arXiv subjects

Yuhao Kang

Publications and source records attributed to Yuhao Kang.

At least 19 recordsLinked to original sources

arXiv2026

EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards

Earth-system analysis reconstructs changing physical processes from observations that differ in source, scale, timing, and modality. Natural hazards make this work consequential because incomplete evidence can change estimates of severity, exposure, and mechanism. We introduce EarthVerse, a benchmark that evaluates scientific agents through package-scoped investigations. Its 405 reproducible tasks are grounded in 199 documented events and 19 hazard families. Agents inspect heterogeneous event packages, choose compatible evidence, execute transparent calculations, reconcile source differences, and preserve provenance in the final answer. We provide executable ground truth that decomposes each task into fine-grained answer units, together with task-specific rubrics that assess the supporting research process while allowing multiple valid paths. We evaluate 25 model and agent systems under a controlled tool-using protocol, then use controlled studies to locate failures in evidence access, tool selection, memory, reasoning, interaction, and scientific execution. Across systems, the best mean answer-unit accuracy is 84.65%, while the highest Strict@95 is only 34.81%. The gap shows that current agents often complete individual steps without maintaining a consistent chain across evidence, scales, units, calculations, and physical interpretation. EarthVerse provides a reproducible basis for measuring end-to-end scientific reliability in dynamic Earth systems.

cs.AI↗

arXiv2026

Uncovering the Associations between Human Big Five Personality Traits and Built Environment Characteristics from Street View Imagery

Human-environment interactions, a classic topic in geography, suggest that individuals and their environments might shape each other. Yet the specific mechanisms underlying these interactions regarding human personality traits have not been explored. This study examines the associations between human Big Five personality traits and built environment characteristics derived from street view imagery across four cities in Texas, United States, providing a descriptive foundation for understanding these complex human-environment dynamics. By integrating fine-resolution self-reported personality assessments with computer vision analysis of urban environments, we identified significant spatial clustering of personality traits at the ZIP code level. Our regression analyses reveal that built environment features and socioeconomic characteristics explain substantial variance in personality distributions, with Openness showing the strongest model fit (R^2 = 0.47), followed by Agreeableness, Conscientiousness, Extraversion, and Neuroticism. Grouped built environment categories, socioeconomic factors, and demographic composition showed trait-specific patterns of association. These findings illustrate how personality traits may be associated with physical spaces at a smaller geographic scale than previously examined. Our results provide empirical evidence for understanding the link between psychological characteristics and environmental features, which can potentially enrich geography studies from a human-centered perspective.

cs.HC↗

arXiv2025

Human vs. AI Safety Perception? Decoding Human Safety Perception with Eye-Tracking Systems, Street View Images, and Explainable AI

The way residents perceive safety plays an important role in how they use public spaces. Studies have combined large-scale street view images and advanced computer vision techniques to measure the perception of safety of urban environments. Despite their success, such studies have often overlooked the specific environmental visual factors that draw human attention and trigger people's feelings of safety perceptions. In this study, we introduce a computational framework that enriches the existing body of literature on place perception by using eye-tracking systems with street view images and deep learning approaches. Eye-tracking systems quantify not only what users are looking at but also how long they engage with specific environmental elements. This allows us to explore the nuance of which visual environmental factors influence human safety perceptions. We conducted our research in Helsingborg, Sweden, where we recruited volunteers outfitted with eye-tracking systems. They were asked to indicate which of the two street view images appeared safer. By examining participants' focus on specific features using Mean Object Ratio in Highlighted Regions (MoRH) and Mean Object Hue (MoH), we identified key visual elements that attract human attention when perceiving safe environments. For instance, certain urban infrastructure and public space features draw more human attention while the sky is less relevant in influencing safety perceptions. These insights offer a more human-centered understanding of which urban features influence human safety perceptions. Furthermore, we compared the real human attention from eye-tracking systems with attention maps obtained from eXplainable Artificial Intelligence (XAI) results. Several XAI models were tested, and we observed that XGradCAM and EigenCAM most closely align with human safety perceptual patterns.

