AI is helping smart cities in many ways such as: 1. Analyzing video data to detect human actions, traffic flow, and count cars in parking lots. 2. Using deep learning models and sensor data to predict taxi demand and optimize issues like parking availability, public transportation times, and traffic light timing. 3. Large-scale mapping applications using geo-tagged videos to map events like parades and analyze spatial data.

1. How$AI$is$helping
Smart$Cities
SK#Reddy
Chief#Product#Officer#AI
skreddy99
skreddy99

2. Confidential2
11"variables,"including"
whether"the"restaurant"had"
previous"violations,"how"long"
it"has"been"in"business"(the"
longer,"the"better),"the"
weather"(violations"are"more"
likely"when"it’s"hot),"even"
stats"about"nearby"burglaries"
(which"tells"something"about"
the"neighborhood,"though"
analysts"aren’t"sure"what)
How$to$prioritize$restaurant$inspections
https://www.wsj.com/articles/the7rise7of7the7smart7city71492395120

3. Confidential3
https://blogs.worldbank.org/opendata/artificial8intelligence8smart8cities8insights8ho8chi8minh8city8s8spatial8development
Land%Use%Classification%from%Satellites%in%Ho%Chi%Minh%City

4. Confidential4
Los$Angeles’s$Clean$Streets$program
https://www.wsj.com/articles/the7rise7of7the7smart7city71492395120

5. Confidential5
Listens'to'gun'shot'sounds…..

6. Confidential6
New$Orleans$mapped$the$combined$risk$of$missing$smoke$alarms$and$fire$deaths
https://www.wsj.com/articles/the7rise7of7the7smart7city71492395120

7. Confidential7
AR#experience

8. Confidential8
Smart&City&Use&Cases
• Parking1availability1
• How1long1a1bus1will1take?
• What1is1the right1length1for1a1stop1light at1every1given1minute
• Real&time&data&about&traffic&and&accidents
• Check1if1vehicles1parked1there1have1the1right1permits
• License1plate1recognition
• Detecting&human&action&using&videos
• Preventive1maintenance1of1the1road1infrastructure
• Buses1and1trains1communicate1with1each1other1and1the1general1public
• Face&recognition
• Find1stolen1cars
• Locate1gunfire1based1on1a1sensor1network
• Localized1warnings1about1possible1natural1disaster
• Count&vehicles and1pedestrians1
• Recognize&faces
• Track1the1speed1and1movements1of1vehicles1
• Count&cars&in&a&parking&lot&or1track1road1use
• Emergency1dispatch1
• Ambulance1call1
https://www.techemergence.com/smartIcityIartificialIintelligenceIapplicationsItrends/
Data
• Volume:1terabytes1(TB),1petabytes1
(PB),1zettabytes1(ZB)
• Variety:1types1of1data1(sensors,1
devices,1social1networks,1the1web,1
mobile1phones,1etc.)
• Velocity:1how1frequently1the1data1is1
generated1(every1millisecond,1
second,1minute,1hour,1day,1week,1
month,1or1year

9. Confidential9
Online&data*processing&architecture
https://arxiv.org/pdf/1709.01363.pdf
Main<components<of<a<Storm<cluster
Apache<Flink’s execution<model

10. Confidential10
Text%processing%Framework
https://arxiv.org/pdf/1709.03406.pdf
• Lowercasing
• Lemmatization
• Tokenization
• TransformingDrepeatedDcharacters:D(e.g.D"loooool"DtoD
"loool”)
• PunctuationDremoval
• CleaningDEntitiesDandDNumericalDSymbols
• RemovingDURLs,DuserDmentions,DhashtagsDandDdigitsDfromD
theDtextDmessages
• StopDandDshortDwordsDremoval
• GeographicDDistributionsD
• TemporalDFrequencies
• volumeDofDtweetsDpostedDperDhour,DperDday,DasDwellD
asDtheDactivityDbyDdayWofWtheWweekDorDhourWofWtheWdayD
• ContentDComposition
• metadataD(hashtags,DuserDmentions,DURLsDandD
mediaDattached)DexplorationDforDaddedWvalueD
information

