Presentation about the Belmont-funded PARSEC project at the University of Queensland 19th November 2019 on the PARSEC project.

1. Using satellite imagery and AI to discern the effects of
protected areas in your backyard, while improving the
interface between scientists and the digital world (the
PARSEC project).
Alison Specht, SEES, University of Queensland
With contributions from Shelley Stall, AGU, David Mouillot, U. Montpellier, Nicolas
Mouquet, CNRS, FRB-CESAB, Laurence Mabile, U Toulouse,
Marc Chaumont & Gérard Subsol, LIRMM.

2. The Belmont Forum is a global partnership of funding
organizations, whose operations aim to encourage:
International transdisciplinary research providing knowledge for
understanding, mitigating and adapting to global environmental
change.
The Science-driven e-Infrastructure Innovation (SEI) aims to:
• Enhance the impact of environmental change research by supporting
technological innovation that would accelerate discovery, inform policy,
and support decision making.
• Enable teams of computer and information scientists and technologists to
work together with natural and social scientists and related stakeholders
in transnational projects.
• These teams would integrate data streams and analysis systems,
amalgamate best practices from public and private sectors, and foster
open data and open access.
Belmont Forum « www.belmontforum.org »

3. The Belmont Forum is a global partnership of funding
organizations, whose operations aim to encourage:
International transdisciplinary research providing knowledge for
understanding, mitigating and adapting to global environmental
change.
The Science-driven e-Infrastructure Innovation (SEI) aims to:
• Enhance the impact of environmental change research by supporting
technological innovation that would accelerate discovery, inform policy,
and support decision making.
• Enable teams of computer and information scientists and technologists to
work together with natural and social scientists and related stakeholders
in transnational projects.
• These teams would integrate data streams and analysis systems,
amalgamate best practices from public and private sectors, and foster
open data and open access.
Belmont Forum « www.belmontforum.org »

4. The Belmont Forum is a global partnership of funding
organizations, whose operations aim to encourage:
International transdisciplinary research providing knowledge for
understanding, mitigating and adapting to global environmental
change.
The Science-driven e-Infrastructure Innovation (SEI) aims to:
• Enhance the impact of environmental change research by supporting
technological innovation that would accelerate discovery, inform policy,
and support decision making.
• Enable teams of computer and information scientists and technologists to
work together with natural and social scientists and related stakeholders
in transnational projects.
• These teams would integrate data streams and analysis systems,
amalgamate best practices from public and private sectors, and foster
open data and open access.
Belmont Forum « www.belmontforum.org »

5. https://media.institut-alternativa.org/2016/05/OpenDataInfoGraphic.jpg

6. IPBES 2019 Global Assessment Report
Demographic
and
sociocultural
Economic
and
technological
Institutions
and
governance
Conflicts
and
epidemics
Natural ecosystems have declined
by 47% on average, relative to their
earliest estimated states.
Approximately 25% of species are
already threatened with extinction in
most animal and plant groups studied.
Biotic integrity-the abundance of
naturally-present species-has declined by
23% on average in terrestrial communities,
*Since prehistory
Terrestrial
INDIRECT DRIVERS
ECOSYSTEM EXTENT AND CONDITION
SPECIES EXTINCTION RISK
ECOLOGICAL COMMUNITIES
BIOMASS AND SPECIES ABUNDANCE
NATURE FOR INDIGENOUS PEOPLES
AND LOCAL COMMUNITIES
Freshwater
Marine
Direct exploitation
Land/sea use exchange
Climate change
Pollution
Invasive alien species
Others
The global biomass of wild mammals
has fallen by 82%. Indicators of
vertebrate abundance have declined
rapidly since 1970.
72% of indicators developed by indigenous
people sand local communities show
ongoing deterioration of elements of
nature important to them.
47%
25%
23%
82%
72%
Valuesandbehaviours

8. Synthesis strand:
To combine remote sensing,
artificial intelligence and
socioeconomic data to
assess change in
socioeconomic conditions
Data strand:
To increase the number of
properly cited data sets,
provide credit and attribution,
and accurately track data
and code reuse
2018 2022Four years
PARSEC project in a nutshell

