Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning

Ghorbanzadeh, Omid1,2; Crivellari, Alessandro3; Tiede, Dirk1; Ghamisi, Pedram2,4; Lang, Stefan1

2022-12-01

10.3390/rs14246382

REMOTE SENSING   Impact Factor & Quartile

2072-4292

The improvement in computer vision, sensor quality, and remote sensing data availability makes satellite imagery increasingly useful for studying human settlements. Several challenges remain to be overcome for some types of settlements, particularly for internally displaced populations (IDPs) and refugee camps. Refugee-dwelling footprints and detailed information derived from satellite imagery are critical for a variety of applications, including humanitarian aid during disasters or conflicts. Nevertheless, extracting dwellings remains difficult due to their differing sizes, shapes, and location variations. In this study, we use U-Net and residual U-Net to deal with dwelling classification in a refugee camp in northern Cameroon, Africa. Specifically, two semantic segmentation networks are adapted and applied. A limited number of randomly divided sample patches is used to train and test the networks based on a single image of the WorldView-3 satellite. Our accuracy assessment was conducted using four different dwelling categories for classification purposes, using metrics such as Precision, Recall, F1, and Kappa coefficient. As a result, F1 ranges from 81% to over 99% and approximately 88.1% to 99.5% based on the U-Net and the residual U-Net, respectively.

remote sensing refugees humanitarian operations Africa

SCI

English

Others

Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology

Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:000903238200001

MDPI

Web of Science

1.Univ Salzburg, Dept Geoinformat Z GIS, Christian Doppler Lab Geospatial & EO based Humani, A-5020 Salzburg, Austria
2.Inst Adv Res Artificial Intelligence IARAI, Landstr Hauptstr 5, A-1030 Vienna, Austria
3.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
4.Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, Machine Learning Grp, Chemnitzer Str 40, D-09599 Freiberg, Germany

Ghorbanzadeh, Omid,Crivellari, Alessandro,Tiede, Dirk,et al. Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning[J]. REMOTE SENSING,2022,14(24).

Ghorbanzadeh, Omid,Crivellari, Alessandro,Tiede, Dirk,Ghamisi, Pedram,&Lang, Stefan.(2022).Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning.REMOTE SENSING,14(24).

Ghorbanzadeh, Omid,et al."Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning".REMOTE SENSING 14.24(2022).