Monitoring household upgrading in unplanned settlements with unmanned aerial vehicles
In-situ slum upgrading projects include infrastructural improvements such as new roads, which are perceived to improve the quality of life for the residents and encourage structural improvements at a household level. Although these physical changes are easily visible in satellite imagery, it is more...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243419309900 |
| _version_ | 1811228417733951488 |
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| author | Caroline M. Gevaert Claudio Persello Richard Sliuzas George Vosselman |
| author_facet | Caroline M. Gevaert Claudio Persello Richard Sliuzas George Vosselman |
| author_sort | Caroline M. Gevaert |
| collection | DOAJ |
| description | In-situ slum upgrading projects include infrastructural improvements such as new roads, which are perceived to improve the quality of life for the residents and encourage structural improvements at a household level. Although these physical changes are easily visible in satellite imagery, it is more difficult to track incremental improvements undertaken by the residents – which are perhaps more closely linked to the socio-economic development of the households themselves. The improved detail provided by imagery obtained from Unmanned Aerial Vehicles (UAVs) has the potential to monitor these more subtle changes in a settlement. This paper provides a framework which takes advantage of high-resolution imagery and a detailed elevation model from UAVs to detect changes in informal settlements. The proposed framework leverages expert knowledge to provide training labels for deep learning and thus avoids the cost of manual labelling. The semantic classification is then used to interpret a change mask and identify: new buildings, the creation of open spaces, and incremental roof upgrading in an informal settlement. The methodology is demonstrated on UAV imagery of an informal settlement in Kigali, Rwanda, successfully identifying changes between 2015 and 2017 with an Overall Accuracy of 95 % and correctly interpreting changes with an Overall Accuracy of 91 %. Results reveal that almost half the buildings in the settlement show visible changes in the roofing material, and 61 % of these changed less than 1m². This demonstrates the incremental nature of housing improvements in the settlement. |
| first_indexed | 2024-04-12T09:57:40Z |
| format | Article |
| id | doaj.art-83e93da974144015a3adb495ddfa43e2 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-12T09:57:40Z |
| publishDate | 2020-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-83e93da974144015a3adb495ddfa43e22022-12-22T03:37:38ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322020-08-0190102117Monitoring household upgrading in unplanned settlements with unmanned aerial vehiclesCaroline M. Gevaert0Claudio Persello1Richard Sliuzas2George Vosselman3Corresponding author.; Faculty ITC, University of Twente, 7500 AE Enschede, The NetherlandsFaculty ITC, University of Twente, 7500 AE Enschede, The NetherlandsFaculty ITC, University of Twente, 7500 AE Enschede, The NetherlandsFaculty ITC, University of Twente, 7500 AE Enschede, The NetherlandsIn-situ slum upgrading projects include infrastructural improvements such as new roads, which are perceived to improve the quality of life for the residents and encourage structural improvements at a household level. Although these physical changes are easily visible in satellite imagery, it is more difficult to track incremental improvements undertaken by the residents – which are perhaps more closely linked to the socio-economic development of the households themselves. The improved detail provided by imagery obtained from Unmanned Aerial Vehicles (UAVs) has the potential to monitor these more subtle changes in a settlement. This paper provides a framework which takes advantage of high-resolution imagery and a detailed elevation model from UAVs to detect changes in informal settlements. The proposed framework leverages expert knowledge to provide training labels for deep learning and thus avoids the cost of manual labelling. The semantic classification is then used to interpret a change mask and identify: new buildings, the creation of open spaces, and incremental roof upgrading in an informal settlement. The methodology is demonstrated on UAV imagery of an informal settlement in Kigali, Rwanda, successfully identifying changes between 2015 and 2017 with an Overall Accuracy of 95 % and correctly interpreting changes with an Overall Accuracy of 91 %. Results reveal that almost half the buildings in the settlement show visible changes in the roofing material, and 61 % of these changed less than 1m². This demonstrates the incremental nature of housing improvements in the settlement.http://www.sciencedirect.com/science/article/pii/S0303243419309900Change detection algorithmsImage analysisUnmanned aerial vehiclesUrban planningInformal settlements |
| spellingShingle | Caroline M. Gevaert Claudio Persello Richard Sliuzas George Vosselman Monitoring household upgrading in unplanned settlements with unmanned aerial vehicles International Journal of Applied Earth Observations and Geoinformation Change detection algorithms Image analysis Unmanned aerial vehicles Urban planning Informal settlements |
| title | Monitoring household upgrading in unplanned settlements with unmanned aerial vehicles |
| title_full | Monitoring household upgrading in unplanned settlements with unmanned aerial vehicles |
| title_fullStr | Monitoring household upgrading in unplanned settlements with unmanned aerial vehicles |
| title_full_unstemmed | Monitoring household upgrading in unplanned settlements with unmanned aerial vehicles |
| title_short | Monitoring household upgrading in unplanned settlements with unmanned aerial vehicles |
| title_sort | monitoring household upgrading in unplanned settlements with unmanned aerial vehicles |
| topic | Change detection algorithms Image analysis Unmanned aerial vehicles Urban planning Informal settlements |
| url | http://www.sciencedirect.com/science/article/pii/S0303243419309900 |
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