Satellite Image Analysis for Poverty Prediction - Swapna, T.
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High-resolution satellite imagery is increasingly available globally and contains abundant information about landscape features that could be correlated with economic activity. Unfortunately, such data are highly unstructured and thus challenging to extract meaningful insights from at scale, even with intensive manual analysis. Recent applications of deep learning techniques to large-scale image data sets have led to marked improvements in fundamental computer vision tasks such as object detection and classification, but these techniques are generally the most effective in supervised learning.

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Produktbeschreibung
High-resolution satellite imagery is increasingly available globally and contains abundant information about landscape features that could be correlated with economic activity. Unfortunately, such data are highly unstructured and thus challenging to extract meaningful insights from at scale, even with intensive manual analysis. Recent applications of deep learning techniques to large-scale image data sets have led to marked improvements in fundamental computer vision tasks such as object detection and classification, but these techniques are generally the most effective in supervised learning.
Autorenporträt
T. Swapna trabalha como professora assistente no Departamento de Ciência da Computação e Engenharia no Instituto de Tecnologia e Ciência G. Narayanamma (para mulheres), Hyderabad. Ela tem 17 anos de rica experiência docente. Participou de cerca de 25 workshops sobre diversos temas na área de aprendizado de máquina, segurança de redes e inteligência artificial.