1.
apidays Helsinki & North
16.3.2022
Apps without APIs
leveraging the stack that we all use,
but never think about

2.
Sampo
Savolainen
CTO
Spatineo focuses on data flows and the
intersection of Data APIs, Cloud technologies
and Geospatial
We have a SaaS platform for API monitoring &
analytics for geospatial APIs: Spatineo Monitor

3.
complexity
reliability
Complexity
Complexity has grown over time:
- frontend tech
- load balancers
- middleware
- api gateways
- cloud platforms & third party APIs
- containers
- infrastructure as code
- IaC middleware / helpers
- serverless functions
- specialized server implementations
- …
Our usual tendency is to only add
complexity. We must actively think about
this and attempt to reduce it when
possible t

4.
Tuulituhohaukka
💨💥🦅
Aalto, Forest Centre & FMI are developing an AI
model for predicting wind damages in forests
Model based on:
- Sentinel satellite imagery
- Weather data
- Forest data, terrain models
- Wind damage declarations
Image credits: https://www.copernicus.eu/en/media/images/meeting-waters, https://www.copernicus.eu/en/media/images/sentinel-3b-being-mated-rockot-adapter

5.
2022 SERIES OF EVENT
New York
JULY
(HYBRID)
Australia
SEPTEMBER
(HYBRID)
Singapore
APRIL
(VIRTUAL)
Helsinki & North
MARCH
(VIRTUAL)
Paris
DECEMBER
(HYBRID)
London
OCTOBER
(HYBRID)
Hong Kong
AUGUST
(VIRTUAL)
JUNE (VIRTUAL)
India
MAY
(VIRTUAL)
APRIL (VIRTUAL)
Dubai & Middle East
JUNE
(VIRTUAL)
Check out our API Conferences here
Wa nt t o t a lk a t one of our conference?
Apply t o spea k here

6.
The App
Spatineo built a tool for the project to
study Tuulituhohaukka model output
Allows comparing model and source data
Supports any number of maps side by
side, showing the same area and time but
different data sets
Visual comparison & time series for
changes over time
.. with lots of data: Sentinel satellites
produce 20 TB per day. FMI hosts more
than a petabyte of data, growing each
day

7.
Getting data in the browser?
API: WMS, WMTS or other standards
Expensive: 1+ PB of storage on a virtual
machine for an application geared
towards occasional research use
Still requires non-trivial computing
power for application server
(multi-gigabyte files)
Latency when accessing non-cached data
No API, direct access to resources and
processing offloaded to clients
Application server with a local copy? No full mirror, only caching Without an application server altogether
Application Server (API)
Local copy of data sets
index
Data set master storage
Application Server (API)
Local cache of data sets
index
Data set master storage
💨💥🦅 💨💥🦅 💨💥🦅
Data set master storage
index

8.
Standards based
Solution for
Tuulituhohaukka
Cloud optimized GeoTIFFS (COG)
Satellite imagery and other data
sets in a file format that provides
efficient access using capabilities
of HTTP
STAC Catalogue
Interlinked JSON-files describing
data sets and organizing the data
into collection and subcollections
providing the client with an
efficient index on available data
sets, time periods for which data is
available
https://stacspec.org/
https://www.cogeo.org/

9.
STAC
When new satellite images and other
data assets are received, they are
analyzed to produce STAC items
After items are built, they are indexed
into catalogues
These catalogues are subdivided into
per-month catalogues
Clients can index assets via the catalogue
hierarchy
The Catalogue
Asset
Item
Time
Dataset
Image credits European Union, Copernicus Sentinel-1 imagery

10.
Cloud optimized
GeoTIFF
HTTP GET Range queries
- read internal index and
metadata (tiles, overviews,
georeferencing)
- data access can be target to
only the required tiles at
required resolution
Allow clients of efficiently retrieve
only the parts of the file they need
Specialized TIFF files
Include georeferencing information
Data in an image pyramid:
- data structured in tiles
- overviews (resampled)
Internal index and metadata (georeferencing)
Level 0 tiles: full resolution Level 1 tiles L2 3
GeoTIFF
Image pyramid illustration credits: https://climserv.ipsl.polytechnique.fr/documentation/idl_help/Image_Tiling.html
Client App
💨💥🦅

11.
Metrics
Actual download size
Session with Chrome Dev Tools
cache entirely disabled:
Transferred: 250 Mb
(includes the browser re-downloading data
during this session)
Amount of source data
Maps:
● Sentinel 1: 2x 10Gb
● Sentinel 2: 3x 1Gb
● Model output: 1x 5 Mb
● x2 with comparison tool
The time series:
● 3x 20 x 1Gb images
Total: 100+ Gb

12.
#1
#2
#3
#4
#5
#6
Key Takeaways
Keep it simple, understand the basics, use
what you already have in your tech stack -
you can do a lot with just HTTP!
No application server or “serverless”
required: less maintenance, less code, less
cost, one fewer point of failure
Very fast - but does require more
bandwidth than with a server
pre-processing the data (WMS/WMTS)
No data replication or cache management -
access data directly as they are stored in the
master data store
Standards based solution helps tech work
together: COGs work as-is in openlayers,
and time series simple to produce with
geotiff.js
Because of standards, catalogue and data
usable for other applications as well

13.
16.3.2022
Sampo Savolainen
sampo.savolainen@spatineo.com
https://www.spatineo.com/
PS. We are hiring…