How to Master Raster (Faster) - Tips and Examples
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Transcript of How to Master Raster (Faster) - Tips and Examples
New to FME?
Housekeeping slide
What is this webinar about
We’re going to examine some problems customers have run into with solutions that customers have crafted, in some cases going to expand on their solutions to give additional advice- 5 different scenarios, each case, dig into - Goal: well rounded overview of usings
FME’s raster capabilities to the full for solving common raster scenarios
Customer Story 1: Reprojecting tiles
Demo/Solution 1: http://blog.websoftdev.com/fme/making-reprojected-raster-tiles-work-with-fme
Reprojection/Mosaicing using NOdata/Alpha for
Problem we’re solving
Using Nodata values in raster processing to avoid gaps in reprojected mosaics.
The Solution
Demo/Solution 1
Key FME Functionality Used
Apply nodata and or Alpha before any reprojections or clips
Customer story 2: Image Download
http://impossibleaddress2find.blogspot.ca/2014/03/featching-all-of-ahn2-raster-data-with.html
**make intro page with this blog
Problem we’re solving
Utilizing a remote raster dataset
The Solution
Key FME Functionality Used
Federating GeoRSS and Shape data to get required tiles.
Raster downloading and mosaicking.
Reading from URL and Zip file.
Midway Q&A
Customer stories 3: Raster Arithmetic to the rescue
Solving problems with raster arithmetic
http://blog.safe.com/2013/09/a-perfect-pairing-fme-and-rapideye-imagery-in-la-rioja-spain/
Problem we’re solving
Generate land cover classification from RapidEye multispectral images for agricultural analysis:
● Step 1 – Convert each pixel’s Digital Number (DN) to a radiance value by multiplying the DN by
the radiometric scale factor.
● Step 2 – Convert radiance values to ToA (top of atmosphere) reflectance values, taking into
consideration variables such as:
o distance from the sun
o angle of incoming solar radiation.
The variables, different for each scene, are defined as parameters in the workspace.
Calculate the solar azimuth angle from the attributes created with a ParameterFetcher
Calculate the distance between the sun and earth.
RasterExpressionEvaluator performs ToA calculations in each band.
Use another RasterExpressionEvaluator to calculate vegetation indexes:
● NDVI (Normalized Difference Vegetation Index)● TCARI (Transformed Chlorophyll Absorption in Reflectance
● OSAVI (Optimized Soil Adjusted Vegetation Index)
The Solution
Write final result to TIFF file.
Customer story 3b:
Extract dangerous vegetation heights from Lidar
Solution
pic of workspace
Key FME Functionality Used
RasterExpressionEvaluator
Customer story 4: Rapid response Flood mapping
Gavin Park's FME UC presentation (featuring Mapnik and Excel) -
http://www.safe.com/fmeuc/erdas-imagine-spatial-modeller/
Problem we’re solving
Using a combination of FME functionality and external tools to create Flood maps
SOlution: Flooding Proof of Concept
Choose Image Scene
Processing Workflow
Flood Detection
Download and write to disk
Vectorize Raster Output
Remove Existing
Water Bodies
Identify Impacted Assets
WCS
Pan Sharpen Image
Image MapFME Processing
ERDAS Processing
FME Driven Workflow
Access Web Coverage Service
Mid Translation Read / Write
● Need to pass Raster Image between FME and Imagine● Use FME to write the data to local disk for Imagine to pick up
and use within the Spatial Modeller
Flood Detection Spatial Model
Cloud Free
Cloud Cover
Running Spatial Models in FME
Reading Raster Back into FME
Running Erdas Pan-Sharpening within FME
Generate Raster Output
Flood Extents Overlay
Automatic Excel Reports
● Real Time River Flows
● Automatically graph flows
● Identify affected assets / property
● Embed Image Map
Key FME Functionality Used
FME’s ability to utilize other applications through the Python APIs and the PythonCaller .
Bonus customer story 5 - gowing trees with FME (Owen Powell)
Blog ** add link**, FME trees, Mapnik splashscreen
Owen’s splashscreen
Poll question
Which solution was the most relevant to the challenges you face?
Wrap-up slides
Midway Q&A
Contact slide