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Mapping Poverty In The Philippines Using Machine Learning, Satellite Imagery, And Crowd-sourced Geospatial Information.pdf

Mapping-Poverty-in-the-Philippines-Using-Machine-Learning-Satellite-Imagery-and-Crowd-sourced-Geospatial-Information.pdf
Preview of Mapping Poverty in the Philippines Using Machine Learning, Satellite Imagery, and Crowd-sourced Geospatial Information
🔗 Source: k4all.org
📊 Size: 2.49 MB
📄 Pages: 6 pages
⬇️ Downloads: 110

Summary

Researchers used machine learning, satellite imagery, and crowd-sourced geospatial information to map poverty in the Philippines, estimating socioeconomic indicators such as wealth level, education, access to electricity, and access to water. The study found that models trained on publicly available data achieved similar predictive performance to those using proprietary satellite images, with the best models explaining approximately 63% of the variation in asset-based wealth. The approach leveraged nighttime lights satellite imagery and volunteered geographic information from OpenStreetMap to estimate socioeconomic indicators, providing a cost-effective alternative to traditional methods.

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Description

Researchers used machine learning, satellite imagery, and crowd-sourced geospatial information to map poverty in the Philippines, estimating socioeconomic...

Technical Information

  • File Format: PDF
  • File Size: 2.49 MB
  • Pages: 6
  • Language: EN
  • Total Downloads: 110
  • Last Updated: 2 weeks ago

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This PDF document about Mapping Poverty in the Philippines Using Machine Learning, Satellite Imagery, and Crowd-sourced Geospatial Information provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Mapping Poverty in the Philippines Using Machine Learning, Satellite Imagery, and Crowd-sourced Geospatial Information.

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Mapping guideMapping tutorialPoverty guidePoverty tutorialPhilippines guidePhilippines tutorialUsing guideUsing tutorialMachine guideMachine tutorialLearning, guideLearning, tutorialSatellite guideSatellite tutorialImagery, guideImagery, tutorialCrowd-sourced guideCrowd-sourced tutorialGeospatial guideGeospatial tutorialInformation guideInformation tutorial

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