@inproceedings{22f32e6744bb4c2aa0ea5f31b3413c2b,
title = "Data to science: an open-source online platform for managing, visualizing, and publishing UAS data",
abstract = "Recent advancements in sensor technologies make it possible to collect fine spatial and high temporal resolution remote sensing data and automatically extract informative features in a high throughput mode. As researchers increasingly have access to tools to collect big data, such as Unmanned Aerial Vehicles (UAV) and Controlled Environment Phenotyping Facility (CEPF), there is a need for generating quantitative phenotypic from the collected geospatial data. While precision agriculture technology aims to protect our environment and produce enough food to feed a growing population, the massive volume of geospatial data generated by the research scientists and the lack of software packages customized for processing these data make it challenging to develop transdisciplinary research collaboration around this data. We will share our efforts to develop an open-source online platform for UAS HTP data management to address the big data challenges.",
keywords = "Big Data, FAIR Principle, High Throughput Phenotyping (HTP), Open Data Science, Unoccupied Aircraft System (UAS)",
author = "Jinha Jung and Songlin Fei and Mitch Tuinstra and Yang Yang and Diane Wang and Carol Song and Jeffrey Gillan and Mahendra Bhandari and Amir Ibrahim and Lan Zhao and Tyson Swetnam and Bryan Barker and Minyoung Jung and Ben Hancock",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX 2024 ; Conference date: 22-04-2024 Through 23-04-2024",
year = "2024",
doi = "10.1117/12.3021199",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Thomasson, {J. Alex} and Christoph Bauer",
booktitle = "Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX",
}