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OPEN INNOVATION CHALLENGE SPRING 2022

Feb 22, 2022

We are excited to announce a new type of 'hackathon' we call an 'Open Innovation
Challenge,' where teams will compete to find the most efficient solution using
satellite imagery to detect invasive pests in LA County's tree canopy.

Individuals and unfunded teams can apply -- we are looking for
cross-disciplinary teams: computer scientists, data scientists, geospatial
scientists, entrepreneurs, designers, MLops, et al.

Teams will be provided pre-processed Landsat 8 data, GPU compute, and mentors to
help them design and develop a proof of concept GeoAI-enabled pest infestation
alert system in 4 weeks.

Important Dates
Info Session: March 8th at 5 pm PST Virtual
Kick-Off: March 18-20th in Los Angeles
Demo Day: April 21st in Los Angeles

Important Details
It's mandatory that at least 1 team member participate in the in-person kick-off
and demo day.
Individuals and unfunded teams can apply (ideally 3-5).
We are looking for cross-disciplinary teams: computer scientists, data
scientists, geospatial scientists, entrepreneurs, designers, MLops, et al.
Teams will present on stage at AI LA's Earth Summit on April 21st at the
BioscienceLA Collaboration Hub in Culver City.

‍
Register here. ‍

‍
Prizes‍
First: $10,000
Second: $2,500
Third: $2,500

The 3 winning teams will also receive office hours from Starburst Aerospace
Accelerator and free corporate legal services from Wilson Sonsini, and other
perks from our partners.

The judging criteria for this project reflect the need for hybrid design and
data skills:
Data Workflow (20%), Model Accuracy (20%), Technical Implementation (20%), UX
Design (20%), Alignment with Challenge (10%), and Presentation (10%).

More information on judging criteria and the competition will be announced
during our information session on March 8th at 5 pm PST.

Please fill out this form in order to receive the Zoom link for the info session
and be considered as a participant in this challenge.

Background:

Trees provide many important benefits to the residents of LA County. In addition
to beautifying our communities, they provide shade and cooling, support
biodiversity, help manage stormwater, reduce air pollution, and improve mental
health. Unfortunately, trees in the County face multiple threats. In addition to
the ongoing threats of drought stress and heat stress, trees in the County also
face the threat of invasive pests which have the potential to kill millions of
trees in Southern California—of particular concern are the invasive shot hole
borer (ISHB) and the golden spotted oak borer (GSOB). On May 18, 2021, the LA
County Board of Supervisors adopted a motion, authored by Supervisors Kuehl and
Solis, titled “Implementing an Early Detection Rapid Response Plan to Invasive
Pests.” The motion directed the Chief Sustainability Office (CSO) to work with
relevant County departments to develop a plan for early detection and rapid
response (EDRR) to these invasive pests. One part of this plan is to pilot a
remote sensing solution for early detection of invasive beetles.

The Opportunity:

County staff are racing against the clock to locate and control new infestations
before they spread to surrounding areas. Today, the County relies on physical
inspection, including surveying and trapping, to identify infestations of
invasive pests. This boots-on-the-ground process is time-consuming,
labor-intensive, and expensive, making it unrealistic for limited staff to
manually survey every tree in the County. The County needs a way to frequently
and comprehensively identify areas of potential new infestations Countywide so
limited resources can be deployed to conduct physical inspections at priority
locations. An automated remote sensing program using publicly available
satellite data would provide an important monitoring tool to meet this need and
help the County protect its vital tree resources.

‍Why Participate:

This challenge gives participants the ability to consider the relationship
between designing a targeted application and developing the data models utilized
by that application.

Through this project, participants will explore building data models around a
specific data challenge for the identification of trees with potential pest
infestations. To successfully complete this challenge, teams must consider: What
are we predicting and why? What is the end use for the data models we are
training towards specifically?

Through research and conversations with stakeholders, teams go through a process
of value discovery to identify applications that would be viable within the
context of the stakeholder’s needs. This process of refining the technical goal
must guide how teams utilize the data and identify relevant models.

Based on the identified end goal, participants may choose to utilize a
computational, machine learning workflow, or a remote sensing spatial workflow
to fulfill the data goals of this project. Workshops and available mentors will
guide participants through possible workflows, although a combination of spatial
and computational skills will be necessary to complete the final challenge.

This isn’t simply a data challenge. The goal of this program is to not only
enable participants to learn data skills relevant to this use case, but to also
give them the tools for R&D of product development with data.

