The AmericaView NAIP Viewer is a collaborative project involving several AmericaView members and combining capabilities of multiple AmericaView technology projects to provide both visualization and download of National Agricultural Imagery Program (NAIP) imagery for much of the nation. Since the project's inception in 2010, much has changed, and the project is under continual development.
The original Web Mapping Service mechanism developed by researchers from AlaskaView at the University of Alaska, Fairbanks was surplanted by RealEarth technology developed at the Space Science Engineering Center(SSEC) at the University of Wisconsin, Madison (WisconsinView.) Development continues on the project as new data and features are added and tested. The following description includes features still being developed.
(1) Data are uploaded to a special folder on EODN using eodn_upload from the dlt-tools package by a participating AV member at StateView X. (2) A technician downloads the data using eodn_download (also part of the dlt-tools package and (3) writes the files to local storage. (4). The RealEarth ingest process retrieves NAIP files from local storage, processes the data and builds WMS tiles for RealEarth.
The source NAIP files are then deleted from local storage. (5) After processing, users can view NAIP imagery in RealEarth.
(A) A user initiates a download through RealEarth. (B) RealEarth accesses a URL on the StateView archive server that (C) uploads the selected image to EODN. (D) When the upload is complete, the user downloads the image via multiple simultanious data streams from EODN.This basic process can be used with other datasets to provide visualization and download of diverse data sets and illustrates two important use cases for EODN. The first is EODN as a back-end transport system for moving very large datasets (NAIP data for larger states can represent several terrabytes.) Secondly, it illustrates the use of EODN for delivery of data that are not permanetly stored on EODN. Even with the overhead of uploading data to EODN on demand, the efficiency of EODN's mutli-stream data movement results in a substantial performance advantage over traditional data distribution approaches.