The Rohingya Muslims of Myanmar are considered one of the world's most persecuted groups and their plight has created the fastest growing refugee crisis. The Myanmar government and co-opted Buddhist villagers have destroyed homes and killed thousands. As a result of this escalation, over 870,000 people have been displaced from their homeland, with a majority taking refuge along the coastal border in temporary overcrowded camps. To make the region’s issues worse, the next crisis is looming: the rain and tropical storm season.

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Sneak Peek

Targeted 'Cleansing' in Myanmar

On August 25, 2017, Rohingya militants killed 12 Myanmar security force personnel in a series of attacks. Immediately following these militant attacks, Myanmar's military began large-scale clearing operations across Rakhine State. The crackdown against the Rohingya quickly reached new levels of brutality. The following maps demonstrate how remote sensing and imagery-based crowdsourcing can be used to rapidly identify destroyed Rohingya villages and queue more advanced imagery analysis, change monitoring, and geospatial modeling.

Cyclone Season Poses Great Risk to Refugees

Another crisis looms for the Rohingya refugees this summer: the potential for devastating floods, landslides and disease outbreaks that the annual rain and tropical cyclone season could yield. The coastal Bangladesh- Myanmar border region has been directly impacted by a cyclone in each of the past three years. Following Cyclone Mora in May 2017, the United Nations Institute for Training and Research (UNITAR) identified 1,880 possible damaged shelters among a refugee population of approximately 53,000 in the Kutupalong, Leda, and Nayapara Camps. Today, however, these three camps collectively hold nearly 700,000 refugees. A storm of similar strength and trajectory to Mora will undoubtedly result in far more damage. The map below shows the location of the Rohingya refugee settlements in Bangladesh and their proximity to observed flooding over the past 33 years (as detected by Landsat imagery).

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