‍


KEY DATES:

February 22nd: Applications Open
March 11th: Applications Close
March 18-20th: Design and Development Workshop
‍Friday Evening: Stakeholder Presentation
Friday Evening: Teambuilding Activities
Saturday Morning: Design Workshop
Saturday Afternoon: Computational Analysis Workflow
Saturday Afternoon: Remote Sensing Spatial Analysis Workflow
Saturday Evening: Work Sessions
Sunday Morning: Work Sessions
Sunday Afternoon: Teams present their Design Breakdowns and projected Data
Workflows to stakeholders for feedback
April 21st: Demo Day

‍


DESIGN

User interface for multiple jurisdictions

 * The data workflow and outputs for this challenge should take into
   consideration the way that the information will be implemented. If the
   prospective analysis is successful, the automated remote sensing process can
   be expanded to be used Countywide.
 * Take into consideration the time cycles that new data becomes available, as
   well as the frequency of on-the-ground monitoring in order to design a
   challenge-appropriate feedback system. In anticipation of a successful
   prospective analysis, design a user interface (e.g., monitoring dashboard)
   that can alert the County of new areas of potential infestation and notify
   the County of the jurisdiction of the potential infestation. The interface
   should include a way for the County to share information with affected
   jurisdictions in a user-friendly fashion (e.g., to identify the relevant
   jurisdiction and provide a push email or text message to the relevant
   contacts).

DATA

Retrospective analysis

 * Starting with imagery closest to the date of infestation, work backwards in
   time to determine when the infestation first becomes detectable in the
   satellite imagery. Consider that your findings may or may not differ between
   ISHB infestations and GSOB infestations. Once the early detection window has
   been identified, train and validate a ML methodology or create a spatial
   analysis workflow to prioritize emerging infestation sites.

Prospective analysis – Santa Monica Mountains Pilot

 * Create a process to automate the early detection of potential pest
   infestations using newly collected Landsat data. Apply your process to each
   newly acquired Landsat image, using the Santa Monica Mountains National
   Recreation Area as the pilot study boundary. Identify potential areas of new
   infestations within the boundary area for the County to ground truth with
   physical inspections. If your methodology and time allow, also provide
   potential areas of new infestation Countywide.

Proactive response (optional)

 * Today, the County’s response to invasive pests is reactive. To mount a
   proactive response to invasive pests, the County will need a better
   understanding of some of the habits and impacts of these pests, especially
   ISHB. Incorporate data gathering on the impacts of ISHB into your automated
   remote sensing program. Potential data points of interest include infested
   tree data such as tree species, tree size, tree canopy density, and tree
   health; infestation physical distribution data such as geography and ecology;
   infestation temporal distribution data such as meteorology and seasonality;
   and response effectiveness information such as the effect of infested tree
   removal on the spread of pests.
   


FAQS

 * What pre-processing will already be completed on the data?

The lat/long and time stamps of the ISHB and GSOB infestations are available as
pre-processed chips, with the data mapping to the Landsat time-series data.

 * Where will all of the pre-processed data be stored and how will it be
   regularly accessed by the teams?

During the March 18-20th workshop, we have a powerful data science workstation
that can be on-prem during the kick off weekend.

Workstation Specs: Dual Socket Xeon GOLD 8 Core, 196gb DDR4 Ram, 1TB NVME SSD
and Dual Nvidia Quadro RTX 8000

In the 4 weeks leading up to demo day, the data will have to be accessed
remotely.

 * What will project teams complete during the workshop weekend?

During the workshop weekend, participants will be offered a deeper dive into
challenge considerations, as well as potential project workflows. Teams will use
this information to present their proposed workflow and breakdown of design
requirements to stakeholders at the end of the weekend for feedback.

 * What are recommended competencies for project teams?

Required 

 * Image processing/feature extraction
 * Machine learning
 * Geospatial tools and methods
 * Statistical methods and analysis

Desired 

 * Knowledge of ecosystem analysis and forests
 * Understanding of local government needs and processes
 * Systems and user experience design

 * How will final projects be judged?

The judging criteria for this project reflect the need for hybrid design and
data skills. Judging criteria are: Data Workflow, Model Accuracy, Technical
Implementation, UX Design, Alignment with Challenge, and Presentation.

 * How can we get additional questions about the challenge answered?

For more information about this challenge, or if you have questions, please
email OIS@joinai.la with the subject line, “OIC Question.” We will also be
holding an information session with more information on March 8th.

ABOUT THE AUTHOR
Rachel Joy Victor
Head of Design, Open Innovation Studio

Rachel Joy Victor is a strategist and designer working with emerging
technologies. Her strategy work aligns brand, product, and content strategy,
centering user engagement and experience throughout. She designs systems
architectures and user experiences--from tools and platforms to spaces and
cities to narratives and immersive content. Rachel draws from degrees in
computational neuroscience and spatial economics and data analysis to align
emergent systems and human behavior in her work.



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