Publicação
Urban Sprawl Analysis in Kutupalong Refugee Camp, Bangladesh
| Resumo: | Urban sprawl is a common phenomenon associated with geographical and political challenges such as refugee settlements and environmental extremes. Urban sprawl related to refugee or habitation settlement has been an area of active interest because of humanitarian and environmental problems. For example, higher rates of urban sprawling are positively correlated with higher rates of deforestation. The present study explored the viability and reproducibility of different classification techniques in assessing urban sprawl among Rohingya refugees in the Kutupalong refugee camp in Bangladesh. These classification methods include the Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC). The urban sprawl was measured based on the classification of urban and non-urban classes. The SVM yielded better overall accuracy performance compared to MLC classification. The study showed that urban class exhibited exponential growth from 2.01 km2 to 5.37 km2 within nine months. On the con trary, the non-urban class shrunk from 12.58 km2 to 9.95 km2 during the same period due to a high influx of refugees and rapid camp expansion. |
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| Autores principais: | Loncar, Filip |
| Outros Autores: | Cabral, Pedro |
| Assunto: | Urban Sprawl Refugee Camp Unmanned Aerial Vehicle Support Vector Machine Maximum Likelihood Classification Computer Graphics and Computer-Aided Design Computer Networks and Communications Computer Science Applications Computer Vision and Pattern Recognition Information Systems Software SDG 16 - Peace, Justice and Strong Institutions |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Urban sprawl is a common phenomenon associated with geographical and political challenges such as refugee settlements and environmental extremes. Urban sprawl related to refugee or habitation settlement has been an area of active interest because of humanitarian and environmental problems. For example, higher rates of urban sprawling are positively correlated with higher rates of deforestation. The present study explored the viability and reproducibility of different classification techniques in assessing urban sprawl among Rohingya refugees in the Kutupalong refugee camp in Bangladesh. These classification methods include the Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC). The urban sprawl was measured based on the classification of urban and non-urban classes. The SVM yielded better overall accuracy performance compared to MLC classification. The study showed that urban class exhibited exponential growth from 2.01 km2 to 5.37 km2 within nine months. On the con trary, the non-urban class shrunk from 12.58 km2 to 9.95 km2 during the same period due to a high influx of refugees and rapid camp expansion. |
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