Testing Transferability of Deep-Learning-Based Dwelling Extraction in Refugee Camps

Abstract

For effective humanitarian response in refugee camps, reliable information concerning dwelling type, extent, surrounding infrastructure, and respective population size is essential. As refugee camps are inherently dynamic in nature, continuous updating and frequent monitoring is time and resource-demanding, so that automatic information extraction strategies are very useful. In this ongoing research, we used labelled data and highresolution Worldview imagery and first trained a Convolutional Neural Network-based U-net model architecture. We first trained and tested the model from scratch for Al Hol camp in Syria. We then tested the transferability of the model by testing its performance in an image of a refugee camp situated in Cameroon. We were using patch size 32, at the Syrian test site, a Mean Area Intersection Over Union (MIoU) of 0.78 and F-1 score of 0.96, while in the transfer site, MIoU of 0.69 and an F-1 score of 0.98 were achieved. Furthermore, the effect of patch size and the combination of samples from test and transfer sites are investigated.

Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning

“…The study site in this paper is the refugee camp Minawao in the far northern region of Cameroon, at 10°33 30 N 13°51 30 E (see Figure 1), managed by the UNHCR and mainly housing refugees from the neighboring Nigerian Borno State, where activities of the Boko Haram militia forced over 240,000 people to flee their homes [25]. The camp has existed since 2012 and shows a quite high dynamic in population number, increasing rapidly since early 2014.…”

“…Nevertheless, the main reason for the lack of large-scale data sets for refugee camps has to do with the security concerns associated with this field. [21,24,25]. The specific properties of dwellings such as varying sizes and a range of different building materials (e.g., wood and plastic tarp) are extra challenges for dwelling classification tasks within the remote sensing community [3].…”