Abstract
To effectively disseminate location-linked information despite the existence of digital walls across institutions, this study developed a cross-institution mobile App, named GeoFairy2, to overcome the virtual gaps among multi-source datasets and aid the general users to make thorough accurate in-situ decisions. The app provides a one-stop service with relevant information to assist with instant decision making. It was tested and proven to be capable of on-demand coupling and delivering location-based information from multiple sources. The app can help general users to crack down the digital walls among information pools and serve as a one-stop retrieval place for all information. GeoFairy2 was experimented with to gather real-time and historical information about crops, soil, water, and climate. Instead of a one-way data portal, GeoFairy2 allows general users to submit photos and observations to support citizen science projects and derive new insights, and further refine the future service. The two-directional mechanism makes GeoFairy2 a useful mobile gateway to access and contribute to the rapidly growing, heterogeneous, multisource, and location-linked datasets, and pave a way to drive us into a new mobile web with more links and less digital walls across data providers and institutions.
Highlights
Earth observation (EO) data have been increasing exponentially in the past decades, and have become a valuable data source in many important scientific and application domains
The information server, crowd data server, and validation server are deployed on a private cloud, GeoBrainCloud, powered by Apache CloudStack and backed by the resources located in the Aquatic data center of GMU
For the datasets not hosted in GMU, GeoFairy2 would directly query third-party web services without going through the GMU server to avoid potential bottlenecks on performance, relieve the burden on the proxy server, accelerate the information loading, and eventually address the data heterogeneity challenges
Summary
Earth observation (EO) data have been increasing exponentially in the past decades, and have become a valuable data source in many important scientific and application domains. The data are stored in distributed institutions and governed by a different set of regulations, policies, and standards [3]. Virtual walls exist among datasets and can be strongly felt when trying to retrieve and carry out combined analyses. NOAA (National Oceanic and Atmospheric Administration) data centers mostly use NetCDF (Network Common Data Form) as a default form. NASA (National Aeronautics and Space Administration) and DAAC
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