Things used in this project

Hardware components:
11868 00a
Raspberry Pi Camera module
×1
R8326274 01
Raspberry Pi 2 Model B
×1
Adafruit ADXL345 - Triple-Axis Accelerometer
×1
Adafruit Ultimate GPS Module
×1
Adafruit Prototyping Pi Plate Kit for Raspberry Pi
×1
Software apps and online services:
ImageMagick
Used to perform simple Image processing, programmatically.
Rm web services amazon s3
Amazon Web Services AWS S3
Images and telemetry data are streamed to an S3 bucket
73318 301258139977848 644841747 n
OpenCV
We use this for our automated approaches to detect cracks and other street defects
open street maps
Used for data publishing and cleaning.
OSMnx - Python for Street Networks
Geoff Boeing's creation allows us to quickly convert raw GPS data to actionable maps really quickly and in an open manner.
Hand tools and fabrication machines:
09507 01
Soldering iron (generic)
Makerbot Replicator 2

Custom parts and enclosures

Case Makerbot file
This is the layout for our 3d printed case
squid_case_v2.makerbot

Schematics

SQUID poster
Digitizing Municipal Street Inspections - 2016 Data for Good exchange
"People want an authority to tell them how to value things. But they chose this authority not based on facts or results. They chose it because it seems authoritative and familiar." - The Big Short

The pavement condition index is one such a familiar measure used by many US cities to measure street quality and justify billions of dollars spent every year on street repair. These billion-dollar decisions are based on evaluation criteria that are subjective and not representative. In this paper, we build upon our initial submission to D4GX 2015 that approaches this problem of information asymmetry in municipal decision-making.
We describe a process to identify street-defects using computer vision techniques on data collected using the Street Quality Identification Device (SQUID). A User Interface to host a large quantity of image data towards digitizing the street inspection process and enabling actionable intelligence for a core public service is also described. This approach of combining device, data and decision-making around street repair enables cities make targeted decisions about street repair and could lead to an anticipatory response which can result in significant cost savings. Lastly, we share lessons learnt from the deployment of SQUID in the city of Syracuse, NY.

Code

NYC BIG APPS Final Demo
bigapps_squid_demo.py shows how to overlay accelerometer readings on top of a video stream.

Credits

Varun profile pic
Varun Adibhatla

Varun works at ARGO Labs, a civic data science org. that rapidly prototypes for cities by partnering with local gov around device, data & decision making.

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