Slides of the talk for data science Leuven on 18 September 2018: Some high level ideas what makes geographic data interesting and why it is a field to watch (or get into) in the near future.

1. Challenges
of Map Data
Processing

3. ?

4. ?

5. ?
Shite,
turns oot
this isnae
th' way
tae th'
bloody
pacific!

6. Cartography:
The oldest Data Science?
Geographic
Data
(Locations, Routes...)
Applications
(Navigation, Planning...)

7. New Applications,
New Data,
New Challenges
ApplicationsGeographic
Data

8. New Applications,
New Data,
New Challenges
Better
Bigger
Faster
... Harder, Stronger!

9. What is a map anyway?
• Geometries
• Names (multiple languages)
• Addresses
• Historical Speeds
• Voice instructions
• 3D buildings
• …
Data is already large and
growing!
Bigger

10. Size of map data is already
large and growing.
Standard Big Data tools
work...
… but some things are
quite unique:
Our data can be looked
at both as a graph or
as a table.
We need to do
geospatial
operations on top of
big data platforms.
Bigger

11. ApplicationsGeographic
Data
• Need more details
than humans can
map out alone.
• Need to be very
precise.
• Enough satellites
to get a new picture
of earth daily.
• Better
price/quality of
images
• Cloud solutions
available.
Better

12. Source: Zhang,Liangpei& Xia, Gui-Song &Wu,Tianfu & Lin, Liang &Tai,Xue-Cheng.(2015). DeepLearning for Remote Sensing ImageUnderstanding. Journal ofSensors.501.
173691. 10.1155/2016/7954154.
Model/NetworkTraining
Convert to vector,
simplify.
Better

13. • Detect Building
Footprints
• Remove known
Address Points
• Identify zones
for investigation
Better

14. 1.5bn map changes / month
map as piece of paper
map as data
map as a stream?
Faster

15. This crew is looking for shipmates!
@DMescheder
daniel.mescheder@tomtom.com