EO-Validation: Low Latency Commodity-Based Collaborative Validation Framework for Geoai Data Products

Jordan A. Caraballo-Vega, Caleb S. Spradlin, Mark Carroll, Christopher S.R. Neigh, Margaret Wooten, Konrad Wessels, Minh Tri Le, Savannah L. Strong, Melanie Frost, Amanda Burke, Woubet Alemu, Abdoul Aziz Diouf, Modou Mbaye, Babacar Ndao, Nathan Thomas, Molly Brown

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

Abstract

The proliferation of machine learning models, architectures, and datasets for Earth observation (EO) continues to rise dramatically. This pattern is expected to continue growing bringing with it an increase in the generation of remote sensing derived data products powered by geospatial artificial intelligence (GeoAI) techniques. Rigorous quality assessments and accuracy analysis needs to be undertaken for the science community to adopt many of these data products for scientific discovery of changes of the Earth’s land surface. While there is existing literature supporting and documenting best practices for the validation of GeoAI data products, the software to support large-scale collaborative validation efforts is limited. In this study we present the design and software implementation of a flexible commodity-based framework for large-scale global to regional validation of GeoAI data products. This framework’s main purpose is to enable, speed up, and optimize the acquisition of validation data for large-scale science projects with support across multiple sensors and spatial resolutions with little to no code. In addition, we present several use cases where this framework has enabled and streamlined the validation of global to regional data products at different spatial resolutions and within different computational platforms.
Original languageEnglish
Title of host publicationIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Place of PublicationAthens, Greece
PublisherIEEE
Pages6916-6919
Number of pages4
Volume51
ISBN (Electronic)979-8-3503-6032-5 , 979-8-3503-6031-8
ISBN (Print)979-8-3503-6033-2
DOIs
Publication statusPublished - 7 Jul 2024

Publication series

NameIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE

Keywords

  • deep learning
  • land cover
  • land use
  • machine learning
  • remote sensing
  • validation

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