cs.HC↗

arXiv2025

Envisioning Generative Artificial Intelligence in Cartography and Mapmaking

Generative artificial intelligence (GenAI), including large language models, diffusion-based image generation models, and GenAI agents, has provided new opportunities for advancements in mapping and cartography. Due to their characteristics including world knowledge and generalizability, artistic style and creativity, and multimodal integration, we envision that GenAI may benefit a variety of cartographic design decisions, from mapmaking (e.g., conceptualization, data preparation, map design, and map evaluation) to map use (such as map reading, interpretation, and analysis). This paper discusses several important topics regarding why and how GenAI benefits cartography with case studies including symbolization, map evaluation, and map reading. Despite its unprecedented potential, we identify key scenarios where GenAI may not be suitable, such as tasks that require a deep understanding of cartographic knowledge or prioritize precision and reliability. We also emphasize the need to consider ethical and social implications, such as concerns related to hallucination, reproducibility, bias, copyright, and explainability. This work lays the foundation for further exploration and provides a roadmap for future research at the intersection of GenAI and cartography.

cs.HC↗

arXiv2025

Where are GIScience Faculty Hired from? Analyzing Faculty Mobility and Research Themes Through Hiring Networks

Academia is profoundly influenced by faculty hiring networks, which serve as critical conduits for knowledge dissemination and the formation of collaborative research initiatives. While extensive research in various disciplines has revealed the institutional hierarchies inherent in these networks, their impacts within GIScience remain underexplored. To fill this gap, this study analyzes the placement patterns of 946 GIScience faculty worldwide by mapping the connections between PhD-granting institutions and current faculty affiliations. Our dataset, which is compiled from volunteer-contributed information, is the most comprehensive collection available in this field. While there may be some limitations in its representativeness, its scope and depth provide a unique and valuable perspective on the global placement patterns of GIScience faculty. Our analysis reveals several influential programs in placing GIScience faculty, with hiring concentrated in the western countries. We examined the diversity index to assess the representation of regions and institutions within the global GIScience faculty network. We observe significant internal retention at both the continental and country levels, and a high level of non-self-hired ratio at the institutional level. Over time, research themes have also evolved, with growing research clusters emphasis on spatial data analytics, cartography and geovisualization, geocomputation, and environmental sciences, etc. These results illuminate the influence of hiring practices on global knowledge dissemination and contribute to promoting academic equity within GIScience and Geography.

cs.HC↗

arXiv2025

Inverse design of the transmission matrix in a random system using Reinforcement Learning

This work presents an approach to the inverse design of scattering systems by modifying the transmission matrix using reinforcement learning. We utilize Proximal Policy Optimization to navigate the highly non-convex landscape of the object function to achieve three types of transmission matrices: (1) Fixed-ratio power conversion and zero-transmission mode in rank-1 matrices, (2) exceptional points with degenerate eigenvalues and unidirectional mode conversion, and (3) uniform channel participation is enforced when transmission eigenvalues are degenerate.

physics.optics↗

arXiv2025

SounDiT: Geo-Contextual Soundscape-to-Landscape Generation

Recent audio-to-image models have shown impressive performance in generating images of specific objects conditioned on their corresponding sounds. However, these models fail to reconstruct real-world landscapes conditioned on environmental soundscapes. To address this gap, we present Geo-contextual Soundscape-to-Landscape (GeoS2L) generation, a novel and practically significant task that aims to synthesize geographically realistic landscape images from environmental soundscapes. To support this task, we construct two large-scale geo-contextual multi-modal datasets, SoundingSVI and SonicUrban, which pair diverse environmental soundscapes with real-world landscape images. We propose SounDiT, a diffusion transformer (DiT)-based model that incorporates environmental soundscapes and geo-contextual scene conditioning to synthesize geographically coherent landscape images. Furthermore, we propose the Place Similarity Score (PSS), a practically-informed geo-contextual evaluation framework to measure consistency between input soundscapes and generated landscape images. Extensive experiments demonstrate that SounDiT outperforms existing baselines in the GeoS2L, while the PSS effectively captures multi-level generation consistency across element, scene,and human perception. Project page: https://gisense.github.io/SounDiT-Page/

