Best Practices for Managing & Serving Processed Ortho imagery · Best Practices for Managing &...
Transcript of Best Practices for Managing & Serving Processed Ortho imagery · Best Practices for Managing &...
Best Practices for Managing & Serving
Processed Ortho Imagery Cody Benkelman
Characteristics of Processed Ortho Imagery
• Typically 8 bit (sometimes 16)
• Typically 3 spectral bands (sometimes 4)
- RGB, Color IR
• May/may not have been color corrected
• File layout
- Typically delivered as regular edge-matched tiles
- Multi-image mosaics (e.g. from Drone2Map, or NAIP “compressed county mosaics”)
- Orthorectified single frames
• Example sources:
- USDA NAIP program
- Custom collections for state/local governments
- Drone flights processed by Drone2Map, Ortho Mapping, Pix4D, Agisoft, Simactive, Others…
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
Data Management Objectives
• Share imagery with users
• Manage cost vs. performance
- Implement In-house, Public Cloud, AGOL
• Ensure scalability & maintainability
- Apply automation
Image Management Using Mosaic DatasetsHighly 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 DatasetLarge Image
Collections
Desktop
Dynamic 4-band image service
NAIP imagery
Data courtesy of:
USDA APFO (Air Photo Field Office)
Mosaic Dataset Design
• Key metadata → Attribute Table
- Dates acquired (start, end)
- Date published
- Horizontal Accuracy (CE90)
• Handling NoData
• Source / Derived Model with Raster Functions
• Apply automation!
File Layout – one of three cases
Edge matched or overlapping
ortho tiles Individual orthophotosOrthorectified mosaic
( *.JPG, *SID or *JP2)
Handling NoData – Build Footprints
Edge matched or overlapping
ortho tiles Individual orthophotos
Build footprints → Clip to footprints
(recommended method to remove NoData)
Orthorectified mosaic
( *.JPG, *SID or *JP2)
Handling NoData – Set “NoData Value”
Edge matched or overlapping
ortho tiles Individual orthophotos
Alternative method to manage NoData
• May leave dark pixels (compression artifacts)
• May create NoData “holes” in dark areas
Orthorectified mosaic
( *.JPG, *SID or *JP2)
Source Imagery
SourceMosaic
Datasets
Source / Derived Data Model – begin with “Source” Mosaic Datasets
2007
2004
2010
Projection = same as source
Use Footprints to clip NoData
Create Overviews
Source Imagery
SourceMosaic
Datasets
2007
2004
2010
Source Mosaic Datasets – Direct to Raster Tile Cache (optional)
Raster Tile
Cache
Tile CacheService
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
Tile Cache considerationsImagery basemap with metadata (Example on ArcGIS Online)
• Cached imagery does not include metadata (date, sensor, etc.)
• Feature service overlay can be added, with imagery metadata, if desired
• See Cache Tools (next slide) for your data
• Example from ArcGIS Online for Esri Imagery Basemap:
Python Toolbox for generating, attributing, and publishing cache
http://esriurl.com/RasterTileCacheTools
Under Imagery Workflows
http://esriurl.com/ImageryWorkflows
Source Imagery
SourceMosaic
Datasets
Source / Derived Data Model – begin with “Source” Mosaic Datasets
2007
2004
2010
Source Imagery
SourceMosaic
Datasets
DerivedMosaic 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
Source Imagery
SourceMosaic
Datasets
DerivedMosaic Dataset
Combine into Derived Mosaic Dataset
Projection = adequate to cover all possible data
Overviews typically not required
Use Derived mosaic vs. cache if:
• Large # of image collections
• Imagery = 4+ bands
• Maximizing image quality
and/or minimizing disk
space
Source Imagery
SourceMosaic
Datasets
DerivedMosaic 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
Source Imagery
SourceMosaic
Datasets
DerivedMosaic 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
When the Derived parent is updated, all services
synchronize automatically
Image Management Workflows
http://esriurl.com/ImageryWorkflows
Automation
Customizing for your data
• C:\image_mgmt_workflows\PreprocessedOrthos\Parameter\Config
• Most fields may be left as-is → Esri’s recommended ‘best practices’
• Change highlighted fields...
Imagery Workflows
• Landing page
- http://esriurl.com/ImageryWorkflows
- http://esriurl.com/PreprocessedOrthos
• ArcGIS Online Group
- Downloadable scripts & sample data
- http://esriurl.com/PreprocessedScript
- http://esriurl.com/RasterTileCacheTools
Best Practice Workflows for Image Management, Analysis, & Use
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 users need imagery in overlap
• If cache, is original metadata important?
- Seamlines with metadata in Feature Service e.g. http://esriurl.com/ImageryBasemapMetadata
• Time enabled?
• Data format: Tiles, Ortho Files, or Orthomosaic
• Apply automation!