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Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments.

Fedra TrujillanoGabriel JimenezEdgar ManriqueNajat F KahambaFredros OkumuNombre ApollinaireGabriel Carrasco-EscobarBrian BarrettKimberly Fornace
Published in: International journal of health geographics (2024)
Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.
Keyphrases
  • deep learning
  • convolutional neural network
  • high resolution
  • artificial intelligence
  • machine learning
  • public health
  • big data
  • mass spectrometry
  • risk assessment
  • climate change
  • liquid chromatography