Artificial Neural Network and Machine Learning Based Methods for Population Estimation of Rohingya Refugees: Comparing Data-Driven and Satellite Image-Driven Approaches

Abstract

Manual¯eld-based population census data collection method is slow and expensive, especially for refugee management situations where more frequent censuses are necessary. This study aims to explore the approaches of population estimation of Rohingya migrants using remote sensing and machine learning. Two di®erent approaches of population estimation viz., (i) data-driven approach and (ii) satellite image-driven approach have been explored. A total of 11 machine learning models including Arti¯cial Neural Network (ANN) are applied for both approaches. It is found that, in situations where the surface population distribution is unknown, a smaller satellite image grid cell length is required. For data-driven approach, ANN model is placed fourth, Linear Regression model performed the worst and Gradient Boosting model performed the best. For satellite image-driven approach, ANN model performed the best while Ada Boost model has the worst performance. Gradient Boosting model can be considered as a suitable model to be applied for both the approaches.

Mapping of Dwellings in IDP/Refugee Settlements from Very High-Resolution Satellite Imagery Using a Mask Region-Based Convolutional Neural Network

“…Remote sensing has been applied to monitor the various attributes of IDP/refugee camps, including the infrastructure evolution [7], the environment [8][9][10][11], refugee camp expansion [9,[12][13][14][15], and natural hazard vulnerability analysis [16], towards an estimation of the residing populations within the camps [17][18][19], using various interpretation, prediction, classification, and modeling approaches. There are previous works that are specific to dwelling detection in IDP/refugee camp [4,[20][21][22][23][24].…”

Uneven Frontiers: Exposing the Geopolitics of Myanmar’s Borderlands with Critical Remote Sensing

“…In this vein, remote sensing has detected processes spilling over Myanmar's borders, from atrocities committed by the Myanmar military to resource extraction. Satellite imagery has been used to capture the burning of Rohingya villages [54][55][56] and estimate the population of refugee camps in Bangladesh [57] and their ecological impacts [58,59], with displacement and deforestation positively correlated [60]. Researchers have also tracked natural resource extraction within the country's borderlands, from mining in Shan State bordering China [61] to oil palm and rubber plantations in the southeast [62,63], and along the Chinese and Laotian borders [64].…”