Self-supervised Variational Autoencoder for Unsupervised Object Counting from Very High-Resolution Satellite Imagery: Applications in Dwelling Extraction in FDP Settlement Areas

In supervised learning, deep learning models demand a large corpus of annotated data for object detection and classification tasks. This constrains their utility in humanitarian emergency response. To overcome this problem, we have proposed an unsupervised dwelling counting from very high-resolution...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 62; p. 1
Main Authors:	Gella, Getachew Workineh, Gangloff, Hugo, Wendt, Lorenz, Tiede, Dirk, Lang, Stefan
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Annotations Anomalies Anomaly Anomaly detection cutPaste Datasets Deep learning Detection Displaced persons Dwelling counting Dwellings Emergency preparedness Emergency response Engineering Sciences Geography High resolution Image reconstruction Image resolution latent space conditioning Localization Location awareness Machine learning Object recognition Satellite imagery Satellite images self-supervision Signal and Image processing Supervised learning Task analysis Training unsupervised learning Variational Autoencoder
ISSN:	0196-2892, 1558-0644
Online Access:	Get full text
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