Deep Learning techniques are being used to address challenges in Smart Cities. Though many use cases have been solved independently, stitching these solutions together using a flexible, scalable and efficient architecture is difficult. We developed a platform for Smart Cities. Here are some technical papers and solutions that inspired us in the process. The youtube video could be found at https://youtu.be/LFB83yrbqEc . This is a presentation I shared in Global BigData Conference in Apr2018 (http://www.globalbigdataconference.com/santa-clara/global-data-science-conference-98/speaker-details/sk-reddy-62567.html).

2. Practical AIsolutions
for
Smart Cities
by SK Reddy
Chief Product Officer AI
linkedin.com/in/sk-reddy/

3. New Orleans mapped the combined risk of missing smoke alarms and fire
deaths
https://www.wsj.com/articles/the-rise-of-the-smart-city-1492395120

4. 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/the-rise-of-the-smart-city-1492395120

5. Sensor-equipped asthma inhalers in Louisville
https://www.wsj.com/articles/the-rise-of-the-smart-city-1492395120

6. Smart City Use Cases
• Parking availability
• How long a bus will take?
• What is the right length for a stop light at every given minute
• Real time data about traffic and accidents
• Check if vehicles parked there have the right permits
• License plate recognition
• Detecting human action using videos
• Preventive maintenance of the road infrastructure
• Buses and trains communicate with each other and the general public
• Face recognition
• Find stolen cars
• Locate gunfire based on a sensor network
• Localized warnings about possible natural disaster
• Count vehicles and pedestrians
• Recognize faces
• Track the speed and movements of vehicles
• Count cars in a parking lot or track road use
• Emergency dispatch
• Ambulance call
https://www.techemergence.com/smart-city-artificial-intelligence-applications-trends/
Data
• Volume
• Variety
• Velocity
• Veracity
• Volatility

7. Online data-processing architecture
https://arxiv.org/pdf/1709.01363.pdf
Main components of a Storm cluster
Apache Flink’s execution model

8. Text processing Framework
https://arxiv.org/pdf/1709.03406.pdf
• Lowercasing
• Lemmatization
• Tokenization
• Transforming repeated characters: (e.g. "loooool" to "loool”)
• Punctuation removal
• Cleaning Entities and Numerical Symbols
• Removing URLs, user mentions, hashtags and digits from the
text messages
• Stop and short words removal
• Geographic Distributions
• Temporal Frequencies
• volume of tweets posted per hour, per day, as well as the
activity by day-of-the-week or hour-of-the-day
• Content Composition
• metadata (hashtags, user mentions, URLs and media
attached) exploration for added-value information

9. Data streaming
Infrastructure architecture
https://arxiv.org/pdf/1703.02755.pdf

10. https://arxiv.org/pdf/1712.01432.pdf
Large-scale video management

11. https://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-9d5a029a596b
YOLO2

12. 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

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

14. 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

15. Thank you
SK Reddy
www.linkedin.com/in/sk-reddy/