Unsupervised semantic change detection in informal settlements using UAV imagery

Research output: Contribution to conferenceAbstractOther research output

Abstract

The detailed spatial data obtained from Unmanned Aerial Vehicles can shed light on subtle changes in informal settlements which may be linked to important socio-economic developments in the area. For example: the creation of new roads, building extensions, and incremental improvements in roof quality. Typical challenges for change detection methods include the detection of irrelevant changes through unsupervised methods, and training data requirements for supervised change detection methods. We therefore illustrate a workflow which combines rule-based, deep learning approach, and unsupervised change detection algorithms to develop an automatic workflow which identifies changes in informal settlements and assigns semantic meaning to them Figure 1: RGB imagery of an informal settlement in Rwanda (left column), rule-based class predictions (middle column) and predicted class label (right column) for UAV data from 2015 (top row) and 2017 (bottom row). Red indicates buildings, beige indicates terrain, green indicates trees, and white indicates low vegetation.
Original languageEnglish
Pages1
Number of pages1
Publication statusPublished - 29 Nov 2018
EventNCG symposium 2018 - Wageningen university, Wageningen, Netherlands
Duration: 29 Nov 201829 Nov 2018
https://ncgeo.nl/index.php/nl/actueel/nieuws/item/2781-programma-ncg-symposium-2018

Conference

ConferenceNCG symposium 2018
CountryNetherlands
CityWageningen
Period29/11/1829/11/18
Internet address

Fingerprint

informal settlement
imagery
detection method
spatial data
roof
economic development
learning
road
vegetation
prediction
detection

Cite this

@conference{a1792815fcd94a408528513a865e8c08,
title = "Unsupervised semantic change detection in informal settlements using UAV imagery",
abstract = "The detailed spatial data obtained from Unmanned Aerial Vehicles can shed light on subtle changes in informal settlements which may be linked to important socio-economic developments in the area. For example: the creation of new roads, building extensions, and incremental improvements in roof quality. Typical challenges for change detection methods include the detection of irrelevant changes through unsupervised methods, and training data requirements for supervised change detection methods. We therefore illustrate a workflow which combines rule-based, deep learning approach, and unsupervised change detection algorithms to develop an automatic workflow which identifies changes in informal settlements and assigns semantic meaning to them Figure 1: RGB imagery of an informal settlement in Rwanda (left column), rule-based class predictions (middle column) and predicted class label (right column) for UAV data from 2015 (top row) and 2017 (bottom row). Red indicates buildings, beige indicates terrain, green indicates trees, and white indicates low vegetation.",
author = "C.M. Gevaert and C. Persello and R.V. Sliuzas and G. Vosselman",
year = "2018",
month = "11",
day = "29",
language = "English",
pages = "1",
note = "NCG symposium 2018 ; Conference date: 29-11-2018 Through 29-11-2018",
url = "https://ncgeo.nl/index.php/nl/actueel/nieuws/item/2781-programma-ncg-symposium-2018",

}

Unsupervised semantic change detection in informal settlements using UAV imagery. / Gevaert, C.M.; Persello, C.; Sliuzas, R.V.; Vosselman, G.

2018. 1 Abstract from NCG symposium 2018 , Wageningen, Netherlands.

Research output: Contribution to conferenceAbstractOther research output

TY - CONF

T1 - Unsupervised semantic change detection in informal settlements using UAV imagery

AU - Gevaert, C.M.

AU - Persello, C.

AU - Sliuzas, R.V.

AU - Vosselman, G.

PY - 2018/11/29

Y1 - 2018/11/29

N2 - The detailed spatial data obtained from Unmanned Aerial Vehicles can shed light on subtle changes in informal settlements which may be linked to important socio-economic developments in the area. For example: the creation of new roads, building extensions, and incremental improvements in roof quality. Typical challenges for change detection methods include the detection of irrelevant changes through unsupervised methods, and training data requirements for supervised change detection methods. We therefore illustrate a workflow which combines rule-based, deep learning approach, and unsupervised change detection algorithms to develop an automatic workflow which identifies changes in informal settlements and assigns semantic meaning to them Figure 1: RGB imagery of an informal settlement in Rwanda (left column), rule-based class predictions (middle column) and predicted class label (right column) for UAV data from 2015 (top row) and 2017 (bottom row). Red indicates buildings, beige indicates terrain, green indicates trees, and white indicates low vegetation.

AB - The detailed spatial data obtained from Unmanned Aerial Vehicles can shed light on subtle changes in informal settlements which may be linked to important socio-economic developments in the area. For example: the creation of new roads, building extensions, and incremental improvements in roof quality. Typical challenges for change detection methods include the detection of irrelevant changes through unsupervised methods, and training data requirements for supervised change detection methods. We therefore illustrate a workflow which combines rule-based, deep learning approach, and unsupervised change detection algorithms to develop an automatic workflow which identifies changes in informal settlements and assigns semantic meaning to them Figure 1: RGB imagery of an informal settlement in Rwanda (left column), rule-based class predictions (middle column) and predicted class label (right column) for UAV data from 2015 (top row) and 2017 (bottom row). Red indicates buildings, beige indicates terrain, green indicates trees, and white indicates low vegetation.

UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2018/pres/gevaert_uns_abs.pdf

M3 - Abstract

SP - 1

ER -