Automatic Damage Annotation on Post-Hurricane Satellite Imagery

A graphic depicting satellite imagery of flooding after Hurricane Harvey

The red bounding box at the center contains a flooded building after Hurricane Harvey. Image credit: DigitalGlobe.

Project lead: Youngjun Choe, Ph.D., Assistant Professor, University of Washington, Industrial & Systems Engineering

Data scientist lead: Valentina Staneva

DSSG fellows: Sean Andrew Chen, Andrew Escay, Christopher Haberland, Tessa Schneider, An Yan

Project Summary: When a hurricane makes landfall, situational awareness is one of the most critical needs emergency managers face before they can respond to the event. To assess the situation and damage, the current practice largely relies on driving around the impacted area (also known as a windshield survey) by emergency response crews and volunteers. Recently, drone-based aerial images and satellite images have helped improve situational awareness, but the process still relies on human visual inspection. These current approaches are generally time-consuming and/or unreliable during an evolving disaster.

The governing research question of the project is: Can a machine learning algorithm automatically annotate damage on post-hurricane satellite images? To answer the question, the project uses satellite imagery data on the Greater Houston area before and after Hurricane Harvey in 2017, and damage labels created by crowdsourcing. If the project results in a successful algorithm (which is trained to quickly detect ‘Flooded / Damaged Building’, ‘Flooded / Blocked Road’, and ‘Blocked Bridge’ on a satellite image for a new event), it will be an exciting technological innovation to improve situational awareness during the first response to hurricane-induced disasters.

Seattle Mobility Index Project

Graphic depicting the Seattle Mobility Index Project

The Seattle Mobility Index Project

Project leads: Stephen Barham, Data Scientist, and Alex Hagenah, Data Librarian, Seattle Department of Transportation

Data scientist leads: Joseph Hellerstein and Ryan Maas

DSSG fellows: Rebeca de Buen Kalman, Darius Irani, Hyeon Jeong Kim, Amandalynne Paullada, Woosub Shin

Project Summary: The Seattle Mobility Index Project measures transportation mode choice, affordability, and reliability at 450 Census Block Groups in Seattle and predicts mode share (the percentage of travelers using each transportation option) based on their mobility indices. The project represents a low-cost, granular approach to measuring and communicating mobility that can be replicated anywhere, similar to Redfin’s “Walk Score” and “Transit Score”, which measure walkability and transit options in proximity to any location. The Seattle Mobility Indices, however, are based on the ability to reach a “market basket” of destinations, or common travel points, derived from actual travel patterns, not solely based on locations nearby. Our indices vary with time of day and are sensitive to near- and long-term changes in the transportation system.

Using the Google Distance Matrix API, we will consume millions of distance and travel time estimates for driving, transit, walking, and bike travel. We will also access aggregated travel pattern information from the Puget Sound Regional Council Household Travel survey (see links below) to validate and tune our approach. We expect to complete the project in three distinct steps:

  1. Market Basket of Destinations. We will refine an algorithm that identifies a “market basket” of destinations relevant to people who travel in Seattle. The basket may include collections of trips to nearby points of interest and activity centers that are specific to each origin, and a collection of trips to citywide destinations that are the same for all starting points. The basket algorithm is a low-cost approach to creating a transportation origin-destination model.
  2. Mobility Indices. We will analyze travel from each Census Block Group to the Block Group’s basket of destinations and develop scalable algorithms that return the following indices:
    • Mode Choice: the quantity of modes available to reach the basket of travel destinations, within designed parameters. 
    • Affordability: the relative cost to reach the basket of travel destinations, based on the costs of the least expensive modes and the costs of the fastest modes.
    • Reliability: measurements of actual travel times versus optimal times and the amount of travel that exceeds percentile thresholds. Travel time reliability algorithms will be applied to data that has been collected over a period of time.
  3. Mode Share Predictions. We will attempt to model and predict the probability that a traveler will use a single occupancy vehicle and other modes given the Mode Choice, Affordability, and Reliability scores for their location.

Seattle is entering an expanded era of intense public and private construction projects that transportation planners have called the “Period of Maximum Constraint.” For the next 5 to 10 years, measuring the ability to drive, walk, bike, and use transit will be critical to mitigating the impacts. This research is particularly important to the City’s race and social justice equity programs because it will enable us to identify where geographic and time-of-day disparities in mobility exist and quantify how they are impacted by changes in the transportation system.

The mobility indices are a key component of the Seattle Department of Transportation’s Strategic Data Initiative and performance metrics that enable the City to drive outcomes, make decisions, and move our work from being project driven to outcome driven. The indicators will be baselined, tracked, and used to communicate the status and health of the transportation system.

Related Links

Sample Distance Matrix Data Collection for a Matrix of 13 Points in Seattle

Google Distance Matrix API (Primary data source)

Puget Sound Regional Council Household Travel Survey (Secondary data source)

“The Period of Maximum Constraint” – links one and two

Access to Out-of-School Opportunities and Student Outcomes

Project lead: Sivan Tuchman, Research Analyst, University of Washington, Bothell, Center on Reinventing Public Education

Data scientist leads: Jose Hernandez and Karen Lavi

DSSG fellows: Joe Abbate, Sreekanth Krishnaiah, Kellie MacPhee, Andrew Taylor, Haowen Zheng

Graphic depicting the Blueprint 4 Summer program for studetnts

Blueprint 4 Summer offers families a way to find summer programs for students.

Project Summary: For students living in disadvantaged communities, accessing organizations or institutions that provide enrichment programs for the arts, sports, and tutoring, or social services such as counseling, meals, or medical care can be challenging. And while we know that experiences outside of the school day can be highly enriching to student academic and non-academic learning, they remain elusive to the students who need them the most. Financial, time, accessibility, and safety constraints can all limit the feasibility of a student going from school or home to an enrichment program or service provider. There are potential policy solutions that may be able to increase access for disadvantaged students to engage in these out-of-school opportunities, but we need to better understand what the highest impact lever might be.

The Center on Reinventing Public Education is currently working with ReSchool Colorado, a local organization that is trying to reimagine education that is curated around individual student needs. To do this, ReSchool works to help families design a multi-faceted education that enriches and supports individual students, which includes wraparound and community-based services. They utilize learner advocates, who help families navigate educational options, transportation, and other resources they may need or want. Our goal is to engage in an iterative process with ReSchool and our DSSG team to inform their work around summer opportunities through the “Blueprint 4 Summer” initiative, as well as their year-round support services to families, so they can curate personalized education for their students.

To begin this work, we would like to explore the following questions:

  1. What is the relationship between access to out-of-school opportunities and student outcomes (academic, behavioral, other)?
  2. How does crime moderate this relationship?
  3. What is the variation in these relationships by student subgroups?

Our data from Denver Public Schools includes enrollment data, grade, gender, race/ethnicity, disability and English learner status for every K-12 student in the years 2011-’12 to 2017-’18.  Outcomes of standardized tests (including end-of-course exams), along with data on discipline (in-school suspension, out-of-school suspension, and expulsion), graduation, and attendance are available for 2011-’12 through 2016-’17. These data will make it possible to do various subgroup analyses. Crime and out-of-school opportunities from Denver’s Open Data Catalog, as well as ReSchool’s Blueprint 4 Summer catalog, will give our DSSG team significant data to work with so they can inform the work that ReSchool and others in the City of Denver are doing to improve the educational opportunities available to students.