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Transcending the Settled Ground: Mapping Obligations of Settler Architects in Canada
No full textThis research begins with the recognition that architecture, as both a discipline and a practice, has never been neutral. The design process has long been entangled with histories of colonization, where land was surveyed and transformed into property. In Canada, these processes were instrumental in establishing a settler-colonial relationship to land, one that privileges extraction and productivity over reciprocity and care. This study asks how the architectural design process reproduces these colonial structures and how settler architects might begin to take responsibility for the histories that shape their work.
In the outskirts of Sarnia, Ontario, an area known as Chemical Valley sits on the traditional territory of the Chippewa, Odawa, and Potawatomi peoples, collectively known as the Anishinaabeg. It is home to the Aamjiwnaang First Nations reserve and a dense concentration of petrochemical refineries. The term “Chemical Valley” dates back to 1947 as a symbol of national progress, but is now used by Aamjiwnaang First Nations and activists to draw attention to the negative impacts of this industrial corridor. The proximity between the two landscapes exposes how colonial systems of land use and resource extraction persist in shaping environmental and social conditions today. By tracing the evolution of architectural and planning tools, from early land surveys to contemporary zoning and professional standards, this research uncovers how these systems continue to define who has access to land and who bears the burden of its consequences.
Through archival analysis and an examination of city planning and architectural practice, this work delineates how the design process itself has been used to legitimize and justify land dispossession. Acts of measuring and drawing are political gestures that determine what is seen, valued, and remembered. Confronting this legacy requires a reorientation of architectural practice, one that shifts toward methods grounded in care and reciprocity, prioritizing relationships with the land
Climate-Geoengineering as Development: Applying the Capabilities Approach to Climate Responses in South- Asia
No full textThe significant range of climate change impacts and capacities to address climate change demands that responses be diverse and indigenized through a democratic process. In the case of climate geoengineering (CGE), the predominant views have come from scholars from the global North who have explored CGE research based on their perspectives and developed states' political and economic contexts. As the discussion surrounding CGE moves from the periphery to the center of climate policy debates, there is a need for more scholars from the global South to come forward and apply perspectives and theoretical lenses informed by the conditions experienced in the global South to the debates on CGE research and governance. Framing CGE as a set of climate responses that address the needs and values of the global South may increase the salience of these debates for developing countries. To this end, this dissertation develops a theoretical framework for examining CGE based on Amartya Sen's capabilities approach. Then, it applies this framework to two case studies addressing the relationships between CGE and South Asia's development context.
In the theoretical chapter, I argue that it is important to reframe CGE in a way that is more likely to align with the broader development aspirations of the global South. Reframing CGE as a development is a difficult task because development is a contested term with multiple meanings. Therefore, I consider Sen's idea of “development as freedom” and explore CGE through Amartya Sen’s capability framework to analyze whether CGE can be of potential use for protecting and advancing human capabilities threatened due to climate change. The theory of capabilities offers a promising framework that can provide an ethical foundation for analyzing the interface of developing countries with CGE research and governance. The capability approach is rooted in the reality of the global South. The justice-oriented capability framework emphasizes human autonomy, which looks at practical capabilities to attain individual and collective ends.
The first case study examines the link between Carbon Dioxide Removal (CDR) and development under China's transnational development initiative, namely the Belt and Road Initiative (BRI) in Pakistan. BRI is seen as a crucial enterprise for economic development by policymakers of many developing countries. However, simultaneously, BRI can compromise the emission reduction goals of host countries, which they committed to under the Paris Agreement. Pakistan is one of the most significant participants of the BRI, with numerous coal-fired energy projects sponsored by China under the BRI. Pakistan also has ambitious emission reduction goals demonstrated through its Nationally Determined Contributions (NDCs) and an internationally celebrated afforestation and reforestation project like the Billion Tree Tsunami (BTS). This case study examines whether CDR initiatives can be integrated into modern development structures like the BRI and the associated governance challenges. The key findings point to the limited degree that the BRI structure has engaged with CDR, notwithstanding the acknowledgment of the need to “green” the BRI. CDR holds potential as a source of just climate development, but the top-down structure of the BRI limits the democratic values that lie at the center of Sen’s approach to development (as freedom).
