@inproceedings{58711e4856374a04a56c582d614dc8bc,
title = "EO-Validation: Low Latency Commodity-Based Collaborative Validation Framework for Geoai Data Products",
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{\textquoteright}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{\textquoteright}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.",
keywords = "deep learning, land cover, land use, machine learning, remote sensing, validation",
author = "Caraballo-Vega, {Jordan A.} and Spradlin, {Caleb S.} and Mark Carroll and Neigh, {Christopher S.R.} and Margaret Wooten and Konrad Wessels and Le, {Minh Tri} and Strong, {Savannah L.} and Melanie Frost and Amanda Burke and Woubet Alemu and Diouf, {Abdoul Aziz} and Modou Mbaye and Babacar Ndao and Nathan Thomas and Molly Brown",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.",
year = "2024",
month = jul,
day = "7",
doi = "10.1109/igarss53475.2024.10641506",
language = "English",
isbn = "979-8-3503-6033-2 ",
volume = "51",
series = "IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium",
publisher = "IEEE",
pages = "6916--6919",
booktitle = "IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium",
}