9. Synthesis strand:
To combine remote sensing,
artificial intelligence and
socioeconomic data to
assess change in
socioeconomic conditions
Data strand:
To increase the number of
properly cited data sets,
provide credit and attribution,
and accurately track data
and code reuse
Determine the
influence of protected
areas on
socioeconomic
outcomes
2018 2022Four years
PARSEC project in a nutshell

10. Synthesis strand:
To combine remote sensing,
artificial intelligence and
socioeconomic data to
assess change in
socioeconomic conditions
Data strand:
To increase the number of
properly cited data sets,
provide credit and attribution,
and accurately track data
and code reuse
Determine the
influence of protected
areas on
socioeconomic
outcomes
Improve practices
and tools for
interdisciplinary
projects worldwide
2018 2022Four years
PARSEC project in a nutshell

11. PARSEC – a transnational team
BRAZIL:
EPUSP : P Pizzigati Corrêa
NISR : J-P Ometto
São Paulo U : KMPMB Ferraz
SCIELO : S Santos
USA:
AGU: S Stall
Southern Oregon U: JE Trammell
U California: M O’Brien
The Nature Conservancy: S Reddy, J Evans
UK :
H Glaves : BGS
FRANCE:
N Mouquet : CESAB-FRB, CNRS
A Cambon-Thomsen,
L Mabile, M Thomsen: Toulouse U
M Chaumont, G Subsol: LIRMM
J Claudet, L Thiault : CNRS
L Durieux, F Sèyler : IRD
D Mouillot, L Velez : Montpellier U
O Hologne, R David : INRA
JAPAN:
Y Murayama, K Imai: NICT
Y Kondo, : RIHN
T Osawa: Tokyo Met U
AUSTRALIA:
A Specht: U of Queensland
L Wyborn: NCI
Associates:

12. PARSEC data strand
Leader: Shelley Stall,
Senior Director, Data Leadership, American Geophysical Union

13. • incentives for researchers…
• a sturdy credit and attribution
infrastructure that benefits
researchers…
• recommendations and best
practices that work for
researchers to encourage data
sharing and data reuse…
PARSEC data strand: objectives
Increase the number of properly cited data sets, provide credit and
attribution, and accurately track data reuse. For this we need:

14. • incentives for researchers…
• a sturdy credit and attribution
infrastructure that benefits
researchers…
• recommendations and best
practices that work for
researchers to encourage data
sharing and data reuse…
PARSEC data strand: objectives
Increase the number of properly cited data sets, provide credit and
attribution, and accurately track data reuse. For this we need:

15. • incentives for researchers…
• a sturdy credit and attribution
infrastructure that benefits
researchers…
• recommendations and best
practices that work for
researchers to encourage data
sharing and data reuse…
PARSEC data strand: objectives
Increase the number of properly cited data sets, provide credit and
attribution, and accurately track data reuse. For this we need:

16. A. Robustly connect identifiers across papers, people, and repositories. Currently, even if these
identifiers are included, the necessary linking to allow tracking is not fully implemented.
B. Conduct outreach and adoption campaigns on the importance of persistent identifiers and their
infrastructure to all relevant stakeholders—these include the data repositories, publishers, researchers,
and the key groups that set standards for publishers for reference tagging.
C. Promote and extend data usage metrics generated by RDA’s Data Usage Metrics Working Group
and data citations generated by RDA’s Scholix Working Group. By requiring that data be cited from a
trusted, community-accepted repository, the value of repositories can be better measured. Data sharing
through citation increases the likelihood of data discovery and reuse.
D. Provide guidance to our own science-synthesis team and the selected project teams from our
partners to optimize data reuse as well as data deposition of generated data for possible reuse.
Demonstrate that when researchers follow the FAIR Data Principles, data are better prepared for others to
understand, reuse increases, and discovery is improved.
E. Promote the work of integrated guidance that will address recommendations (generic and specific to
the ecological/biodiversity community) to improve each step of the process of data sharing, reuse, credit
and reward for researchers and repositories.
PARSEC data strand: work program

