Building Footprints with Python or: How I Learned to Stop Worrying and Love the CAMA
A cama
Why do you need Footprints? • Urban Planning – Building Density – Impervious surfaces – Shadow areas
• Emergency Services – Wind corridors – Flood / Surge damage predictions
• Law Enforcement – Event safety planning
Making footprints the fast way • Automatic LiDAR extraction – High cost – Wonky polygons – Great attributes!
Making footprints the slow way • Head-down digitizing – Slow – No attributes
What CAMA stores for you: UOP2007=W25S8BAS2007=S25UOP2007=W12B AS2007=N16W18N2W10UOP2007=N10W36S10 E36$W46S2W18S16UOP2007=W12FGR2007=N 24W2UOP2007=N15W23S15E23$W23S50E25N 26$S8E12N8$S29E18S2E10FOP2007=S10E36N1 0W36$E46N2E18N29$S8E12N8$S18FST2007=S1 0E25N10W25$E25N43W25$E25N8$
What Python does with that:
'W25', 'S8', 'S25', 'W12', 'N16', 'W18', 'N2', 'W10', 'N10', 'W36', 'S10', 'E36', 'W46', 'S2', 'W18', 'S16…
QPublic Web Output
Python Output
“Finishing” • Spatial Adjustment – Rotation – Shifting – No scaling
• Attributing with LiDAR – LiDAR Analyst by Overwatch
Potential Hang-ups • Poor data • Typos • Different CAMA system
Questions?
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