Best Practices for Managing Processed Ortho Imagery

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 Desktop Large Image Collections Mosaic Dataset

7. USDA NAIP http://naip.arcgis.com 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 Orthorectified mosaic (compressed, *SID or *JP2) Edge matched or overlapping ortho tiles Individual orthophotos

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

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

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

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

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

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

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

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

19. Automation Image Management Workflows

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

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

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

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!