In the second case study, I analyze the case of targeted geoengineering for protecting and preserving the rapidly melting glaciers in the Himalayas, Karakoram, and Hindukush (HKHK) in South Asia. These rapidly melting glaciers can cause floods and food insecurity in the region and reduce the capabilities of the people to pursue development. The imperative of glacier preservation is recognized by the United Nations General Assembly Resolution in designating 2025 as the International Year of Glacier Preservation. In South Asia, the mountain ranges of HKHK present a case for glacier preservation because of the extreme importance of these glaciers for the water, food, and energy security of the region's people, making glacier preservation a key development issue. This chapter examines whether targeted geoengineering can protect development under threat due to climate-induced disasters in the HKHK region. The idea of targeted geoengineering has been primarily explored in the context of Arctic intervention to preserve and protect the polar ice, but the urgency of glacier preservation in South Asia underlines the importance of examining new climate responses. The case study applies the capabilities framework to consider the normative and governance dimensions of glacier preservation. This case study is more exploratory, given the high degree of uncertainty surrounding approaches to glacier preservation. The chapter seeks to identify a set of governance principles consistent with the capabilities approach that may guide the complex cooperation and governance challenges that regional glacier preservation would entail.
The conclusion revisits the merits of framing CGE as an emerging element of development discourses
The Architecture of Grief: Representing the Evolution of Shia Mourning Spaces and Contributions to Islamic Architecture
No full textIn Shia Islam, commemorative mourning rituals for the martyrdom of Imam Hussain, the grandson of the Islamic Prophet Muhammad, at the Battle of Karbala in 680 CE, have undergone a process of evolution over the past 1,400 years. In documenting the evolution of the typology of the Shia Mosque, we find that its programs are directly related to the mourning rituals and symbolic icons that they house. This evolution is marked by the migration of material and visual forms to new lands, and its resultant replications vary in their scale and in their accuracy, often interacting and absorbing the cultural underpinnings of the region it occupies.
This process reflects the spatiotemporal re-imagining of the phenomenology of “parallel pilgrimages” that captivates generations of Muslims. This thesis aims to explore these practices by focusing on ritual architectural events such as craft-making, mosaic arts, processions, and the creation of replica shrines. With the aim to demystify the current Shia practices and their distinctions from universal mosque spaces, a design approach focused on religious and cultural contributions on this form of collective grief and remembrance can have an opportunity to provide a space for clarity and education for what is a heavily stigmatized practice
Type-Safe Tree Transformations for Precisely-Typed Compilers
No full textCompilers translate programs from a source language to a target language. This can be
done by translating into an intermediate tree representation, performing multiple partial
transformations on the tree, and translating out. Compiler implementers must choose
between encoding transformation invariants with multiple tree types at the cost of code
boilerplate and duplication, or forgoing static correctness by using a broad and imprecise
tree datatype.
This thesis proposes a new approach to writing precisely-typed compilers and tree
transformations without code boilerplate or duplication. The approach requires encoding
the tree nodes using two type indices, one for the phase of the root node and another for
the phase of all children nodes. This tree datatype can represent partially transformed
nodes, and distinguish between trees of different phases.
In order to make this approach possible it was necessary to modify the Scala type
checker to better infer types in ‘match’-expressions. Precisely-typed tree transformations
make use of the default case of a ‘match’-expression; this case must be elaborated to a
type which is the type difference of the scrutinee expression and all previously matched
patterns.
We demonstrate the viability of this approach by implementing a case study for a
precisely-typed compiler for the instructional language Lacs. This case study modifies the
existing implementation of Lacs to track which subset of tree nodes may be present before
and after any given tree transformation. We show that this approach can increase static
correctness without the burden of additional code boilerplate or duplication
Towards SLAM-Centric Inspection of Infrastructure
No full textThe inspection and maintenance of civil infrastructure are essential for ensuring public safety, minimizing economic losses, and extending the lifespan of critical assets such as bridges and parking garages. Traditional inspection methods rely heavily on manual visual assessments, which are often subjective, labor-intensive, and inconsistent. These limitations have driven the development of robotic-aided inspection techniques that leverage mobile robotics, sensor fusion, computer vision, and machine learning to enhance inspection efficiency and accuracy.