cs.SD↗

arXiv2025

CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation

The rapid development of generative artificial intelligence (GenAI) presents new opportunities to advance the cartographic process. Previous studies have either overlooked the artistic aspects of maps or faced challenges in creating both accurate and informative maps. In this study, we propose CartoAgent, a novel multi-agent cartographic framework powered by multimodal large language models (MLLMs). This framework simulates three key stages in cartographic practice: preparation, map design, and evaluation. At each stage, different MLLMs act as agents with distinct roles to collaborate, discuss, and utilize tools for specific purposes. In particular, CartoAgent leverages MLLMs' visual aesthetic capability and world knowledge to generate maps that are both visually appealing and informative. By separating style from geographic data, it can focus on designing stylesheets without modifying the vector-based data, thereby ensuring geographic accuracy. We applied CartoAgent to a specific task centered on map restyling-namely, map style transfer and evaluation. The effectiveness of this framework was validated through extensive experiments and a human evaluation study. CartoAgent can be extended to support a variety of cartographic design decisions and inform future integrations of GenAI in cartography.

cs.HC↗

arXiv2025

Generative AI for Autonomous Driving: Frontiers and Opportunities

Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation, reasoning, planning, and multimodal understanding. This revolutionary force offers the most promising path yet toward solving one of engineering's grandest challenges: achieving reliable, fully autonomous driving, particularly the pursuit of Level 5 autonomy. This survey delivers a comprehensive and critical synthesis of the emerging role of GenAI across the autonomous driving stack. We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models (LLMs). We then map their frontier applications in image, LiDAR, trajectory, occupancy, video generation as well as LLM-guided reasoning and decision making. We categorize practical applications, such as synthetic data workflows, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI. We identify key obstacles and possibilities such as comprehensive generalization across rare cases, evaluation and safety checks, budget-limited implementation, regulatory compliance, ethical concerns, and environmental effects, while proposing research plans across theoretical assurances, trust metrics, transport integration, and socio-technical influence. By unifying these threads, the survey provides a forward-looking reference for researchers, engineers, and policymakers navigating the convergence of generative AI and advanced autonomous mobility. An actively maintained repository of cited works is available at https://github.com/taco-group/GenAI4AD.

cs.CV↗

arXiv2025

Human-centered Geospatial Data Science

This entry provides an overview of Human-centered Geospatial Data Science, highlighting the gaps it aims to bridge, its significance, and its key topics and research. Geospatial Data Science, which derives geographic knowledge and insights from large volumes of geospatial big data using advanced Geospatial Artificial Intelligence (GeoAI), has been widely used to tackle a wide range of geographic problems. However, it often overlooks the subjective human experiences that fundamentally influence human-environment interactions, and few strategies have been developed to ensure that these technologies follow ethical guidelines and prioritize human values. Human-centered Geospatial Data Science advocates for two primary focuses. First, it advances our understanding of human-environment interactions by leveraging Geospatial Data Science to measure and analyze human subjective experiences at place including emotion, perception, cognition, and creativity. Second, it advocates for the development of responsible and ethical Geospatial Data Science methods that protect geoprivacy, enhance fairness and reduce bias, and improve the explainability and transparency of geospatial technologies. With these two missions, Human-centered Geospatial Data Sciences brings a fresh perspective to develop and utilize geospatial technologies that positively impact society and benefit human well-being and the humanities.

cs.CY↗

arXiv2024

A Review of Spatial Network Insights and Methods in the Context of Planning: Applications, Challenges, and Opportunities

