Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid Learning
Many areas of the world are without basic information on the socioeconomic well-being of the residing population due to limitations in existing data collection methods. Overhead images obtained remotely, such as from satellite or aircraft, can help serve as windows into the state of life on the grou...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10443032/ |
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| author | Ethan Brewer Giovani Valdrighi Parikshit Solunke Joao Rulff Yurii Piadyk Zhonghui Lv Jorge Poco Claudio Silva |
| author_facet | Ethan Brewer Giovani Valdrighi Parikshit Solunke Joao Rulff Yurii Piadyk Zhonghui Lv Jorge Poco Claudio Silva |
| author_sort | Ethan Brewer |
| collection | DOAJ |
| description | Many areas of the world are without basic information on the socioeconomic well-being of the residing population due to limitations in existing data collection methods. Overhead images obtained remotely, such as from satellite or aircraft, can help serve as windows into the state of life on the ground and help “fill in the gaps” where community information is sparse, with estimates at smaller geographic scales requiring higher resolution sensors. Concurrent with improved sensor resolutions, recent advancements in machine learning and computer vision have made it possible to quickly extract features from and detect patterns in image data, in the process correlating these features with other information. In this work, we explore how well two approaches—a supervised convolutional neural network and semisupervised clustering based on bag-of-visual-words—estimate population density, median household income, and educational attainment of individual neighborhoods from publicly available high-resolution imagery of cities throughout the United States. Results and analyses indicate that features extracted from the imagery can accurately estimate the density (<italic>R</italic><inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> up to 0.81) of neighborhoods, with the supervised approach able to explain about half the variation in a population's income and education. In addition to the presented approaches serving as a basis for further geographic generalization, the novel semisupervised approach provides a foundation for future work seeking to estimate fine-scale information from aerial imagery without the need for label data. |
| format | Article |
| id | doaj-art-cb1e829b75bb462caa9c9400c75f8c84 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-cb1e829b75bb462caa9c9400c75f8c842025-08-20T02:55:53ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-01175668567910.1109/JSTARS.2024.336801810443032Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid LearningEthan Brewer0https://orcid.org/0000-0002-4367-0763Giovani Valdrighi1https://orcid.org/0000-0003-0106-4789Parikshit Solunke2https://orcid.org/0009-0003-5546-0135Joao Rulff3https://orcid.org/0000-0003-3341-7059Yurii Piadyk4https://orcid.org/0000-0002-2975-635XZhonghui Lv5https://orcid.org/0000-0001-5186-3699Jorge Poco6https://orcid.org/0000-0001-9096-6287Claudio Silva7https://orcid.org/0000-0003-2452-2295Spectral Sciences, Inc., Burlington, USAFundação Getulio Vargas, Rio de Janeiro, BrazilNew York University, New York, USANew York University, New York, USANew York University, New York, USAWilliam and Mary, Williamsburg, USAFundação Getulio Vargas, Rio de Janeiro, BrazilNew York University, New York, USAMany areas of the world are without basic information on the socioeconomic well-being of the residing population due to limitations in existing data collection methods. Overhead images obtained remotely, such as from satellite or aircraft, can help serve as windows into the state of life on the ground and help “fill in the gaps” where community information is sparse, with estimates at smaller geographic scales requiring higher resolution sensors. Concurrent with improved sensor resolutions, recent advancements in machine learning and computer vision have made it possible to quickly extract features from and detect patterns in image data, in the process correlating these features with other information. In this work, we explore how well two approaches—a supervised convolutional neural network and semisupervised clustering based on bag-of-visual-words—estimate population density, median household income, and educational attainment of individual neighborhoods from publicly available high-resolution imagery of cities throughout the United States. Results and analyses indicate that features extracted from the imagery can accurately estimate the density (<italic>R</italic><inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> up to 0.81) of neighborhoods, with the supervised approach able to explain about half the variation in a population's income and education. In addition to the presented approaches serving as a basis for further geographic generalization, the novel semisupervised approach provides a foundation for future work seeking to estimate fine-scale information from aerial imagery without the need for label data.https://ieeexplore.ieee.org/document/10443032/Aerial imagerycomputer visiondeep learningremote sensingsustainable development |
| spellingShingle | Ethan Brewer Giovani Valdrighi Parikshit Solunke Joao Rulff Yurii Piadyk Zhonghui Lv Jorge Poco Claudio Silva Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid Learning IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Aerial imagery computer vision deep learning remote sensing sustainable development |
| title | Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid Learning |
| title_full | Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid Learning |
| title_fullStr | Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid Learning |
| title_full_unstemmed | Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid Learning |
| title_short | Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid Learning |
| title_sort | granularity at scale estimating neighborhood socioeconomic indicators from high resolution orthographic imagery and hybrid learning |
| topic | Aerial imagery computer vision deep learning remote sensing sustainable development |
| url | https://ieeexplore.ieee.org/document/10443032/ |
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