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Edgar Allan Poe/Sullivan's Island Library
Closed for renovations
Phone: (843) 883-3914
West Ashley Library
9 a.m. – 7 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
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Village Library
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St. Paul's/Hollywood Library
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Otranto Road Library
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McClellanville Library
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Keith Summey North Charleston Library
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John's Island Library
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Hurd/St. Andrews Library
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Beyond satellite imagery: the influence of map representations on socio-economic prediction.
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- Author(s): Koch, David; Thaler, Simon; Zhang, Zedong; Despotovic, Miroslav
- Source:
Remote Sensing Letters; Jun2024, Vol. 15 Issue 6, p634-644, 11p- Subject Terms:
- Source:
- Additional Information
- Subject Terms:
- Abstract: Incorporating satellite imagery is crucial for remote sensing and computer vision in socio-economic studies. Machine learning techniques are typically used to extract valuable information from satellite images. This article presents an enhanced approach applying computer vision not only to satellite images but also to five different map sources, such as OpenStreetMap and building footprints. The goal is to determine if useful insights can be derived from simplified feature representations, improving the understanding of fundamental satellite imagery data. We conducted an experiment predicting the settlement patterns of university graduates in Vienna, using a convolutional neural network (CNN) to analyze grid cell images (250 m × 250 m) from satellites and five different maps. The model predicted five density classes of graduates, achieving an accuracy rate of 35.99% using building footprints, outperforming the 35.15% accuracy based on satellite images, while other map representations underperformed. These results suggest that building outlines and the open space between buildings contain vital predictive information. Our findings highlight the potential of this approach beyond socio-economic variables, demonstrating the capability of understanding maps via CNNs. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Remote Sensing Letters is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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