Automated Damage Assessment for Event Response from Overhead Imagery. Shari Matzner Jerry Tagestad Sadie Montgomery Andre Coleman Tom Warfel

Transcription

1 Automated Damage Assessment for Event Response from Overhead Imagery Shari Matzner Jerry Tagestad Sadie Montgomery Andre Coleman Tom Warfel

2 The power is out Extreme weather events are the primary cause of power outages that affect thousands of people. Weather- Related Outages Las;ng > 48 Hours Equipment 3% Fuel Supply 9% Physical Attack 2% Software Problem 1% Category Weather Causes of Power Outages Software Problem Physical Attack Weather 85% Fuel Supply Equipment TRE 6% FRCC 5% WECC 14% ear ure 9. Significantly increasing (p < 0.03 weather related outages with affec Data: Reported outages from

3 Situational awareness is key to rapid power restoration. Remotely sensed imagery can provide situational awareness. Automated processing increases the value of imagery. Decision support systems need to be flexible, able to consume whatever data is available. 3

4 Imagery can provide situational awareness. Synthetic Aperture Radar See at night, through clouds. Multi-spectral Satellite Image See the big picture. ellow = flooded Tornado track Tornado at Tuscaloosa, AL 2011 ASTER, 15 m resolu2on Natural Color Aerial Image See details. Road blocked 4 Hurricane Ike at Galveston, T 2009 NOAA, 34 cm resolu2on Flooding at Queensland, AU 2011 RADARSAT- 2, 8 m resolu2on

5 Imagery can be acquired within 24 hours of an event. Satellite operators offer rapid acquisi;on to support first responders. Image Copyright DigitalGlobe NOAA s Remote Sensing Division mobilizes its airborne sensor for emergencies. New micro- satellite constella;ons promise real- ;me coverage. Image Copyright PlanetLabs UAVs are the future of disaster response. 5

6 Automated processing increases the value of imagery. PNNL is developing algorithms for different image types to automatically extract damage information. Algorithms Change DetecCon Rubble DetecCon Flood Mapping Downed Tree DetecCon Burn Mapping MulCspectral SAR Natural Color LR MR HR LR MR HR HR 6 Algorithm is applicable LR = Low Resolu;on MR = Medium Res. HR = High Res.

7 Change can indicate damaged areas. Change detection compares a before image and an after image. The challenge is to distinguish between changes due to the weather event and other changes. Breezy Point fire, Queens, N 2012 Before A_er Source: Google Crisis Maps 7

8 Change Detection Algorithm raw Before raw A_er raw Difference Convert to reflectance Apply map projec;on Resample Align and select overlap region processed Before processed A_er Convert to z- score Apply classifica;on model Interpolate and smooth Damage Map other data tornado track storm track land cover subtract 8

9 Automated processing extracts damage information Alabama: 62 confirmed tornadoes across the state; 262,000 customers without power. BEFORE None Minor Major Damage Category Destroyed Damaged Transmission Lines None Damaged Distribution Lines Source: Na;onal Agricultural Imagery Program (NAIP Damage Category Destroyed Change DetecCon Tornado Damage Area None Minor AFTER Major Damage Visualiza;on Source: WorldView- 2, Resolu;on: 2 m, Area: 125 square miles Miles 3.2 Minor Major Destroyed Damage Category Damaged Distribution Poles None Minor Damage Report Major Destroyed Damage Category Damaged Substations

10 Rubble indicates damage. Original image. Rubble detec;ons (red. 10

11 Rubble Detection Algorithm 1. Convert color image to intensity (gray scale. 2. Calculate the gradient at each pixel. 3. Calculate the entropy of the gradient orienta;on Gradient Orienta;on Histogram Count Magnitude: Orienta;on: G = x 2 + y 2 G = atan y x Entropy: H = G G plog p p = count( G Talbot, L. M. and Talbot, B. G. (2013. Fast- responder: Rapid mobile- phone access to recent remote sensing imagery for first responders. In Aerospace Conference, 2013 IEEE, pp IEEE. 11

12 Automated processing quickly turns data into information km2 984 images 27 minutes Desktop PC 12

13 Concept for Decision Support Using Automated Image Processing Imagery Electric UClity Data Backend The backend can be running anywhere, at mul;ple sites, removed from the affected area. Automated Damage Assessment Data Fusion and AnalyCcs OperaCons Center Field Crew User Interface Informa;on is delivered using exis;ng geospa;al visualiza;on applica;ons. Space- Time Insight Google Earth ESRI 13

14 Summary Overhead imagery can provide situational awareness for decision support. Automated processing increases the value of imagery by rapidly extracting information. Decision support systems need to be flexible, able to consume whatever data is available. 14

15 Next Steps Deploy the technology to electric utilities. Continue to develop new algorithms. Incorporate predictive analytics. Acknowledgements This work is funded by the Department of Homeland Security Science and Technology Directorate. Thanks to our collaborators: CenterPoint Energy Consolidated Edison MDA Geospatial Services Space-Time Insight 15

16 Thank you. Questions? ellow = flooded Tornado track Road blocked 16