Senior Fellow | Nord University
Conflict and Violence
South Asia

Using Satellite Data and Machine Learning to Study Conflict-Induced Environmental and Socioeconomic Destruction in Data-Poor Conflict Areas: The Case of the Rakhine Conflict

Climate ActionPeace, Justice and Strong Institutions

The idea behind this article was to apply new, digital technologies in order to study some of the least accessible places in the world, namely neighbouring regions of Bangladesh and Myanmar. We studied changes over time in environmental, social and economic domains, and explored the causes of these changes (2012-2019).

This summary, including its recommendations and ideas, was created by Dr Roman Vakulchuk and is based on original research. The original research itself was conducted in collaboration with the following researchers.

Original research
Journal article: Using Satellite Data and Machine Learning to Study Conflict-Induced Environmental and Socioeconomic Destruction in Data-Poor Conflict Areas: The Case of the Rakhine Conflict (2021)
Peer Reviewed
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Authors:

Dr Roman Vakulchuk, Thiri Shwesin Aung, Indra Overland and Yanhua Xie

About:

This research used a quantitative approach, combining conflict studies, data imagery and machine learning.

We analysed Google Data Storage imagery (high resolution satellite imagery of bordering areas between Myanmar and Bangladesh where Rakhine people live) with machine learning algorithms in order to assess the change over time (from 2012 to 2019) in terms of the environmental, economic and social state of the areas. We used a variety of indicators, from the quantity of trees to the state of roofs on houses.

However, it should be remembered that our research denotes very general dynamics, and we cannot explain specific developments like, for instance, why 5% of hospitals in the areas have different roofs. We can make general conclusions about large-scale developments, but our analysis cannot give us answers for more specific findings.

Another limitations is the fact that our research could be difficult to replicate or do on other areas, because obtaining the satellite imagery we used was expensive. Smaller-scale researchers will not have the access to this equipment.

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Funding:

This research was independently conducted and did not receive funding from outside of the university.

  • For Academia & Research
  • Bangladesh
  • Myanmar
  • conflict
  • deforestation
  • fragility
  • satellite imagery
  • violence
  • Summary made: 2022

Key points

  • The main takeaway is that the technologies we’ve used (data imagery and machine learning) really allow us to view the situation from many different angles, and allow us to have a clear picture of the overall dynamics.

The purpose of this research was to find new ways to do research in areas that are difficult to reach, and therefore research with traditional methods such as interviews, survey, field work.

In our case, we looked at regions where the Rakhine people are located. The Rakhine people are an ethnic group with whom the central government in Myanmar has had a series of conflicts and tensions, and what little we knew based on some individual accounts of people who had been there and some limited journalistic accounts. This was not enough to make reliable conclusions about what has been happening in the area, and we wanted to explore the change over time in environmental, social and economic domains.

Findings

  • While there are many indicators used to estimate the social, environmental and economic situations of areas, we need new indicators to analyse satellite imagery.
  • The 2017 conflict has led to impoverishment, deforestation and an overall worsening of the situation in those regions.

    Our indicators showed that the range of household income is lower, and deforestation has strongly increased.

  • From 2012 to 2019, the size of forests in the bordering regions on the side of Myanmar and Bangladesh decreased by 20% and 13% respectively.

What it means

This research has yielded a couple of findings:

First, we found that the methodology we used still needed to be developed, especially in terms of the kind of indicators that can be used to draw conclusions. Many indicators can be used to estimate the social, environmental and economic situations of the areas, and it is important to know which to use where (for instance, one can estimate the economic state of an area by looking at the state of roofs on houses). It is important that future research explores in more detail these indicators and finds new ones.

Second, we found that the economic, social and environmental situation has largely worsened. Our indicators showed that the range of household income is lower, and deforestation has strongly increased. What we concluded was that the main driver of these tendencies was the 2017 conflict, which has led to impoverishment, deforestation and an overall worsening of the situation in those regions.

How to use

  • The international aid community that works on Myanmar can use the evidence we've found in negotiations, in designing development aid projects, in gaining a better understanding of the situation on the ground
  • Our study encourages researchers to expand their methodological tools and to try to do research with a larger variety of methods, with more innovative tools, in order to gain a more nuanced understanding of complex situations

Acknowledgements

Thank you to ASEAN

These insights were made available thanks to the support of ASEAN, who are committed to the dissemination of knowledge for all.

 

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Vakulchuk, Roman. 'Using Satellite Data and Machine Learning to Study Conflict-Induced Environmental and Socioeconomic Destruction in Data-Poor Conflict Areas: The Case of the Rakhine Conflict'. Acume. https://www.acume.org/r/using-satellite-data-and-machine-learning-to-study-conflict-induced-environmental-and-socioeconomic-destruction-in-data-poor-conflict-areas-the-case-of-the-rakhine-conflict/