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Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements
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| 4.65 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
The identification and monitoring of informal settlements in urban areas is an important
step in developing and implementing pro-poor urban policies. Understanding when,
where and who lives inside informal settlements is critical to efforts to improve their
resilience. This study aims at integrating OSM data and sentinel-2 imagery for
classifying and monitoring the growth of informal settlements methods to map informal
areas in Kampala (Uganda) and Dar es Salaam (Tanzania) and to monitor their growth
in Kampala. Three building feature characteristics of size, shape and Distance to nearest
Neighbour were derived and used to cluster and classify informal areas using Hotspot
Cluster analysis and ML approach on OSM buildings data. The resultant informal
regions in Kampala were used with Sentinel-2 image tiles to investigate the spatiotemporal
changes in informal areas using Convolutional Neural Networks (CNNs).
Results from Optimized Hot Spot Analysis and Random Forest Classification show that
Informal regions can be mapped based on building outline characteristics. An accuracy
of 90.3% was achieved when an optimally trained CNN was executed on a test set of
2019 satellite image tiles. Predictions of informality from new datasets for the years
2016 and 2017 provided promising results on combining different open source
geospatial datasets to identify, classify and monitor informal settlements.
Descrição
Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
Palavras-chave
Informal Settlements Remote Sensing Urbanization Machine Learning (ML) Random Forest (RF) Convolutional Neural Networks (CNN) Sentinel-2 satellite imagery