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SUMMARY:Earth Observation Network November Seminar: Metzler\, Barbara\, ‘
 Characterising urban environments in Sub-Saharan Africa with satellite ima
 gery and unsupervised deep learning’
DESCRIPTION:Title: Characterising urban environments in Sub-Saharan Africa 
 with satellite imagery and unsupervised deep learning\nAbstract: Sub-Saha
 ran Africa and other developing regions have urbanized extensively\, leadi
 ng to complex urban features with varying presence and types of roads\, bu
 ildings and vegetation. We use a novel hierarchical deep learning framewor
 k to characterize multidimensional urban environments in multiple cities. 
 Application of the model to Accra\, Dakar\, and Dar es Salaam revealed ana
 logous patterns of building density\, roads and vegetation\, such as dense
  settlements within the metropolitan boundary and a peri-urban intermix of
  natural and built environment\, which we attribute to shared colonial leg
 acies and recent growth trajectories. Kigali\, with its mountainous geogra
 phy and post-colonial expansion\, exhibited unique urban characteristics i
 ncluding a sparser urban core and significant wildland-urban intermix. Our
  results demonstrate that unlabeled satellite images with unsupervised dee
 p learning enables near real-time urban monitoring\, particularly in regio
 ns where traditional data are scarce.\nBio: Dr A Barbara Metzler is a Rese
 arch Associate with the Science of Cities and Regions group at the Alan Tu
 ring Institute. As part of her research\, she is leveraging deep learning 
 models and satellite imagery to study urban areas. During her PhD from Imp
 erial College London\, she used unsupervised/self-supervised deep learning
  techniques together with high-resolutions satellite images to classify ur
 ban areas in Sub-Saharan Africa for applications in Public Health.\n 
URL:https://www.imperial.ac.uk/events/185958/earth-observation-network-nove
 mber-seminar-metzler-barbara-characterising-urban-environments-in-sub-saha
 ran-africa-with-satellite-imagery-and-unsupervised-deep-learning/
DTSTART;TZID=Europe/London:20241127T160000
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LOCATION:United Kingdom
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