17. A. Robustly connect identifiers across papers, people, and repositories. Currently, even if these
identifiers are included, the necessary linking to allow tracking is not fully implemented.
B. Conduct outreach and adoption campaigns on the importance of persistent identifiers and their
infrastructure to all relevant stakeholders—these include the data repositories, publishers, researchers,
and the key groups that set standards for publishers for reference tagging.
C. Promote and extend data usage metrics generated by RDA’s Data Usage Metrics Working Group
and data citations generated by RDA’s Scholix Working Group. By requiring that data be cited from a
trusted, community-accepted repository, the value of repositories can be better measured. Data sharing
through citation increases the likelihood of data discovery and reuse.
D. Provide guidance to our own science-synthesis team and the selected project teams from our
partners to optimize data reuse as well as data deposition of generated data for possible reuse.
Demonstrate that when researchers follow the FAIR Data Principles, data are better prepared for others to
understand, reuse increases, and discovery is improved.
E. Promote the work of integrated guidance that will address recommendations (generic and specific to
the ecological/biodiversity community) to improve each step of the process of data sharing, reuse, credit
and reward for researchers and repositories.
PARSEC data strand: work program

18. A. Robustly connect identifiers across papers, people, and repositories. Currently, even if these
identifiers are included, the necessary linking to allow tracking is not fully implemented.
B. Conduct outreach and adoption campaigns on the importance of persistent identifiers and their
infrastructure to all relevant stakeholders—these include the data repositories, publishers, researchers,
and the key groups that set standards for publishers for reference tagging.
C. Promote and extend data usage metrics generated by RDA’s Data Usage Metrics Working Group
and data citations generated by RDA’s Scholix Working Group. By requiring that data be cited from a
trusted, community-accepted repository, the value of repositories can be better measured. Data sharing
through citation increases the likelihood of data discovery and reuse.
D. Provide guidance to our own science-synthesis team and the selected project teams from our
partners to optimize data reuse as well as data deposition of generated data for possible reuse.
Demonstrate that when researchers follow the FAIR Data Principles, data are better prepared for others to
understand, reuse increases, and discovery is improved.
E. Promote the work of integrated guidance that will address recommendations (generic and specific to
the ecological/biodiversity community) to improve each step of the process of data sharing, reuse, credit
and reward for researchers and repositories.
PARSEC data strand: work program

19. A. Robustly connect identifiers across papers, people, and repositories. Currently, even if these
identifiers are included, the necessary linking to allow tracking is not fully implemented.
B. Conduct outreach and adoption campaigns on the importance of persistent identifiers and their
infrastructure to all relevant stakeholders—these include the data repositories, publishers, researchers,
and the key groups that set standards for publishers for reference tagging.
C. Promote and extend data usage metrics generated by RDA’s Data Usage Metrics Working Group
and data citations generated by RDA’s Scholix Working Group. By requiring that data be cited from a
trusted, community-accepted repository, the value of repositories can be better measured. Data sharing
through citation increases the likelihood of data discovery and reuse.
D. Provide guidance to our own science-synthesis team and the selected project teams from our
partners to optimize data reuse as well as data deposition of generated data for possible reuse.
Demonstrate that when researchers follow the FAIR Data Principles, data are better prepared for others to
understand, reuse increases, and discovery is improved.
E. Promote the work of integrated guidance that will address recommendations (generic and specific to
the ecological/biodiversity community) to improve each step of the process of data sharing, reuse, credit
and reward for researchers and repositories.
PARSEC data strand: work program

