1. INPE’s Data Cube Initiatives
Lubia Vinhas
Image Processing Division
General-Coordination on Earth Observation
INPE
43 CEOS/WGISS Meeting, April , Annapolis, MD, USA

2. Forest degradation
Deforestation events
Long time series of EO data play a crucial role in our operational and research drivers
LULC change

3. Space-first time-later or time-first space-later?
Space first: classify
images separately
Compare results in time
Time first: classify
time series separately
Join results to get maps

4. E. g. MapReduce streaming analytics processing
BFAST: NDVI
time series, location
( , )
Source: Assis et al., Big data streaming for remote sensing time series analytics using MapReduce.

5. Architecture based on array databases
Architecture for Big EO data analytics
Architecture based on array databases
Array databases
Data as a collection of multidimensional arrays, instead of tables used in object- relational DBMSs.
3D array: Time-first and space-first approaches.
Examples: Rasdman and SciDB.
Source: Camara et al., Big Earth Observation Data Analytics: Matching Requirements to System Architectures.
DOI: /

6. Architecture based on array databases
Architecture for Big EO data analytics
Architecture based on array databases
MOD13Q1 for South America, since 2000 ✓
Experiments with LANDSAT ✓
CBERS AR data for the future ✓

7. WTSS - Server SciDB Analysis
TerraAmazon
TWDTW via Web
Validation tool
via web
Client
APIs
wtss.cxx
wtss.R
wtss.py
wtss.js
HTTP/GET
HTTP/JSON
Analysis
Using time series data through a lightweight web service
WTSS - Server
SciDB
Source: Vinhas et al., Web Services for Big Earth Observation Data.

8. Methods: example TWDTW
Source: Maus, Victor, et al. "A time-weighted dynamic time warping method for land-use and land-cover mapping.”

9. Methods: example TWDTW

10. Applications: LULC change trajectories
Amazonian biome of Mato Grosso state ( )

11. Applications: LULC change trajectories
2001
2016
Bolivia ( ). ~20,3 million time series.

12. Applications: LULC change trajectories
2001
2001
2016
2016
Peru ( ). ~24 million time series.

13. Platform

14. Reproducibility e-sensing
Time-Weighted Dynamic Time Warping for satellite image time series analysis – dtWSat
Satellite Image Time Series Analysis – SITS
Earth-Observation Time-Series filters –eotsfilter
Docking options under study

15. Next steps… Validation of results Improvement of the methods
Improvement of the interfaces to access data and results through OGC WMS and WCS
Domain services such as WTSS and WTSCS Web Time Series Classifying Service
Other methods and applications with our researchers and partners

16. Final considerations No cloud computing initiatives
No comercial initiatives
“A global network of interoperable data cubes” is a very interesting idea
Analysis Ready Data for Land is a key component
Driver to invest in data integration specially with CBERS-4 data
Validation
Capacity building

17. Thank you. Questions? lubia.vinhas@inpe.br
Team effort. People more directly involved with the computing:
Adeline Maciel
Andre Carvalho
Eduardo Llapa
Gilberto Camara
Gilberto Ribeiro
Karine Ferreira
Luiz Fernando Assis
Lubia Vinhas
Ricardo Cartaxo
Victor Maus
Alber Sanchez
-> People
Thank you. Questions?