1. Best Practices for Managing Processed Ortho Imagery Cody A. Benkelman

2. Characteristics of Processed Ortho Imagery Typically 8 bit (sometimes 16) Typically 3 spectral bands (sometimes 4) - RGB or Color IR May/may not have been color corrected File layout - Typically delivered as regular edge-joined tiles OR - Multi-image mosaics (e.g. NAIP “compressed county mosaics”, mosaics from UAS flights) Sources: - USDA NAIP program - Custom collections for state/local governments - UAS flights processed through Drone2Map, Pix4D, Agisoft, Simactive, Others…

3. Typical Uses of Processed Ortho Imagery State/county/city government – local data, or web accessible - Background imagery - Change analysis - Growth planning - Pervious/Impervious surface mapping - Public use (Tourism, Real Estate, Hunting, Hiking…) Manual feature extraction - Road/Highway infrastructure - Wetlands - Forestry - Boundary mapping

4. Usage Modes of Processed Ortho Imagery Visual Interpretation (most common) - May desire access to image metadata - Manual feature extraction - Large numbers of Users Technical image analysis(less common) - Users need data values - Typically 4 band, may be 16 bit - Fewer, expert users

5. Data Management Objectives Share imagery with users Manage Cost vs. Performance - Implement In-house, DIY Cloud, AGOL? Ensure scalability & maintainability - Apply automation

6. Image Management Using Mosaic Datasets Highly Scalable, From Small to Massive Volumes of Imagery Create Catalog of Imagery Reference Sources Ingest & Define Metadata Define Processing to be Applied Apply: On-the-fly Processing Dynamic Mosaicking Access as Image or Catalog Mosaic Dataset Large Image Collections Desktop

7. http://naip.arcgis.com USDA NAIP Data courtesy of: USDA APFO (Air Photo Field Office)

8. Mosaic Dataset Design Key metadata  Attribute Table - Dates acquired (start, end) - Date published - Horizontal Accuracy (CE90) Handling NoData Source / Derived Model with Raster Functions Automation!

9. File Layout – one of three cases Edge matched or overlapping ortho tiles Individual orthophotos Orthorectified mosaic (compressed, *SID or *JP2)

10. Handling NoData – Build Footprints Edge matched or overlapping ortho tiles Individual orthophotos Build footprints  Clip to footprints to remove NoData Orthorectified mosaic (compressed, *SID or *JP2)

11. Handling NoData – Set “NoData Value” Edge matched or overlapping ortho tiles Orthorectified mosaic (compressed, *SID or *JP2) Individual orthophotos

12. Source Imagery Source Mosaic Datasets Source / Derived Data Model – begin with “Source” Mosaic Datasets 2007 2004 2010

13. Source Imagery Source Mosaic Datasets 2007 2004 2010 Source Mosaic Datasets – Direct to Raster Tile Cache (optional) Raster Tile Cache Tile Cache Service

14. What is a raster tile cache? Cut image into very large number of small tiles Fixed projection (typically Web Mercator Auxiliary Sphere) Multiple levels Typically 256x256 pixels 3 band RGB No size limit Notes: - Duplicates data volume - Any data in overlap is lost

15. GP Tools for generating, attributing, and publishing cache http://esriurl.com/RasterTileCacheTools Recorded live training seminar (LTS) for image caching: http://esriurl.com/ImageCacheLTS http://esriurl.com/ImageCacheLTS

16. North Carolina OneMap Time Enabled Data courtesy of: State of North Carolina

17. Source Imagery Source Mosaic Datasets Derived Mosaic Dataset Combine into Derived Mosaic Dataset Use TABLE Raster Type Advantage: All image data* available in a single location * “All data” refers to all data that makes sense together; this should not mix elevation data, for example, with imagery

18. Source Imagery Source Mosaic Datasets Derived Mosaic Dataset Full Image Service f Single image service with multiple server functions On-the-fly Products using Server Raster Functions …many other functions True Color NDVI Color Infrared Pan Sharpened

19. Image Management Workflows Automation

20. Source Imagery Source Mosaic Datasets Derived Mosaic Dataset ff f f Referenced Mosaic Datasets Alternative design using Referenced Mosaic Datasets Dynamic Image Services Available since ArcGIS 10.0 Appropriate for serving to WMS clients True Color NDVI Color IR Pan Sharp

21. Source Imagery Source Mosaic Datasets Derived Mosaic Dataset ff f f When the Derived parent is updated, all services synchronize automatically Update with new data Referenced Mosaic Datasets Dynamic Image Services True Color NDVI Color IR Pan Sharp

22. Image Management Workflows Resource Center landing page http://esriurl.com/6005 http://esriurl.com/6005 Guidebook in Help System http://esriurl.com/6007 http://esriurl.com/6007 ArcGIS Online Group http://esriurl.com/6539 http://esriurl.com/6539 - Downloadable scripts & sample data Recorded webinars - Image management http://esriurl.com/LTSImgMgmt http://esriurl.com/LTSImgMgmt - Image caching http://esriurl.com/ImageCacheLTS http://esriurl.com/ImageCacheLTS Best Practice Workflows for Image Management

23. Summary – Key considerations Raster Tile Cache vs. Dynamic Image Services - Cache: fastest performance for large # of users - Dynamic: if > 8 bit, or > 3 bands, or need imagery in overlap If cache, is original metadata important? Time enabled? Data format: Tiles, Ortho Files, or Orthomosaic Apply automation!