20. A. Robustly connect identifiers across papers, people, and repositories. Currently, even if these
identifiers are included, the necessary linking to allow tracking is not fully implemented.
B. Conduct outreach and adoption campaigns on the importance of persistent identifiers and their
infrastructure to all relevant stakeholders—these include the data repositories, publishers, researchers,
and the key groups that set standards for publishers for reference tagging.
C. Promote and extend data usage metrics generated by RDA’s Data Usage Metrics Working Group
and data citations generated by RDA’s Scholix Working Group. By requiring that data be cited from a
trusted, community-accepted repository, the value of repositories can be better measured. Data sharing
through citation increases the likelihood of data discovery and reuse.
D. Provide guidance to our own science-synthesis team and the selected project teams from our
partners to optimize data reuse as well as data deposition of generated data for possible reuse.
Demonstrate that when researchers follow the FAIR Data Principles, data are better prepared for others to
understand, reuse increases, and discovery is improved.
E. Promote the work of integrated guidance that will address recommendations (generic and specific to
the ecological/biodiversity community) to improve each step of the process of data sharing, reuse, credit
and reward for researchers and repositories.
PARSEC data strand: work program

21. PARSEC data strand: credit and reward landscape

22. PARSEC data strand: partnership projects
Slide Credit: Martin Fenner,
DataCite, PARSEC meeting at
RDA P14, 21 Oct 2019

23. PARSEC data strand – synthesis strand linkage

24. PARSEC data strand – synthesis strand linkage

25. PARSEC data strand – synthesis strand linkage

26. PARSEC data strand – synthesis strand linkage

27. PARSEC synthesis strand
Leader: David Mouillot,
University of Montpellier, France

28. PARSEC synthesis strand: work program
• WP1: Stratified sampling of 200 rural
communities close to and far from
natural protected areas (PAs) using
matching algorithms.
• WP2: Estimate socioeconomic
conditions in the selected rural
communities using remote sensing and
artificial intelligence.
• WP3: Using paired comparison tests
determine whether proximity to a PA
can improve socioeconomic outcomes.
Identify contributing factors.

29. PARSEC WP1: stratified sampling
Step 1: Identification of suitable data for the project
Selection of socio-economic systems close to a Protected
Area (PA)
• PA: IUCN category 1-5; >10km2; creation date 2000-
2015
• Town/village: <5000 inhabitants < 20km from PA,
>100km from large city
Step 2: Acquiring Data – for example
• PA: Dipperu NP, IUCN category 1a, 112.05 km2,
created 2014
• Town: Nebo, 840 inhabitants, circa 20km from PA,
>100km from Mackay

30. PARSEC WP1: stratified sampling
Step 1: Identification of suitable data for the project
Selection of socio-economic systems close to a Protected
Area (PA)
• PA: IUCN category 1-5; >10km2; creation date 2000-
2015
• Town/village: <5000 inhabitants < 20km from PA,
>100km from large city
Step 2: Acquiring Data – for example
• PA: Dipperu NP, IUCN category 1a, 112.05 km2,
created 2014
• Town: Nebo, 840 inhabitants, circa 20km from PA,
>100km from Mackay

31. PARSEC WP1: stratified sampling
Further criteria for selection
(a) Socio-economic status of the town so information can be derived
about (for example):
• Gross Domestic Product (GDP)
• Human Development Index (HDI)
• Child Growth Failure (CGF)
• Consumption Expenditure (CE)
• Asset Health (AH)
Sources: UNESCO MICS surveys, World Bank studies, information
that allows comparison across the countries, and beyond. Local surveys
OK, but more difficult to use for comparison.
(b) Image analysis across time: before and after the creation of the
PA. Are the images available? In what format (Landsat, SPOT,
QuickBird etc)? Assessment of socio-economic status using AI
(c) What is the possibility for mirror sites?

32. PARSEC WP1: stratified sampling
Further criteria for selection
(a) Socio-economic status of the town so information can be derived
about (for example):
• Gross Domestic Product (GDP)
• Human Development Index (HDI)
• Child Growth Failure (CGF)
• Consumption Expenditure (CE)
• Asset Health (AH)
Sources: UNESCO MICS surveys, World Bank studies, information
that allows comparison across the countries, and beyond. Local surveys
OK, but more difficult to use for comparison.
(b) Image analysis across time: before and after the creation of the
PA. Are the images available? In what format (Landsat, SPOT,
QuickBird etc)? Assessment of socio-economic status using AI
(c) What is the possibility for mirror sites?