With the rise of geospatial big data, new narratives of cities based on spatial networks and flows have replaced the traditional focus on locations. While plenty of research have empirically analyzed network structures, there lacks a state-of-the-art synthesis of applicable insights and methods of spatial networks in the planning context. In this chapter, we reviewed the theories, concepts, methods, and applications of spatial network analysis in cities and their insights for planners from four areas of concern: spatial structures, urban infrastructure optimizations, indications of economic wealth, social capital, and residential mobility, and public health control (especially COVID-19). We also outlined four challenges that planners face when taking the planning knowledge from spatial networks to actions: data openness and privacy, linkage to direct policy implications, lack of civic engagement, and the difficulty to visualize and integrate with GIS. Finally, we envisioned how spatial networks can be integrated into a collaborative planning framework.

physics.soc-ph↗

arXiv2024

InteractiveVideo: User-Centric Controllable Video Generation with Synergistic Multimodal Instructions

We introduce $\textit{InteractiveVideo}$, a user-centric framework for video generation. Different from traditional generative approaches that operate based on user-provided images or text, our framework is designed for dynamic interaction, allowing users to instruct the generative model through various intuitive mechanisms during the whole generation process, e.g. text and image prompts, painting, drag-and-drop, etc. We propose a Synergistic Multimodal Instruction mechanism, designed to seamlessly integrate users' multimodal instructions into generative models, thus facilitating a cooperative and responsive interaction between user inputs and the generative process. This approach enables iterative and fine-grained refinement of the generation result through precise and effective user instructions. With $\textit{InteractiveVideo}$, users are given the flexibility to meticulously tailor key aspects of a video. They can paint the reference image, edit semantics, and adjust video motions until their requirements are fully met. Code, models, and demo are available at https://github.com/invictus717/InteractiveVideo

cs.CV↗

arXiv2023

Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics

The past decade has witnessed the rapid development of geospatial artificial intelligence (GeoAI) primarily due to the ground-breaking achievements in deep learning and machine learning. A growing number of scholars from cartography have demonstrated successfully that GeoAI can accelerate previously complex cartographic design tasks and even enable cartographic creativity in new ways. Despite the promise of GeoAI, researchers and practitioners have growing concerns about the ethical issues of GeoAI for cartography. In this paper, we conducted a systematic content analysis and narrative synthesis of research studies integrating GeoAI and cartography to summarize current research and development trends regarding the usage of GeoAI for cartographic design. Based on this review and synthesis, we first identify dimensions of GeoAI methods for cartography such as data sources, data formats, map evaluations, and six contemporary GeoAI models, each of which serves a variety of cartographic tasks. These models include decision trees, knowledge graph and semantic web technologies, deep convolutional neural networks, generative adversarial networks, graph neural networks, and reinforcement learning. Further, we summarize seven cartographic design applications where GeoAI have been effectively employed: generalization, symbolization, typography, map reading, map interpretation, map analysis, and map production. We also raise five potential ethical challenges that need to be addressed in the integration of GeoAI for cartography: commodification, responsibility, privacy, bias, and (together) transparency, explainability, and provenance. We conclude by identifying four potential research directions for future cartographic research with GeoAI: GeoAI-enabled active cartographic symbolism, human-in-the-loop GeoAI for cartography, GeoAI-based mapping-as-a-service, and generative GeoAI for cartography.

cs.HC↗

arXiv2023

Understanding Place Identity with Generative AI

Researchers are constantly leveraging new forms of data with the goal of understanding how people perceive the built environment and build the collective place identity of cities. Latest advancements in generative artificial intelligence (AI) models have enabled the production of realistic representations learned from vast amounts of data. In this study, we aim to test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of a set of 31 global cities to two generative AI models, ChatGPT and DALL-E2. Since generative AI has raised ethical concerns regarding its trustworthiness, we performed cross-validation to examine whether the results show similar patterns to real urban settings. In particular, we compared the outputs with Wikipedia data for text and images searched from Google for image. Our results indicate that generative AI models have the potential to capture the collective image of cities that can make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in understanding human perceptions of the built environment. It contributes to urban design literature by discussing future research opportunities and potential limitations.

