Matias Alberto Quintana Rosales

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Quintana Rosales

First Name

Matias Alberto

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0000-0002-0486-221X

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Publications 1 - 10 of 11

From Personal Comfort to District Performance: Using Smartwatch and WiFi Data for Occupant-Driven Operation
Mosteiro-Romero, Martín; Quintana Rosales, Matias Alberto; Miller, Clayton; et al. (2023)
BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
This work proposes the use of a data-driven, agent-based model of building occupants’ activities and thermal comfort in an urban university campus in order to assess how district operation strategies can be leveraged to support the transition to flexible work arrangements. The results show that when users are given the flexibility to pursue more comfortable workspaces, they are still comfortable only 58% of the time.
Introducing the Cool, Quiet City Competition: Predicting Smartwatch-Reported Heat and Noise with Digital Twin Metrics
Miller, Clayton; Quintana Rosales, Matias Alberto; Frei, Mario; et al. (2023)
BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
The productivity and satisfaction of humans in the built environment is impacted significantly by their exposure to high temperature and various noise sources. This paper outlines the city-scale collection of 12,009 smartwatch-driven micro-survey responses that were collected alongside 2,825,243 physiological and environmental measurements from 106 people using the open-source Cozie-Apple platform combined with geolocation-driven urban digital twin metrics from the Urbanity Python package. This paper introduces a machine learning competition that will be launched for participants to compete in training models on the various contextual data to predict noise distraction and source as well as thermal preference across a diversity of spaces. The winning solutions of this competition will provide evidence of the types of pre-processing, modeling, and ensembling methods that provide the most accurate solutions for this context.
Leveraging campus-scale Wi-Fi data for activity-based occupant modeling in urban energy applications
Mosteiro-Romero, Martín; Miller, Clayton; Quintana Rosales, Matias Alberto; et al. (2023)
Journal of Physics: Conference Series ~ CISBAT International Conference 2023: Controls & Occupant Behaviour
The widespread availability of open datasets in urban areas is transforming how urban energy systems are planned, simulated, and visualized. Urban energy models, however, require an understanding of urban dwellers, as their activities create the demands for energy in buildings. In this paper, we explore using campus-scale Wi-Fi data to identify typical occupant activity patterns as an input to an agent-based model of building occupants at the district scale. The data is taken from a Singaporean university's Wi-Fi network at high resolution. Each record comprises a timestamp, a device identifier, the location of the device within the campus, and the access point to which it is connected. The Wi-Fi dataset contains 120 different buildings on campus and 10,300 anonymized individual devices. Activities are then assigned to each location on campus according to the building use type. In order to test the methodology, the activity plans of 27,604 undergraduate students, 8,304 graduate students, and 12,018 employees were simulated over a workweek. The results show the model's ability to produce plausible activity plans but could be improved by implementing sampling rules and expanding the source dataset to include off-peak dates. Nevertheless, using such an agent-based modeling approach at the district scale appears to be a promising methodology to assess the impacts of different planning strategies on occupant behavior and district energy demand.
Global Streetscapes - A comprehensive dataset of 10 million street-level images across 688 cities for urban science and analytics
Hou, Yujun; Quintana Rosales, Matias Alberto; Khomiakov, Maxim; et al. (2024)
ISPRS Journal of Photogrammetry and Remote Sensing
Street view imagery (SVI) is instrumental for sensing urban environments, benefitting numerous domains such as urban morphology, health, greenery, and accessibility. Billions of images worldwide have been made available by commercial services such as Google Street View and crowdsourcing services such as Mapillary and KartaView where anyone from anywhere can upload imagery while moving. However, while the data tend to be plentiful, have high coverage and quality, and are used to derive rich insights, they remain simple and limited in metadata as characteristics such as weather, quality, and lighting conditions remain unknown, making it difficult to evaluate the suitability of the images for specific analyses. We introduce Global Streetscapes — a dataset of 10 million crowdsourced and free-to-use SVIs sampled from 688 cities across 210 countries and territories, enriched with more than 300 camera, geographical, temporal, contextual, semantic, and perceptual attributes. The cities included are well balanced and diverse, and are home to about 10% of the world’s population. Deep learning models are trained on a subset of manually labelled images for eight visual-contextual attributes pertaining to the usability of SVI — panoramic status, lighting condition, view direction, weather, platform, quality, presence of glare and reflections, achieving accuracy ranging from 68.3% to 99.9%, and