Despite advancements in robotic-aided inspection, existing works often focus on isolated components of the inspection process—such as improving data collection or automating defect detection—without providing a complete end-to-end solution. Many approaches utilize robotics to capture 2D images for inspection, but these lack spatial context, making it difficult to accurately locate, quantify, and track defects over multiple inspections. Other works extend this by detecting defects within images; however, without a robust 3D representation, defects cannot be precisely geolocated or measured in real-world dimensions, limiting their utility for long-term monitoring. While some studies explore 3D mapping for inspection, the majority rely on image-only Structure-from-Motion, which is known to be unreliable for generating dense and accurate maps, or are restricted to mapping along 2D surfaces, thereby failing to capture the full complexity of infrastructure assets.
This thesis introduces a novel SLAM (Simultaneous Localization and Mapping)-centric framework for robotic infrastructure inspection, addressing these limitations by integrating lidar, cameras, and inertial measurement units (IMUs) into a mobile robotic platform. This system enables precise and repeatable localization, 3D mapping, and automated inspection of infrastructure assets.
Three key challenges that hinder the development of a practical SLAM-centric inspection system are identified and addressed in this work. The first challenge pertains to the design and implementation of SLAM-centric robotic systems. This thesis demonstrates how sensor selection and configuration can be optimized to simultaneously support both highaccuracy SLAM and high-quality inspection data collection. Additionally, it establishes
a robotic platform-agnostic design, allowing for flexibility across different infrastructure inspection applications. The second challenge involves precise and reliable calibration of camera-lidar systems, particularly when sensors have non-overlapping fields of view as is the case with the proposed inspection systems. To address this, a novel target-based extrinsic calibration technique is developed, leveraging a motion capture system to achieve high-precision calibration across both sensing modalities. This ensures accurate sensor fusion, yielding geometrically consistent inspection outputs. The third challenge is the development of a complete end-to-end inspection methodology. This research implements state-of-the-art online camera-lidar-IMU SLAM, with an added offline refinement process and a decoupled mapping framework. This approach enables the generation of high-quality 3D maps that are specifically tailored for infrastructure inspection by prioritizing accuracy, density, and low noise in the map. Machine learning-based defect detection is then integrated into the pipeline, coupled with a novel 3D map labeling method that transfers visual and defect information onto the 3D inspection map. Finally, an automated defect quantification and tracking system is introduced, allowing for defects to be monitored across multiple inspection cycles—completing the full end-to-end inspection workflow.
The proposed SLAM-centric inspection system is validated through extensive real-world experiments on infrastructure assets, including a bridge and a parking garage. Results demonstrate that the system generates highly accurate, repeatable, and metrically consistent inspection data, significantly improving upon traditional manual inspection methods. By enabling automated defect detection, precise localization, and long-term defect tracking within a robust 3D mapping framework, this research represents a paradigm shift in infrastructure assessment—transitioning from qualitative visual inspections to scalable, data-driven, and quantitative condition monitoring.
Ultimately, this thesis advances the field of robotic infrastructure inspection by presenting a comprehensive SLAM-centric framework that integrates state-of-the-art sensing, calibration, and mapping techniques. The findings have broad implications for the future of automated infrastructure management, providing a foundation for intelligent inspection systems that can enhance the efficiency, reliability, and safety of civil infrastructure maintenance worldwide
Visual Change in the Urban Landscape: Taste, Gentrification & Displacement
No full textMeasuring the pace, characteristics and spatial distribution of gentrification is important to developing policies to mitigate its negative consequences, most crucially displacement. Typically, this is done through an analysis of census data on demographic, socioeconomic or housing change. However, this approach has numerous shortcomings, including the homogenizing effect on differences within neighbourhoods and the infrequency of census data collection. Visual analysis, particularly when examining multiple temporal views of the same location, has the potential to render visible fine-grained detail about spatial, economic and cultural changes within the urban landscape. Google Street View (GSV) is emerging as a source of repeat photography data. In this thesis, GSV is employed for analysis within a number of neighbourhoods and retail streets in Hamilton, Ontario. Coding and analyzing GSV images between 2009 and 2021 reveals an array of specific home upgrades, retail turnover as well as aesthetic changes that reflect middle-class tastes, values and lifestyles that suggest more upgrading than found within conventional statistics or dominant narratives about the city. Mapping these changes paints a complex, and fine-grained, block-by-block picture of gentrification that reveals why some areas are more conducive to gentrification than others and how retail gentrification can lead to both direct and indirect displacement. This analysis is important for critical visual methodologies, gentrification and neighbourhood change theories in addition to planning and policymaking
