PUBLICATIONS

ASSESSING ENVIRONMENTAL AND SOCIOECONOMIC FACTORS OF URBAN FLOOD VULNERABILITY IN KANSAS CITY, KANSAS

November 1, 2022

Pluvial flooding, over-saturated ground, and poor drainage systems disproportionately impact historically disinvested neighborhoods during extreme rainfall events independently of overflowing water bodies. These communities are impacted by physical and socioeconomic factors that make them vulnerable to flooding events, such as high concentration of impervious landcover, high precipitation rates, and a combined sewer system framework. Despite known vulnerability to environmental hazards, there is a lack of data supporting the potential pluvial street-level flooding events. The DEVELOP team investigated flooding events from June 2010 through June 2021 in Google Earth Engine (GEE) using NASA Earth observation products from the Global Precipitation Measure. Alongside the satellite imagery and ancillary datasets, the Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation model was utilized to generate outputs of runoff retention and potential economic damage for risk mapping of Kansas City, Kansas to aid in identifying areas where future intervention is necessary. Then a cloudburst blue spot model produced spatially-explicit outputs of how pluvial flooding would accumulate across the surface elevation gradients. These resulting maps identify the most vulnerable neighborhoods throughout Kansas City, alongside potential economic damage from flooding. The resulting methodology and end products provide partners from Groundwork USA and Groundwork Northeast Revitalization Group (Groundwork NRG) with a detailed analysis of urban flood risk throughout Wyandotte County, Kansas, and streamline the provision of neighborhood-scale vulnerability analysis to Groundwork USA’s Climate Safe Neighborhoods project.

ADVANCING DATA FOR STREET-LEVEL FLOOD VULNERABILITY: EVALUATION OF VARIABLES EXTRACTED FROM GOOGLE STREET VIEW IN QUITO, ECUADOR

April 12, 2022

Data relevant to flood vulnerability is minimal and infrequently collected, if at all, for much of the world. This makes it difficult to highlight areas for humanitarian aid, monitor changes, and support communities in need. It is time consuming and resource intensive to do an exhaustive study for multiple flood relevant vulnerability variables using a field survey. We use a mixed methods approach to develop a survey on variables of interest and utilize an open-source crowdsourcing technique to remotely collect data with a human-machine interface using high-resolution satellite images and Google Street View. Finally, we perform an inter-rater agreement to assess if this technique provides consistent results. This paper focuses on Quito, Ecuador as a case study, but the methodology can be replicated to produce labeled training data in other areas. The overall goal is to advance methods to help build training datasets that allow for assessing and automating the mapping of flood vulnerability for urban areas.

Velez, R. et al., "Advancing Data for Street-Level Flood Vulnerability: Evaluation of Variables Extracted from Google Street View in Quito, Ecuador," in IEEE Open Journal of the Computer Society, vol. 3, pp. 51-61, 2022, doi: 10.1109/OJCS.2022.3166887.

ADVANCING DATA FOR STREET-LEVEL FLOOD VULNERABILITY: EXTRACTION OF VARIABLES FROM GOOGLE STREET VIEW IN QUITO, ECUADOR

August 12, 2021

Data relevant to flood vulnerability is minimal and infrequently collected, if at all, for much of the world. This makes it difficult to highlight areas for humanitarian aid, monitor changes, and support communities in need. It would be time consuming and resource intensive to do an exhaustive study for multiple flood relevant vulnerability variables using a field survey. We use a mixed methods approach to develop a survey on variables of interest and utilize an open-source crowdsourcing technique to remotely collect data with a human-machine interface using high-resolution satellite images and Google Street View. This paper focuses on Quito, Ecuador as a case study, but the methodology can be quickly replicated to produce labelled training data in other areas. The overall project goal is to build training datasets that in the future will allow us to automate the mapping of flood vulnerability for urban areas in geographic regions.

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Velez, R. et al. "Advancing Data for Street-level Flood Vulnerability: Extraction of Variables from Google Street View in Quito, Ecuador." arXiv preprint arXiv:2108.05489 (2021).