Economic well-being assessment: a review of traditional and remote sensing approaches
This paper reviews the evolution of economic well-being assessment, examining traditional methods based on surveys and statistics alongside the emergent field of satellite remote sensing. Traditional approaches, employing indicators like GDP, HDI, and MPI, offer established methodologies but face li...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2504137 |
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| author | Longfei Wang Tengfei Long Wei Jiang Elhadi Adam Chunhui Wen Weili Jiao Guojin He |
| author_facet | Longfei Wang Tengfei Long Wei Jiang Elhadi Adam Chunhui Wen Weili Jiao Guojin He |
| author_sort | Longfei Wang |
| collection | DOAJ |
| description | This paper reviews the evolution of economic well-being assessment, examining traditional methods based on surveys and statistics alongside the emergent field of satellite remote sensing. Traditional approaches, employing indicators like GDP, HDI, and MPI, offer established methodologies but face limitations in data accessibility, spatial coverage, and capturing dynamic changes. Satellite remote sensing, utilizing nighttime light, daytime imagery, and derived data like NDVI, overcomes these constraints by providing large-scale, timely, and periodic surface information. Furthermore, we analyze methods for large-scale economic well-being assessment using remote sensing, encompassing statistical analysis, machine learning, deep learning, and transfer learning. Finally, we explore future directions, emphasizing the development of more comprehensive indicators, multi-source data fusion integrating subjective well-being, and advancements in deep neural networks to improve accuracy, generalization, and interpretability for robust, large-scale economic well-being assessment and poverty reduction strategies. |
| format | Article |
| id | doaj-art-0b3ba31fc56d4c5b9ff75b55bd2e3455 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-0b3ba31fc56d4c5b9ff75b55bd2e34552025-08-25T11:32:04ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2504137Economic well-being assessment: a review of traditional and remote sensing approachesLongfei Wang0Tengfei Long1Wei Jiang2Elhadi Adam3Chunhui Wen4Weili Jiao5Guojin He6Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaChina Institute of Water Resources and Hydropower Research, Beijing, People’s Republic of ChinaSchool of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, South AfricaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of ChinaThis paper reviews the evolution of economic well-being assessment, examining traditional methods based on surveys and statistics alongside the emergent field of satellite remote sensing. Traditional approaches, employing indicators like GDP, HDI, and MPI, offer established methodologies but face limitations in data accessibility, spatial coverage, and capturing dynamic changes. Satellite remote sensing, utilizing nighttime light, daytime imagery, and derived data like NDVI, overcomes these constraints by providing large-scale, timely, and periodic surface information. Furthermore, we analyze methods for large-scale economic well-being assessment using remote sensing, encompassing statistical analysis, machine learning, deep learning, and transfer learning. Finally, we explore future directions, emphasizing the development of more comprehensive indicators, multi-source data fusion integrating subjective well-being, and advancements in deep neural networks to improve accuracy, generalization, and interpretability for robust, large-scale economic well-being assessment and poverty reduction strategies.https://www.tandfonline.com/doi/10.1080/17538947.2025.2504137Povertysubjective well-beingsatellitedeep learningtransfer learning |
| spellingShingle | Longfei Wang Tengfei Long Wei Jiang Elhadi Adam Chunhui Wen Weili Jiao Guojin He Economic well-being assessment: a review of traditional and remote sensing approaches International Journal of Digital Earth Poverty subjective well-being satellite deep learning transfer learning |
| title | Economic well-being assessment: a review of traditional and remote sensing approaches |
| title_full | Economic well-being assessment: a review of traditional and remote sensing approaches |
| title_fullStr | Economic well-being assessment: a review of traditional and remote sensing approaches |
| title_full_unstemmed | Economic well-being assessment: a review of traditional and remote sensing approaches |
| title_short | Economic well-being assessment: a review of traditional and remote sensing approaches |
| title_sort | economic well being assessment a review of traditional and remote sensing approaches |
| topic | Poverty subjective well-being satellite deep learning transfer learning |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2504137 |
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