Advancing Structural Engineering Through Data-Driven Methodologies: Seismic Vulnerability Assessment and Backbone Curve Determination
No full textStructural engineering has traditionally relied on analytical and experimental methods to ensure the safety of structures. These methods, while effective, often require significant resources, time, and expertise, limiting their applicability across diverse contexts. Meanwhile, vast amounts of data collected from surveys, experimental studies, and seismic events remain largely underutilized, providing a unique opportunity to develop advanced data-driven methodologies. This thesis aims to harness the potential of the available data repositories to address critical challenges in structural engineering, with a focus on seismic vulnerability assessment and backbone curve determination. Through the use of machine learning (ML), this thesis introduces innovative methodologies at both the system and component levels. A rapid visual screening (RVS) framework is developed to quickly assess the seismic vulnerability of low-rise reinforced concrete (RC) buildings. By incorporating ML models, this framework outperforms traditional evaluation methods with higher accuracy and broader applicability. Using post-earthquake survey datasets from a variety of seismic events, it proposes a region-independent tool, eliminating reliance on subjective judgments and region-specific calibrations. For backbone curve determination, used for analyzing the seismic behavior of RC columns, this thesis introduces a novel ML-based methodology. By employing experimental datasets and advanced regression techniques, it offers a practical and efficient alternative to the conventional methods. This approach not only predicts backbone curve parameters with high accuracy but also ensures accessibility for broader applications, especially in resource-limited environments. In summary, this thesis bridges system-level and component-level challenges, underscoring the potential of data-driven approaches in structural engineering. By providing a foundation for integrating innovative approaches into the field, this research advances both academic insights and practical applications. These contributions respond to the demand for efficient and reliable solutions, supporting safer structures and more effective resource management in modern structural engineering practices
Redefining Senior Housing: Architecture as a Catalyst for Health, Autonomy, and Community in Toronto
No full textThe urban environment is not seen as an ideal place to retire as housing is becoming increasingly less attainable in terms of affordability and availability. With an impending demographic shift of seniors estimated to rise in the coming years, a viable housing solution is more pressing. As of 2018, adults over 65 made up 17.4% of the Canadian population, but by projected estimates, this could rise to between 21.4% and 29.5% in 2068. As of 2016, over 60% of adults 65 and over in Toronto had one or more health complications, with numbers projected to rise in the following years. This creates great strain on hospital resources as the population ages and grows. Among seniors, the main priority is finding housing that enables them to age-in-community as the thought of long-term care (LTC) and retirement homes (RHs) are not desirable as they often signal the loss of self-autonomy and often require being displaced from their community. COVID-19 uncovered flaws in the senior housing system through high infection rates, aided by architecture not designed to cope with such circumstances. Following COVID-19, more studies began to draw on previous theories focusing on the psychological effects of architecture on its occupants and, more importantly, how it affects one’s health. Significant evidence points to exposure to nature as psychologically improving one’s mental and physical state. Notably, Roger S. Ulrich’s research in the design of healthcare facilities revealed that patients are discharged faster when exposed to nature within a hospital setting, requiring fewer antibiotics and pain relievers than those without a means of interacting with nature. By designing spaces prioritizing health, we can create places of refuge that promote health and well-being outside of a hospital setting. Many of these homes use "beds" as a metric to quantify resources, but this language may indirectly undermine the role that architecture plays as a part in elder care. How can architecture be a driving force in promoting health and fostering independence while supporting the maintenance of physical and cognitive function for seniors in urban Toronto
A Comprehensive Framework Incorporating Hybrid Deep Learning Model, Vi-Net, for Wildfire Spread Prediction and Optimized Safe Path Planning
No full textForest fires are becoming more prevalent than ever, and their intensity and frequency are expected only to increase owing to climate change and environmental degradation. These fires severely threaten the economy, human lives, and infrastructure. Therefore, effective management of wildfires is of utmost importance, and accurately predicting the wildfire spread lies at the core of it. Reliable predictions of fire spread not only provide insights about the at-risk regions but also help in planning several mitigation activities including resource allocation and evacuation planning. This thesis introduces Vi-Net, an innovative hybrid deep learning model, which integrates the localized precision of U-Net with the global contextual awareness of Vision Transformers (ViT) to predict next-day wildfire spread with unprecedented accuracy.