cs.LG↗

arXiv2023

The Ethics of AI-Generated Maps: A Study of DALLE 2 and Implications for Cartography

The rapid advancement of artificial intelligence (AI) such as the emergence of large language models including ChatGPT and DALLE 2 has brought both opportunities for improving productivity and raised ethical concerns. This paper investigates the ethics of using artificial intelligence (AI) in cartography, with a particular focus on the generation of maps using DALLE 2. To accomplish this, we first create an open-sourced dataset that includes synthetic (AI-generated) and real-world (human-designed) maps at multiple scales with a variety settings. We subsequently examine four potential ethical concerns that may arise from the characteristics of DALLE 2 generated maps, namely inaccuracies, misleading information, unanticipated features, and reproducibility. We then develop a deep learning-based ethical examination system that identifies those AI-generated maps. Our research emphasizes the importance of ethical considerations in the development and use of AI techniques in cartography, contributing to the growing body of work on trustworthy maps. We aim to raise public awareness of the potential risks associated with AI-generated maps and support the development of ethical guidelines for their future use.

cs.CY↗

arXiv2022

STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity

Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering. A subregion is created for each geographic object serving as the basic unit when performing clustering. A Markov random field is then constructed to characterize the attribute dependencies of subregions. Using a spatial consistency strategy, nearby objects are encouraged to belong to the same cluster. To test the performance of the proposed STICC algorithm, we apply it in two use cases. The comparison results with several baseline methods show that the STICC outperforms others significantly in terms of adjusted rand index and macro-F1 score. Join count statistics is also calculated and shows that the spatial contiguity is well preserved by STICC. Such a spatial clustering method may benefit various applications in the fields of geography, remote sensing, transportation, and urban planning, etc.

cs.LG↗

arXiv2021

Wave Excitation and Dynamics in Non-Hermitian Disordered Systems

Dynamic and steady state aspects of wave propagation are deeply connected in lossless open systems in which the scattering matrix is unitary. There is then an equivalence among the energy excited within the medium, the Wigner time delay, and the density of states. But these equivalences fall away in the presence of material loss or gain. In this paper, we use microwave measurements, numerical simulations, and theoretical analysis to discover the changing relationships among the internal field, transmission, transmission time, dwell time, total excited energy, and the density of states in with loss and gain, and their dependence upon dimensionality and spectral overlap. The total energy excited is equal to the dwell time. In 1D, the average transmission time is independent of loss, gain, and scattering strength. In higher dimensions, however, the average transmission time falls with loss, scattering strength and channel number, and increases with gain. It is the sum over Lorentzian functions associated with the zeros as well as the poles of the transmission matrix. This endows transmission zeros with robust topological characteristics: in unitary media, zeros occur either singly on the real axis or as conjugate pairs in the complex frequency plane. In weakly absorbing media, the spectrum of the transmission time of the lowest transmission eigenchannel is the sum of Lorentzians due to transmission zeros plus a background due to far-off-resonance poles. The average density of transmission zeros in the complex plane is found from the fall of the average transmission time with absorption as zeros are swept into the lower half of the complex frequency plane. We show that the disposition of poles and zeros in the complex frequency plane provides a framework for understanding and controlling wave propagation in non-Hermitian systems.

cond-mat.dis-nn↗

arXiv2020

Transmission zeros and ultrasensitive detection in complex systems

Understanding vanishing transmission in Fano resonances in quantum systems and metamaterials, and ultralow transmission in disordered media is key to elucidating optical interactions. Using analytic theory and numerical simulations, we discover the topological structure and motion under deformation of transmission zeros in the complex energy plane. We demonstrate the zeros can be controlled to create ultranarrow Lorentzian lines in spectra of transmission time and diverging rates of frequency change of transmission zeros as they converge. This suggests a new approach to ultrasensitive detection.

cond-mat.dis-nn↗