11. Confidential11
Data-streaming-
Infrastructure+architecture
https://arxiv.org/pdf/1703.02755.pdf

12. Confidential12https://arxiv.org/pdf/1712.01432.pdf
Large&scale*video*management

13. Confidential13https://arxiv.org/pdf/1506.02640v5.pdf
Count&cars&in&a&parking&lot&
https://medium.com/the?downlinq/car?localization?and?counting?with?overhead?imagery?an?interactive?exploration?
YOLO2

14. Confidential14
Traffic'flow'detection
https://arxiv.org/pdf/1801.03317.pdf
Example=ray=tracing=scenario=illustrating=the=shadowing=
effects=caused=by=a=SUV=passing=the=proposed=
classification=system

15. Confidential15
(a).0The0spatial0component0uses0a0
local0CNN0to0capture0spatial0
dependency0among0nearby0
regions.0The0local0CNN0includes0
several0convolutional0layers.0A0fully0
connected0layer0is0used0at0the0end0
to0get0a0low0dimensional0
representation.0
(b).0The0temporal0view0employs0a0
LSTM0model,0which0takes0the0
representations0from0the0spatial0
view0and0concatenates0them0with0
context0features0at0corresponding0
times.0
(c).0The0semantic0view0first0
constructs0a0weighted0graph0of0
regions0(with0weights0representing0
functional0similarity)
Deep$Multi*View$Spatial*Temporal$Network$for$
Taxi$Demand$Prediction
https://arxiv.org/pdf/1802.08714.pdf

16. Confidential16
http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Learning_to_Detect_CVPR_2017_paper.pdf
Learning(to(Detect(Salient(Objects(
with(Image7level(Supervision

17. Confidential17
http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Beyond_Triplet_Loss_CVPR_2017_paper.pdf
Deep$quadruplet$network$for$
Person$re3identification

18. Confidential18
https://arxiv.org/pdf/1704.08063.pdf
Comparison*of*open-set*and*closed-set*face*recognition
SphereFace:<
Deep<Hypersphere<Embedding<for<Face<Recognition

19. Confidential19
https://arxiv.org/pdf/1704.00389.pdf
Analyzing)videos)of)
human)actions
TOP:?MOTIONNET.?BOTTOM:?TRADITIONAL?TEMPORAL?STREAM.?M?IS?THE?
NUMBER?OF?ACTION?CATEGORIES.?STR:?STRIDE.?CH?I/O:?NUMBER?OF?
CHANNELS?OF?INPUT/OUTPUT?FEATURES.?IN/OUT?RES:?INPUT/OUTPUT?
RESOLUTION

20. Confidential20https://arxiv.org/pdf/1706.07911.pdf
Large&Scale*Mapping*using*Geo&Tagged*Videos
Training<MotionNet to<learn<optimal<optical<flow<involves<minimizing<the<following<three<objective<
functions:
1. A<standard<pixelGwise<reconstruction<error<
function
2. A<smoothness<loss<to<address<the<ambiguity<of<
estimating<motion<in<nonGtextured<regions<(the<
aperture<problem)
3. A<structural<similarity<(SSIM)<loss<that<helps<
MotionNet learn<the<structures<of<frames.<It<is<
calculated<as
The<overall<loss<is<a<weighted<sum<of<the<pixelGwise<
reconstruction<loss,<the<pixelGwise<smoothness<loss<and<
the<regionGbased<SSIM<loss
Spatial*analysis*of*the*46th*San*Francisco*Pride*parade*in*2016.*
Left:*official*parade*route.*Right:*m ap*of*classified*videos.*Note*the*
correlation

21. Confidential21
Thank&you
SK&Reddy
Chief&Product&Officer&AI
skreddy99
skreddy99