Tesera Systems uses machine learning to provide high resolution forest inventory services to clients by combining imagery, vector data, and precise location data. They developed a solution using tiling schemes and generic FME workspaces to process large amounts of data from 500 to 50,000 square kilometers across multiple projects. This involves taking disparate source data like LiDAR, CIR, and plot data and generating tiled imagery, vectors, and other data layers that can be viewed and analyzed in desktop GIS and web viewers.

1. Slicing through
the forest of
data to see the
trees
Tesera Systems
Tonja Dwyer & Michael Kieser

2. Objectives
Tesera combines high-
resolution imagery, and
precise vector data in a
Machine Learning
environment to deliver High
Resolution Inventory
Services to forestry clients.

3. Challenge
● Project area 500 to 50,000 km2
● How to build data processing
pipelines
○ Generic across projects and
data type

4. Solution
Tiling Scheme
Standard across all
projects
FME Workspaces
Generic across
projects
Tiled Project Data
Universal data
storage & access
START FINISH
Disparate Source Data
LiDAR, CIR, LCC, plot

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Tiled
Imagery
over
15,000 km2

6. Example (re-tile imagery)
● Tiled imagery layers derived
from CIR and LiDAR
● Generate Google slippy map
tiled imagery layers
● Deliver layers in desktop GIS &
web viewer

7. DEMO - FME Server

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Input

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Intermediate Process

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Tilings

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Builder Process - 491 server jobs

12. Results
Result - FMEServer
Result - QGIS
viewer.hris.tesera.com

13. Benefits
● Tiling data is fundamental to
automation
● FME Workspaces require only
a few parameter adjustments
for each client
● FMECloud provides scalability

14. Tip
● BoundingBoxReplacer: 3rd party data
● SpatialRelator: project tile with 3rd party
BB
● Process by project tile
● WebMapTiler: static raster tiles
● AWS S3: storing & serving static raster
tiles
● Vector tiles: static vector tiles

15. Questions?