOGENEOUS AND STABLE METHODOLOGY
Emergence of the notion of 3D raster
2010 2004 1998 1993

17. DEDUCTION PROCESS
DETECTIONS & MORPHOLOGICAL CRITERION
Simple case
Complex case

18. VALIDATION METRIC
RESULTS ASSESSMENT
Manually gathered sets of synchronous buildings
Register : 1962 – National Maps : 1960-1964

19. RESULTS ASSESSMENT
NATIONAL MAPS APPROACH
With an average temporal resolution of 5.8 years : 84.7%

21. STATISTICAL
Using statistical urban model
●
Workaround the lack of maps
●
Improve construction years approximation
●
Covering years before 1950
APPROACH

22. THEORETICAL BACKGROUND
BURGESS URBAN MODEL
Pattern in urban growth & spatial dependence

23. METHODOLOGY
FILLING THE GAPS IN THE DATABASE
Temporal variance to compute spatial radii

24. METHODOLOGY
SPATIO-TEMPORAL CLUSTERING
Approximation of urban model : segment in ranges

25. 1953
1955 1959

26. RESULTS ASSESSMENT
STATISTICAL APPROACH
95% of building correctly placed within a 31 years interval

27. ●
National Maps Approach
84.7% within ±5.8 years
●
Statistical Approach
95 % within ±31 years
●
The importance of Time Dimension
Provides relevant information
CONCLUSION

28. FHNW COLLOQUIUM
March 16, 2021
OBJECT DETECTION FRAMEWORK
Adrian Meyer

29. STDL
OBJECT
DETECTION
FRAMEWORK
Generating a Model from
Cadastral Vectors and
Aerial Images
Predicting Objects in the
Same or a New Area of Interest
TRAINING
with Known Objects
INFERENCE
for Unknown Objects
BASIC IDEA

30. WORKFLOW
TRANSFER
LEARNING
Massive
Training
Dataset A
Acquired
Knowledge
Deep Learning
System
B(A)
Small Specific
Dataset B
Exported from
Cadastre
Deep Learning
System
A
New Predictions
Source: https://cdn-media-1.freecodecamp.org/images/1*lMEd6AcDmpH0mDzBHyiERw.png
Source: https://medium.com/swlh/object-detection-and-instance-segmentation-a-detailed-overview-94ca109274f2

31. Area of Interest AoI
Training AoI with Pool Labels
Prediction AoI

32. ZONING PLAN
Canton Thurgau
LAYER
WATER BASINS
Cadastre Export
Exclusion of Industrial Areas

33. AoI Tiling
Legend
Cantonal Boundary
AoI Boundary
Labels to be checked
Outlier Labels (discarded)

34. TRAINING
Ground Truth Generation:
Dataset Evaluation Split
80% Training
Used to Train Model Weights
10% Validation
Tuning Model Parameters
10% Test
Unbiased Assessment
Ground Truth Labels

35. 7
DEEP LEARNING

36. RESULTS
How Good Did We Do?
# True Positives
# False Negatives
# False Positives

37. Registered &
Undetected (FN)
Detected, but
not registered (FP)
Registered &
Detected (TP)
Wikimedia Commons, 2021

38. F1 Score
The F1 Score is the Harmonic Mean of Precision and Recall.
Wikimedia Commons (2021)
P. Mirla (2018), via github.io

39. Metrics
Test Dataset at Zoom Level 19
Trained on SWISSIMAGE from Geneva and Neuchatel
Precision and Recall are a Function of the Confidence
84%

40. Thurgau Predictions:
Cadastre Updates
True Positives
• Threshold THR ≥ 5%
• 2‘227 of 2‘959 Pools detected
• 75% Detection Success
 25% (732) «wrongly» in
Cadastre ?

41. Thurgau Predictions:
Cadastre Updates
False Negatives
• 732 of 2‘959 Segments
>5m² missed
 25% «wrongly» in cadastre?

42. Thurgau Predictions:
Cadastre Updates
False Positives
• Threshold THR ≥ 97%
• 271 non-listed Pools
 9% missing in Cadastre?
• Threshold THR ≥ 94%
• 672 non-listed Pools
 23% missing in Cadastre?

