Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing Article Swipe
Fan Yang
,
Shinji Ishida
,
Mengyan Zhang
,
Daniel Jenson
,
Swapnil Mishra
,
Jhonathan Navott
,
Seth Flaxman
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.14119
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.14119
Remote sensing imagery offers rich spectral data across extensive areas for Earth observation. Many attempts have been made to leverage these data with transfer learning to develop scalable alternatives for estimating socio-economic conditions, reducing reliance on expensive survey-collected data. However, much of this research has primarily focused on daytime satellite imagery due to the limitation that most pre-trained models are trained on 3-band RGB images. Consequently, modeling techniques for spectral bands beyond the visible spectrum have not been thoroughly investigated. Additionally, quantifying uncertainty in remote sensing regression has been less explored, yet it is essential for more informed targeting and iterative collection of ground truth survey data. In this paper, we introduce a novel framework that leverages generic foundational vision models to process remote sensing imagery using combinations of three spectral bands to exploit multi-spectral data. We also employ methods such as heteroscedastic regression and Bayesian modeling to generate uncertainty estimates for the predictions. Experimental results demonstrate that our method outperforms existing models that use RGB or multi-spectral models with unstructured band usage. Moreover, our framework helps identify uncertain predictions, guiding future ground truth data acquisition.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.14119
- https://arxiv.org/pdf/2411.14119
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404652697
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4404652697Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.14119Digital Object Identifier
- Title
-
Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote SensingWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-21Full publication date if available
- Authors
-
Fan Yang, Shinji Ishida, Mengyan Zhang, Daniel Jenson, Swapnil Mishra, Jhonathan Navott, Seth FlaxmanList of authors in order
- Landing page
-
https://arxiv.org/abs/2411.14119Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2411.14119Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2411.14119Direct OA link when available
- Concepts
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Estimation, Regression, Econometrics, Regression analysis, Remote sensing, Computer science, Statistics, Environmental science, Economics, Geography, Machine learning, Mathematics, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.targeting | 98 |
| abstract_inverted_index.uncertain | 178 |
| abstract_inverted_index.collection | 101 |
| abstract_inverted_index.estimating | 30 |
| abstract_inverted_index.limitation | 54 |
| abstract_inverted_index.regression | 86, 143 |
| abstract_inverted_index.techniques | 67 |
| abstract_inverted_index.thoroughly | 78 |
| abstract_inverted_index.conditions, | 32 |
| abstract_inverted_index.demonstrate | 156 |
| abstract_inverted_index.outperforms | 160 |
| abstract_inverted_index.pre-trained | 57 |
| abstract_inverted_index.quantifying | 81 |
| abstract_inverted_index.uncertainty | 82, 149 |
| abstract_inverted_index.Experimental | 154 |
| abstract_inverted_index.acquisition. | 185 |
| abstract_inverted_index.alternatives | 28 |
| abstract_inverted_index.combinations | 127 |
| abstract_inverted_index.foundational | 118 |
| abstract_inverted_index.observation. | 12 |
| abstract_inverted_index.predictions, | 179 |
| abstract_inverted_index.predictions. | 153 |
| abstract_inverted_index.unstructured | 170 |
| abstract_inverted_index.Additionally, | 80 |
| abstract_inverted_index.Consequently, | 65 |
| abstract_inverted_index.investigated. | 79 |
| abstract_inverted_index.multi-spectral | 134, 167 |
| abstract_inverted_index.socio-economic | 31 |
| abstract_inverted_index.heteroscedastic | 142 |
| abstract_inverted_index.survey-collected | 37 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |