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Building a more just and beautiful future: CBE’s new faculty cohort makes strides on campus

The new cohort of faculty have made a big impact in their initial time on campus. Please see the full story here. The cohort includes: Dr. Narjes Abbasabadi, an assistant professor in the Department of Architecture and affiliate data science faculty UW eScience Institute, studies computation and decarbonization of the built environment. Dr. Amos Darko, an assistant professor in Construction Management, studies how digital technologies can help people better monitor, assess, understand, and improve the sustainability performance of the built…

CBE Research Restart Funding: Progress and Updates

The College of Built Environments awarded Research Restart funding to multiple project teams in 2022. Below are descriptions of their progress and project status to-date. July 2022 Cohort: Arthur Acolin received funding for their project entitled “Accessory Dwelling Units as Potential Source of Affordable Housing Across Generations.” A no-cost extension was approved in May 2023 due to delays in implementing the survey for the project. In July 2023, design of the survey instrument and postcards was completed, and next steps…

Faculty and Staff Recognized for Dedication and Service

The University of Washington College of Built Environments (CBE) recognized faculty and staff at the 2023 CBE Graduation Celebration. These awards celebrate CBE faculty and staff for their dedication, service, and many contributions to our community. Congratulations to all our awardees! Lionel Pries Award for Excellence in Teaching: Catherine De Almeida Outstanding Faculty Award: Jeffrey Ochsner Outstanding Part-Time Teaching Award: Marty Curry Distinguished Staff Award: Laura Barrera Read more here. 

Integration of Urban Science and Urban Climate Adaptation Research: Opportunities to Advance Climate Action

Lobo, J., Aggarwal, R. M., Alberti, M., Allen-Dumas, M., Bettencourt, L. M. A., Boone, C., Brelsford, C., Broto, V. C., Eakin, H., Bagchi-Sen, S., Meerow, S., D’Cruz, C., Revi, A., Roberts, D. C., Smith, M. E., York, A., Lin, T., Bai, X., Solecki, W., … Gauthier, N. (2023). Integration of urban science and urban climate adaptation research: opportunities to advance climate action. Npj Urban Sustainability, 3(1), 32–39. https://doi.org/10.1038/s42949-023-00113-0

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Abstract

There is a growing recognition that responding to climate change necessitates urban adaptation. We sketch a transdisciplinary research effort, arguing that actionable research on urban adaptation needs to recognize the nature of cities as social networks embedded in physical space. Given the pace, scale and socioeconomic outcomes of urbanization in the Global South, the specificities and history of its cities must be central to the study of how well-known agglomeration effects can facilitate adaptation. The proposed effort calls for the co-creation of knowledge involving scientists and stakeholders, especially those historically excluded from the design and implementation of urban development policies.

Detecting Subpixel Human Settlements in Mountains Using Deep Learning: A Case of the Hindu Kush Himalaya 1990–2020

Chen, T.-H. K., Pandey, B., & Seto, K. C. (2023). Detecting subpixel human settlements in mountains using deep learning: A case of the Hindu Kush Himalaya 1990–2020. Remote Sensing of Environment, 294, 113625–. https://doi.org/10.1016/j.rse.2023.113625

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Abstract

The majority of future population growth in mountains will occur in small- and medium-sized cities and towns and affect vulnerable ecosystems. However, mountain settlements are often omitted from global land cover analyses due to the low spatial resolution of satellite images, which cannot resolve the small scale of mountains settlements. This study demonstrates, for the first time, the potential of deep learning to detect human settlements in mountains at the sub-pixel level, based on Landsat satellite imagery. We hypothesized that adding spatial and temporal features could improve the detection of mountain settlements since spectral information alone led to inaccurate results. For spatial features, we compared a U-shaped neural network (U-Net), a deep learning algorithm that automatically learns spatial features, with a simple random forest (RF) algorithm. Then, we assessed whether temporal features would increase accuracy by comparing two input datasets, multispectral imagery and temporal features from the Continuous Change Detection and Classification (CCDC) algorithm. We evaluated each method by calculating the accuracies of (1) the binary settlement footprint, (2) the subpixel estimates of impervious surfaces, and (3) urban growth. We tested the accuracies using visually interpreted datasets from time-series Google Earth images across the Hindu Kush Himalaya that were not used for training to evaluate model transferability. The U-Net successfully improved mountain settlement mapping compared to the random forest, with a substantial discrepancy in small settlements. The time-series results from the U-Net successfully captured long-term urban growth but fewer short-term changes. Contrary to expectations, the CCDC temporal features reduced the accuracy of mountain settlement mapping due to frequent cloud cover in hilly areas. Our subpixel analysis reveals that the built-up area of the Hindu Kush Himalaya has expanded at a rate of 61 km2 per year from 1990 to 2020, which is about twice the estimate of the Global Human Settlement Layer using binary urban/non-urban classifications.

Keywords

Urban land cover; Land cover fraction; Peri-urban; Built-up area; Subpixel mapping; Machine learning; Time-series; Himalaya; CCDC

Urban Infrastructure Lab Report on High-Speed Rail

The Urban Infrastructure Lab researchers have released a report on a Cascadia region high-speed rail project. College of Built Environments faculty Jan Whittington and Qing Shen were authors on the report, along with 3 Interdisciplinary Ph.D. in Urban Design and Planning students (Siman Ning, Haoyu Yue, and Chin-Wei Chen), and a Master of Urban Planning candidate (Richard McMichael). This report examines the successes and lessons learned from existing high-speed rail projects in Europe and Asia, including 50 hours of interviews…

Higher Depression Risks in Medium- Than in High-Density Urban Form Across Denmark

Chen, T.-H. K., Horsdal, H. T., Samuelsson, K., Closter, A. M., Davies, M., Barthel, S., Pedersen, C. B., Prishchepov, A. V., & Sabel, C. E. (2023). Higher depression risks in medium- than in high-density urban form across Denmark. Science Advances, 9(21), eadf3760–eadf3760. https://doi.org/10.1126/sciadv.adf3760

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Abstract

Urban areas are associated with higher depression risks than rural areas. However, less is known about how different types of urban environments relate to depression risk. Here, we use satellite imagery and machine learning to quantify three-dimensional (3D) urban form (i.e., building density and height) over time. Combining satellite-derived urban form data and individual-level residential addresses, health, and socioeconomic registers, we conduct a case-control study (n = 75,650 cases and 756,500 controls) to examine the association between 3D urban form and depression in the Danish population. We find that living in dense inner-city areas did not carry the highest depression risks. Rather, after adjusting for socioeconomic factors, the highest risk was among sprawling suburbs, and the lowest was among multistory buildings with open space in the vicinity. The finding suggests that spatial land-use planning should prioritize securing access to open space in densely built areas to mitigate depression risks.

Tzu-Hsin Karen Chen

UW Department of Urban Design and Planning in the College of Built Environments.

Urban Design & Planning student selected for 2023 applied research fellow cohort

The Population Health Initiative announced the summer 2023 cohort of applied research fellows. Among the group of 5 students (3 graduate and 2 undergraduate) is Pamela Lim from the College of Built Environments Urban Design & Planning department. These students will spend 10 weeks over the summer working collaboratively with King County’s demographer and Public Health – Seattle & King County’s Assessment, Policy Development and Evaluation Unit with the support of the Population Health Applied Research Fellowship. The team will…