Sabine Loos

Sabine Loos
United States Geological Survey | USGS

Doctor of Engineering

About

8
Publications
4,332
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
32
Citations
Additional affiliations
September 2021 - present
United States Geological Survey
Position
  • PostDoc Position
September 2016 - September 2021
Stanford University
Position
  • PhD Student
Description
  • Studying regional impacts of earthquakes as part of the Stanford Urban Resilience Initiative
June 2014 - September 2017
The Ohio State University
Position
  • Research Assistant

Publications

Publications (8)
Article
Full-text available
Weeks after a disaster, crucial response and recovery decisions require information on the locations and scale of building damage. Geostatistical data integration methods estimate post-disaster damage by calibrating engineering forecasts or remote sensing-derived proxies with limited field measurements. These methods are meant to adapt to building...
Article
Full-text available
This is a contributing paper to the UN Office for Disaster Risk Reduction Global Assessment Report 2022. Also downloadable at: https://www.undrr.org/publication/shedding-light-avoided-disasters-measuring-invisible-benefits-disaster-risk-reduction ------------------ The goal of Disaster Risk Management (DRM) is to ensure that society continues to f...
Article
After an earthquake, many responding organizations need to understand the scale and distribution of building damage to react effectively. However, their building damage information needs and information use remain poorly understood, limiting the efficacy of information production, sharing, and research. To clarify those needs, we conducted a two-pa...
Preprint
Full-text available
Following a disaster, crucial decisions about recovery resources often focus on immediate impact, partly due to a lack of detailed information on who will struggle to recover. Here we perform an analysis of surveyed data on reconstruction and secondary data commonly available after a disaster to estimate a metric of non-recovery or the probability...
Article
Full-text available
While unprecedented amounts of building damage data are now produced after earthquakes, stakeholders do not have a systematic method to synthesize and evaluate damage information, thus leaving many datasets unused. We propose a Geospa-tial Data Integration Framework (G-DIF) that employs regression kriging to combine a sparse sample of accurate fiel...
Technical Report
Full-text available
Disasters continue to present tremendous obstacles to sustained development progress and the wellbeing of communities around the world. Key to mitigating the long-term impacts of disasters is the ability to rapidly respond and recover in ways that build resilience and protect hard-fought development gains. However, the information systems needed to...
Article
Full-text available
As coastal infrastructure systems are continuously exposed to deterioration, it is increasingly crucial to analyse their current and future serviceability performance. This paper investigates effects of chloride corrosion on the lateral force capacity and ductility of a wharf-supporting prestressed concrete marine pile and provides new insights int...
Article
Full-text available
In the wake of large earthquake disasters, governments, international agencies, and large nongovernmental organizations scramble to conduct impact and damage assessments that help them understand the nature and scale of the emergency in order to orchestrate a complex series of emergency, response, and recovery activities. Using the Gorkha earthquak...

Network

Cited By

Projects

Project (1)
Project
Informatics for Equitable Recovery is a transdisciplinary research collaboration that brings together data scientists, engineers, social scientists and civic organizations to improve post-disaster information systems and decision support tools. Example use cases for these information systems include post-disaster needs assessments (PDNAs) and recovery aid policy design. After the 2015 Gorkha earthquake in Nepal, the need for holistic post-disaster information systems became even clearer. We developed novel methods combining spatial statistics, field surveys, social science, and machine learning models to develop rapid damage and need estimates that reflect the different ways that disaster impacts are felt by communities and enable these estimates to be used meaningfully in the recovery process.