43. Manual
Evaluation
Frauenfeld
Visible Pools
Dataset Metrics
True Positive: 81
False Positive: 9
False Negative: 18
F1-Score: 85.7%
Precision: 90.0%
Recall: 81.8%
- - - - - - - - - - - -
Detector Metrics
True Positive: 94
False Positive: 16
False Negative: 5
F1-Score: 90.0%
Precision: 85.5%
Recall: 94.9%

44. Swimming Pools: Zoom Level Results
Zoom Level 15
≈ 480 cm/px GSD
16
≈ 240 cm/px GSD
17
≈ 120 cm/px GSD
18
≈ 60 cm/px GSD
19*
≈ 30 cm/px GSD
File System Load
1’949 Tiles
= 0.4 GB
4’216 Tiles
= 1.3 GB
12’817 Tiles
= 4.0 GB
42’990 Tiles
= 11 GB
154’861 Tiles
= 40 GB
Processing Duration
(Prep. / DL+Pred. / Postp.)
± 25 min ± 40 min ± 120 min = 2 h ± 240 min = 4 h ± 1’200 min = 20 h
Max. F1 Score on
TST Dataset
55.5 % 75.3 % 82.5 % 83.4 % 84.1 %
* Zoom Level 20 contains 583’014 Tiles = 179 GB and was expected to take ~120h, but aborted prematurely due to bandwidth memory errors

45. What’s Next for the
End Users?
Update Cadastre
Send Letters to Swimming Pool Owners
Think about Detectable Objects and
Contact Us!

46. Silage Bale Detector
Labeling Strategy
- Manually Digitizing 200 Stacks of
Silage Bales in QGIS
- Training a Preliminary Detector
- Use 300 Highest-Confidence
Predictions as New Labels
- Manual Correction and Filling In of
Complete Training Tiles
Result
700 Labels in 1.5 Days
Wikimedia Commons (2021)

47. New Elements: Silage Bales
Zoom Level 16
≈ 240 cm/px GSD
17
≈ 120 cm/px GSD
18
≈ 60 cm/px GSD
19
≈ 30 cm/px GSD
20
≈ 15 cm/px GSD
File System Load
8’000 Tiles
= 1.3 GB
25’000 Tiles
= 8.0 GB
84’000 Tiles
= 26 GB
310’000 Tiles
= 80 GB
1’310’000 Tiles
= 320 GB
Processing Duration
(Prep. / DL+Pred. / Postp.)
± 40 min ± 120 min = 2 h ± 240 min = 4 h ± 900 min = 15 h ± 6’000 min = 100 h
Max. F1 Score on
TST Dataset
52.5 % 74.7 % 87.2 % 92.3 % 90.9 %

48. Silage Bale Detector

49. Silage Bale Detector

50. SWISS TERRITORIAL DATA LAB
APPLIED DATA SCIENCE
Sicht eines Kantons: Thurgau
(Einbezogen in die Projekte 4D, Schwimmbäder, Ko-
Produktion Hoheitsgrenzen, Siloballen)

51. A Cantonal perspective
Potenziale aus Sicht eines Kantons: 4D-Plattform
 Die amtliche Vermessung ist Referenzdatensatz!
Änderungen wirken sich auf viele «aufbauende
Geodaten» aus. => Information wertvoll
 Datenmigration auf neue IT, neues Datenmodell:
Bleiben die Daten korrekt und vollständig?
 Bauverwaltungen: Entsprechen die Änderungen
den neu bewilligten Bauten? Gibt es nicht bewilligte
Bauten?
SECTION

52. A Cantonal perspective
Potenziale aus Sicht eines Kantons: Objektdetektion
 Schwimmbäder: Im TG eher sekundär, als Test der
KI-Tools wertvoll
 Es gibt viele andere Potenziale: Bsp: Siloballen,
Echo des Landwirtschaftsamtes: Begeisterung
Die Objektdetektion sollte nicht auf das Thema
amtliche Vermessung eingeschränkt werden. Die
Kunden haben vielfältige Interessen, diese sollten
wir «abholen». Das stärkt unsere Position.
SECTION

53. A Cantonal perspective
Potenziale aus Sicht eines Kantons: STDL = Toolbox
 Die STDL-Tools können mit etwas Phantasie und
vor allem mit Kenntnis der Kundenbedürfnisse breit
eingesetzt werden.
Leitsatz Amt für Geoinformation Thurgau:
Wir schaffen mit Geoinformation volkswirtschaft-
lichen Nutzen.
Die STDL-Toolbox hilft uns dabei.
SECTION

54. FHNW COLLOQUIUM
March 16, 2021
SWISS TERRITORIAL DATA LAB
APPLIED DATA SCIENCE
Nils Hamel – Huriel Reichel – Adrian Meyer
Raphael Rollier – Christian Dettwiler
info@stdl.ch
stdl.ch