Application of commercial remote sensing to issues in human geography

Irvine, John; Kimball, Jennessa; Regan, John; Lepanto, Janet A.

doi:10.1109/aipr.2013.6749327

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…• Mean values and/or standard deviations of specific image features [3] computed across all of the image chips for a given country. Thus, these features capture an aggregation of the level and variability of the physical observables derived from the imagery; • The predicted values from the imagery context models for specific indicators related to infrastructure, urbanization, wealth, and governance (Table 2); • Indicators derived from the Twitter data, specifically the volume of political Tweets, and the two indicators described above, dubbed alerts and odds ratio.…”

Section: Models For World Bank Indicesmentioning

confidence: 99%

“…We derive the context information from commercial imagery, which is globally accessible. Previous research has demonstrated the connection between machine-recognizable imagery features and local socio-economic descriptors for selected regions [1] [2] [3]. Previous machine learning models have demonstrated predictions of conditions in Afghanistan, Botswana, Brazil, Kenya, Nigeria, Venezuela, and Zimbabwe.…”

Section: Introductionmentioning

confidence: 99%

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Combining remote sensing and social media data to predict indicators of political and economic instability

Irvine

2023

Geospatial Informatics XIII

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Understanding a region’s socio-economic conditions can inform the development of policies in both the public and private sectors. Commercial satellite imagery provides socio-economic context. By combining commercial imagery with geospatially enhanced social media, we generate local measures of political and economic instability risks at a regional and national scale. We present models that generate instability estimates by fusing socio-economic contextual data from commercial imagery with high-tempo social media data. To assess model performance, we predict annual indicators of conditions for a country as assessed by the World Bank. The models relate model-derived features to indicators of political stability, control of corruption, rule of law, government effectiveness, voice and accountability, and gross domestic product using data from multiple countries. Comparison of our methods to the World Bank data demonstrate the strengths of our approach.

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Section: Models For World Bank Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining remote sensing and social media data to predict indicators of political and economic instability

Irvine

2023

Geospatial Informatics XIII

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show abstract

“…First, overhead imagery reveals numerous indicators of economic status (housing, vehicles, crop land, livestock, and infrastructure). Studies have explored the relationship between remote sensing data and the economy (Elvidge, Baugh, Kihn, Kroehl, Davis, & Davis, 1997;Irvine et al, 2013). Second, both higher income and equitable distribution of income are associated with good governance.…”

Section: Communications Infrastructure (Roads)mentioning

confidence: 99%