This study utilizes an extensive multimodal dataset that accumulates data from different sources across the United States from 2012 to 2020 incorporating critical factors such as topographical, meteorological, anthropological (population density), and vegetation indices. These elements are vital for modeling the complex dynamics of wildfire spread. A significant challenge in this domain is the class imbalance as the fire points are generally quite less compared to non-fire points. The dataset used in this study had fire regions less than 5% of the total data. To address this issue, advanced loss functions, including Focal Tversky Loss (FTL), are employed, prioritizing accurate segmentation of fire-prone regions while minimizing false negatives. FTL modifies the focus towards hard-to-predict regions and crucial boundaries, thereby enhancing the model's predictive accuracy and reliability in practical scenarios.
Vi-Net addresses the complexities in modeling fire dynamics by synergizing the strengths of U-Net and ViT. Integrating U-Net and ViT in Vi-Net allows for a comprehensive analysis that ensures high precision and recall, effectively balancing the sensitivity and specificity needed in wildfire predictions. This dual approach allows the model to process detailed local information and extensive contextual data, making it exceptionally capable of identifying and predicting fire spread across diverse landscapes. Experimental results highlight the superiority of Vi-Net over traditional models, achieving an F1 Score of 97.25% and an Intersection over Union (IoU) of 94.15% on the test dataset. These metrics highlight its capability to accurately capture localized fire patches and long-range dependencies while avoiding overprediction. These advancements validate the model's potential to offer more nuanced predictions, capturing the interplay between micro and macro-level environmental dynamics.
In addition to predictive modeling, this research extends its practical applicability by integrating the predicted fire masks into an optimized A* algorithm for safe path planning. This step ensures actionable insights for emergency response teams, facilitating efficient evacuation routes and resource allocation while avoiding high-risk fire regions. Qualitative and quantitative analyses confirm the hybrid model's efficacy, with visualizations demonstrating Vi-Net’s ability to preserve spatial detail while capturing broad environmental contexts, and path planning results illustrating the model's robustness and reliability.
This research not only sets a new benchmark for wildfire prediction models but also demonstrates the potential of hybrid deep learning systems in environmental science applications. By providing a robust framework for real-time wildfire management, Vi-Net could significantly influence future strategies in disaster response and resource allocation. Future enhancements could include integrating real-time data feeds to further improve the adaptability and predictive capabilities of the model, potentially revolutionizing wildfire management practices globally
Assessment of AI-Generated Pediatric Rehabilitation SOAP-Note Quality
No full textThis study explores the integration of artificial intelligence (AI) or large language models (LLMs) into pediatric rehabilitation clinical documentation, focusing on the generation of SOAP (Subjective, Objective, Assessment, Plan) notes, which are essential for patient care. Creating complex documentation is time-consuming in pediatric settings. We evaluate the effectiveness of two AI tools; Copilot, a commercial LLM, and KAUWbot, a fine-tuned LLM developed for KidsAbility Centre for Child Development (an Ontario pediatric rehabilitation facility), in simplifying and automating this process. We focus on two key questions: (i) How does the quality of AI-generated SOAP notes based on short clinician summaries compare to human-authored notes, and (ii) To what extent is human editing necessary for improving AI-generated SOAP notes? We found no evidence of prior work assessing the quality of AI-generated clinical notes in pediatric rehabilitation.
We used a sample of 432 SOAP notes, evenly divided among human-authored, Copilot-generated, and KAUWbot-generated notes. We employ a blind evaluation by experienced clinicians based on a custom rubric. Statistical analysis is conducted to assess the quality of the notes and the impact of human editing. The results suggest that AI tools such as KAUWbot and Copilot can generate SOAP notes with quality comparable to those authored by humans. We highlight the potential for combining AI with human expertise to enhance clinical documentation and offer insights for the future integration of AI into pediatric rehabilitation practice and other settings for the management of clinical conditions