used to automatically label the entire dataset. Thanks to its scale and pre-computed standard semantic information, the data can be readily used to benefit existing use cases and to unlock new applications, including multi-city comparative studies and longitudinal analyses, as affirmed by a couple of use cases in the paper. Moreover, the automated processes and open-source code facilitate the expansion and updates of the dataset and encourage users to create their own datasets. With the rich manual annotations, some of which are provided for the first time, and diverse conditions present in the images, the dataset also facilitates assessing the heterogeneous properties of crowdsourced SVIs and provides a benchmark for evaluating future computer vision models. We make the Global Streetscapes dataset and the code to reproduce and use it publicly available in https://github.com/ualsg/global-streetscapes .
My street is better than your street: Towards data-driven urban planning with visual perception
Quintana Rosales, Matias Alberto; Gu, Youlong; Biljecki, Filip (2024)
BuildSys '24: Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
Translating street view imagery to correct perspectives to enhance bikeability and walkability studies
Ito, Koichi; Quintana Rosales, Matias Alberto; Han, Xianjing; et al. (2024)
International Journal of Geographic Information Science
Street view imagery (SVI), an emerging geospatial dataset, is useful for evaluating active transportation infrastructure, but it faces potential biases from its vehicle-based capture method, diverging from pedestrians’ and cyclists’ perspectives. Existing literature lacks both an examination of these biases and a solution. This study identifies and quantifies these biases by comparing conventional SVI with views from the road shoulder/sidewalk. To mitigate such perspective biases, we introduce a novel framework with generative adversarial network (GAN)-based image generation models (Pix2Pix and CycleGAN), an image regression model (ResNet-50), and a tabular model (LightGBM). Experiments assessed model effectiveness in translating car-centric views to those from pedestrian and cyclist perspectives. Results show significant differences in semantic indicators (e.g. green view index) between road center and road shoulder/sidewalk SVI, with low Pearson’s correlation coefficients r (0.35–0.55 for road shoulders and 0.45–0.47 for sidewalks) indicating bias. The framework succeeded in creating realistic images and aligning pixel ratios between perspectives, achieving strong correlation coefficients (0.81 for road shoulders and 0.83 for sidewalks), thus reducing bias. This work contributes by providing a scalable and model-agnostic approach to produce accurate SVIs for urban planning and sustainability, setting a foundation for improving bikeability and walkability assessments and promoting active transportation.
Make yourself comfortable: Nudging urban heat and noise mitigation with smartwatch-based Just-in-time Adaptive Interventions (JITAI)
Miller, Clayton; Chua, Yun Xuan; Quintana Rosales, Matias Alberto; et al. (2025)
Building and Environment
Humans can play a more active role in improving their comfort in the built environment if given the right information at the right place and time. This paper outlines the use of Just-in-Time Adaptive Interventions (JITAI) implemented in the context of the built environment to provide information that helps humans minimize the impact of heat and noise on their daily lives. This framework is based on the open-source Cozie iOS smartwatch platform. It includes data collection through micro-surveys and intervention messages triggered by environmental, contextual, and personal history conditions. An eight-month deployment of the method was completed in Singapore with 103 participants who submitted more than 12,000 micro-surveys and had more than 3,600 JITAI intervention messages delivered to them. A weekly survey conducted during two deployment phases revealed an overall increase in perceived usefulness ranging from 8%–19% over the first three weeks of data collection. For noise-related interventions, participants showed an overall increase in location changes ranging from 4%–11% and a 2%–17% increase in earphone use to mitigate noise distractions. For thermal comfort-related interventions, participants demonstrated a 3%–13% increase in adjustments to their location or thermostat to feel more comfortable. The analysis found evidence that personality traits (such as conscientiousness), gender, and environmental preferences could be factors in determining the perceived helpfulness of JITAIs and influencing behavior change. These findings underscore the importance of tailoring intervention strategies to individual traits and environmental conditions, setting the stage for future research to refine the delivery, timing, and content of intervention messages.
Global urban visual perception varies across demographics and personalities
Quintana Rosales, Matias Alberto; Gu, Youlong; Liang, Xiucheng; et al. (2025)
Nature Cities
