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Informal settlement segmentation using VHR RGB and height information from UAV imagery: a case study of Nepal
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Resumo(s)
Informal settlement in developing countries are complex. They are contextually
and radiometrically very similar to formal settlement. Resolution
offered by Remote sensing is not sufficient to capture high variations and feature
size in informal settlements in these situations. UAV imageries offers
solution with higher resolution. Incorporating UAV image and normalized
DSM obtained from UAV provides an opportunity of including information
on 3D space. This can be a crucial factor for informal settlement extraction
in countries like Nepal. While formal and informal settlements have similar
texture, they differ significantly in height. In this regard, we propose segmentation
of informal settlement of Nepal using UAV and normalized DSM, against
traditional approach of orthophoto only or orthophoto and DSM. Absolute
height, normalized DSM(nDSM) and vegetation index from visual band added
to 8 bit RGB channels are used to locate informal settlements. Segmentation
including nDSM resulted in 6 % increment in Intersection over Union for informal
settlements. IoU of 85% for informal settlement is obtained using nDSM
trained end to end on Resnet18 based Unet. Use of threshold value had same
effect as using absolute height, meaning use of threshold does not alter result
from using absolute nDSM. Integration of height as additional band showed
better performance over model that trained height separately. Interestingly,
benefits of vegetation index is limited to settlements with small huts partly
covered with vegetation, which has no or negative effect elsewhere.
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Palavras-chave
Informal Settlement Unmanned Aerial Vehicle (UAV) Deep Learning Semantic Segmentation Normalized Digital Surface Model(nDSM) Visible-Band Difference Vegetation Index (VDVI)