1. The Challenge of Community LiDAR data
LiDAR acquisition over km2 = billions of LiDAR returns
> km2 0.5 m DEM tiles as “standard” products
Large user community with variable needs and levels of sophistication.
How do we deliver large community datasets such as these?
Northern California GeoEarthScope
LiDAR

2. The Challenge of Community LiDAR data
LiDAR coverage of the southern San Andreas and San Jacinto faults
- ~ 3.7 billion LiDAR returns, ~1.9 TB
- Very high-res., supports cm DEMs

3. Delivery
Hard Disc or DVD
Files organized into tiles
Point cloud
Pre-computed DEMs
Organization typically kilometer tiles or USGS quarter quads
Naming convention hopefully meaningful

4. Files
x,y,z,date,time,returnnumber,noreturns,offnidar,returnint,classification
, ,69.06,1204, E+003,1,5,15.63,116,"G"
, ,69.28,1204, E+003,1,5,15.18,114,"G"
, ,69.11,1204, E+003,1,5,14.72,86,"G"
, ,68.84,1204, E+003,1,5,14.27,84,"G"
, ,68.74,1204, E+003,1,5,15.58,104,"G"
, ,68.91,1204, E+003,1,5,15.12,132,"G"
, ,68.86,1204, E+003,1,5,14.67,100,"G"
, ,68.53,1204, E+003,1,5,14.21,77,"G"
, ,68.28,1204, E+003,1,5,13.77,61,"G"
, ,68.12,1204, E+003,1,5,13.40,62,"G"
, ,68.05,1204, E+003,1,5,12.95,59,"G"
, ,67.77,1204, E+003,1,5,12.50,55,"G“
x,y,z,gpstime,returnint,passid
, ,920.10," ",117,"Str_169"
, ,919.99," ",138,"Str_169"
, ,919.24," ",137,"Str_169"
, ,918.94," ",126,"Str_169"
, ,919.26," ",104,"Str_169"
, ,919.28," ",107,"Str_169"

5. Files, Cont.
LAS: Binary point cloud format
“industry standard”
Much smaller file sizes due to binary format
Requires specific tools to read or translate
Relatively uncommon in geoscience datasets

6. Files cont.
Digital Elevation Models:
ESRI binary grids
Filtered and unfiltered
a.k.a. bare earth and full feature
Ascii grids (.asc)
Arc ascii
“standard” ascii