Understanding people’s preferences is crucial for urban planning, yet current approaches often combine responses from multi-cultural populations, obscuring demographic differences and risking amplifying biases. We conducted a large-scale urban visual perception survey of streetscapes worldwide using street view imagery, examining how demographics—including gender, age, income, education, race and ethnicity, and personality traits—shape perceptions among 1,000 participants with balanced demographics from five countries and 45 nationalities. This dataset, Street Perception Evaluation Considering Socioeconomics, reveals demographic- and personality-based differences across six traditional indicators—safe, lively, wealthy, beautiful, boring, depressing—and four new ones: live nearby, walk, cycle, green. Location-based sentiments further shape these preferences. Machine-learning models trained on existing global datasets tend to overestimate positive indicators and underestimate negative ones compared to human responses, underscoring the need for local context. Our study aspires to rectify the myopic treatment of street perception, which rarely considers demographics or personality traits.
What is a Digital Twin Anyway? Deriving the Definition for the Built Environment from over 15,000 Scientific Publications
Abdelrahman, Mahmoud; Macatulad, Edgardo; Lei, Binyu; et al. (2025)
Building and Environment
The concept of Digital Twins (DT) has attracted significant attention across various domains, particularly within the built environment. However, there is a sheer volume of definitions and the terminological consensus remains out of reach. The lack of a universally accepted definition leads to ambiguities in their conceptualization and implementation, and may cause miscommunication for both researchers and practitioners. We employed Natural Language Processing (NLP) techniques to systematically extract and analyze definitions of DTs from a corpus of more than 15,000 full-text articles spanning diverse disciplines. The study compares these findings with insights from an expert survey that included 52 experts. The study identifies concurrence on the components that comprise a “Digital Twin” from a practical perspective across various domains, contrasting them with those that do not, to identify deviations. We investigate the evolution of digital twin definitions over time and across different scales, including manufacturing, building, and urban/geospatial perspectives. We extracted the main components of Digital Twins using Text Frequency Analysis and N-gram analysis. Subsequently, we identified components that appeared in the literature and conducted a Chi-square test to assess the significance of each component in different domains. Our analysis identified key components of digital twins and revealed significant variations in definitions based on application domains, such as manufacturing, building, and urban contexts. The analysis of DT components reveal two major groups of DT types: High-Performance Real-Time (HPRT) DTs, and Long-Term Decision Support (LTDS) DTs. Contrary to common assumptions, we found that components such as simulation, AI/ML, real-time capabilities, and bi-directional data flow are not yet fully mature in the digital twins of the built environment. We derived two definitions for the Building/Architecture DT and the City/Urban DTs. Both definitions have a must-have components (such as spatial and temporal data updates) and good-to-have components such as prediction, AI, bi-directional data flow, and Real-time data exchange. One of the key findings is that the definition of digital twins has not yet reached its equilibrium phase, highlighting the need for ongoing revisions as technologies emerge or existing ones become obsolete. To address this, we introduce a novel, reproducible methodology that enables researchers to refine and adapt the current definitions in response to technological advancements or deprecations.
Designing effective image-based surveys for urban visual perception
Gu, Youlong; Quintana Rosales, Matias Alberto; Liang, Xiucheng; et al. (2025)
Landscape and Urban Planning
Urban visual perception is important for the human experience in cities, shaped by intertwined characteristics of urban landscapes. By quantifying and explaining these perceptual experiences, researchers can gain insights into human preferences and support decision-making in planning and design. However, past studies have shown inconsistencies in survey design and ambiguities in reporting, leading to concerns about the reliability and reproducibility of results. This study proposes the first comprehensive framework to guide image-based survey design for capturing perceptions of outdoor urban environments across different scenarios, addressing the lack of methodological standardization in current research. We reviewed existing surveys to identify key parameters, conducted comprehensive between-subject and within-subject surveys, and performed statistical analyses to determine best practices for survey design across different contexts. Aiming to set a potential community standard, our study doubles as a blueprint for a reporting protocol for survey designs. Based on the results, we recommend: (1) meeting a minimum of 12 and 22 ratings per image for Likert Scale and Pairwise Comparison studies to reach survey reliability, respectively, and reporting these alongside other survey design parameters to enhance transparency and reproducibility; and (2) when resource allows larger experiments, adopt a ranking method such as Pairwise Comparison to achieve firmer rating results; and (3) using perspective (non-panoramic) images more frequently, as they exhibit comparable overall scores to panoramic images (R mostly > 0.7), while being more widely available via crowdsourced sources, supporting their use in large-scale visual perception research.

Publications 1 - 10 of 11

Matias Alberto Quintana Rosales

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