33. PARSEC WP1: stratified sampling
Further criteria for selection
(a) Socio-economic status of the town so information can be derived
about (for example):
• Gross Domestic Product (GDP)
• Human Development Index (HDI)
• Child Growth Failure (CGF)
• Consumption Expenditure (CE)
• Asset Health (AH)
Sources: UNESCO MICS surveys, World Bank studies, information
that allows comparison across the countries, and beyond. Local surveys
OK, but more difficult to use for comparison.
(b) Image analysis across time: before and after the creation of the
PA. Are the images available? In what format (Landsat, SPOT,
QuickBird etc)? Assessment of socio-economic status using AI
(c) What is the possibility for mirror sites?

34. The machine learning component
PARSEC WP2: estimation of socio-economic
conditions using remote sensing and artificial
intelligence

35. PARSEC WP2: machine learning protocol
• STEP 1. We « show » the « network » examples and counter-
examples
• STEP 2. We use the network 
Deep learning
fish
other
fish
other
THELEARNING

36. PARSEC WP2: decisions via a CNN*
•
convolutions
Average or max [+ sub-sampling]
Non linear function
(= activation function)
normalisation
*Convolutional Neural Network

37. PARSEC WP2: prediction of poverty with a CNN?
Predicted poverty in Uganda
Image from Xie et al. (2016)
Using 400x400 pixel images
(1 km x 1km) the CNN should
predict a poverty value (scalar)
(0 = Low ; 100 = high)
Google Static Maps API,
for the image 400 × 400 pixels
at zoom level 16
(poverty  annual consumption level of households)
Predicted poverty probabilities at a fine-grained
10 x 10km block level

38. PARSEC WP2: Parts of the image that “react”
Original daytime
satellite images from
Google Static Maps
filter activation maps
Overlay of activation
maps onto original
images
urban areas nonurban areas water roads
Jean et al. 2016

39. PARSEC WP2: but is this already done?
From Figure 3, Xie et al. (2016)
* World Resources Institute, 2009

40. PARSEC WP2: but is this already done?
From Figure 3, Xie et al. (2016)
* World Resources Institute, 2009

41. PARSEC WP2: but is this already done?
From Figure 3, Xie et al. (2016)
* World Resources Institute, 2009

42. PARSEC WP2: but is this already done?
From Figure 3, Xie et al. (2016)
Only 70% correlated to the ground truth evidence
* World Resources Institute, 2009

43. PARSEC WP2: how to improve the prediction?
• More images with more ground truthing
• Work on methods using a small number of images.
• Work with temporal sequences
• Add diversity in the learning database:
• Really poor to very rich
• Various places in the world
(should we create only one unique CNN or …)
• Challenges
• poverty data accessibility (within time-period, frequency, type)
• Integration of multi-resolution, multi-source, sparsity, time
integration, incomplete data, etc ..

44. And there is more!

45. WP3: Using paired comparison tests determine
whether proximity to a PA can improve
socioeconomic outcomes. Identify contributing
factors.

46. But that’s for 2022
Thankyou!

47. PARSEC: acknowledgements

48. IPBES (2019) Summary for policymakers of the global assessment report on
biodiversity and ecosystem services of the Intergovernmental Science-Policy
Platform on Biodiversity and Ecosystem Services. Eds S. Díaz, J. Settele, E. S.
Brondizio E.S., H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A.
Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M.
Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, A. Pfaff, S.
Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, I. J.
Visseren-Hamakers, K. J. Willis, and C. N. Zayas. IPBES secretariat, Bonn,
Germany. 39 pages.
Jean M., Burke M., Xie M., Davis W. M., Lobell D. B. , Ermon S. (2016) Combining
satellite imagery and machine learning to predict poverty. Science 353(6301):
790-794.
World Resources Institute (2009) Mapping a better future: how spatial analysis can
benefit wetlands and reduce poverty in Uganda. 39p. Washington, D.C. (USA): WRI.
Xie M., Jean N., Burke M., Lobell D., Ermon S. (2016) Transfer learning from deep
features for remote sensing and poverty mapping, pp 3929-3935. Proc. 13th
AAAI Conference (AAAI-16).
PARSEC: cited references