arXiv subjects

Qiusheng Wu

Publications and source records attributed to Qiusheng Wu.

4 recordsLinked to original sources

arXiv2026

NORA: A Harness-Engineered Autonomous Research Agent for End-to-End Spatial Data Science

The automation of scientific research workflows has emerged as a transformative frontier in artificial intelligence, yet existing autonomous research agents remain largely domain-agnostic, lacking the specialized reasoning, method selection, and data acquisition capabilities required for rigorous spatial data science. This paper introduces NORA (Night Owl Research Agent), a harness-engineered, multi-agent autonomous research system purpose-built for GIScience and spatial data science. NORA orchestrates the complete research lifecycle through a skills-first architecture comprising 21 domain-specialized workflow skills, 9 specialist sub-agents, and custom Model Context Protocol (MCP) servers. Central to the system's design are two novel domain-specialized skills: a spatial analysis skill unit that encodes decision frameworks for exploratory spatial data analysis, spatial regression, and diagnostics; and a spatial data download skill that supports reproducible acquisition from authoritative geospatial data sources. We formalize the concept of harness engineering for scientific research agents, demonstrating how lifecycle hooks, safety gates, generator-evaluator separation, human-in-the-loop, and state persistence ensure reliable and reproducible autonomous research. We evaluate NORA through case studies by 6 domain specialists and 3 LLM reviewers across seven dimensions (novelty, quality, rigor, etc). Results demonstrate that domain-specialized harness engineering substantially improves the efficiency and quality of research output compared to general-purpose agent configurations.

cs.AI↗

arXiv2025

dciWebMapper2: Enhancing the dciWebMapper framework toward integrated, interactive visualization of linked multi-type maps, charts, and spatial statistics and analysis

As interactive web-based geovisualization becomes increasingly vital across disciplines, there is a growing need for open-source frameworks that support dynamic, multi-attribute spatial analysis and accessible design. This paper introduces dciWebMapper2, a significant expansion of the original dciWebMapper framework, designed to enable exploratory analysis across domains such as climate justice, food access, and social vulnerability. The enhanced framework integrates multiple map types, including choropleth, proportional symbol, small multiples, and heatmaps, with linked statistical charts (e.g., scatter plots, boxplots) and time sliders, all within a coordinated-view environment. Dropdown-based controls allow flexible, high-dimensional comparisons while maintaining visual clarity. Grounded in cartographic and information visualization principles, dciWebMapper2 is fully open-source, self-contained, and server-free, supporting modularity, reproducibility, and long-term sustainability. Three applied use cases demonstrate its adaptability and potential to democratize interactive web cartography. This work offers a versatile foundation for inclusive spatial storytelling and transparent geospatial analysis in research, education, and civic engagement.

cs.HC↗

arXiv2024

idwMapper: An interactive and data-driven web mapping framework for visualizing and sensing high-dimensional geospatial (big) data

We are surrounded by overwhelming big data, which brings substantial advances but meanwhile poses many challenges. Geospatial big data comprises a big portion of big data, and is essential and powerful for decision-making if being utilized strategically. Volumes in size and high dimensions are two of the major challenges that prevent strategic decision-making from (geospatial) big data. Interactive map-based and geovisualization enabled web applications are intuitive and useful to construct knowledge and reveal insights from high-dimensional (geospatial) big data for actionable decision-making. We propose an interactive and data-driven web mapping framework, named idwMapper, for visualizing and sensing high dimensional geospatial (big) data in an interactive and scalable manner. To demonstrate the wide applicability and usefulness of our framework, we have applied our idwMapper framework to three real-world case studies and implemented three corresponding web map applications: iLit4GEE-AI, iWURanking, and iTRELISmap. We expect and hope the three web maps demonstrated in different domains, from literature big data analysis through world university ranking to scholar mapping, will provide a good start and inspire researchers and practitioners in various domains to apply our idwMapper to solve (or at least aid them in solving) their impactful problems.

cs.DB↗

arXiv2023

The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot

Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. SAM is known for its exceptional generalization capabilities and zero-shot learning, making it a promising approach to processing aerial and orbital images from diverse geographical contexts. Our exploration involved testing SAM across multi-scale datasets using various input prompts, such as bounding boxes, individual points, and text descriptors. To enhance the model's performance, we implemented a novel automated technique that combines a text-prompt-derived general example with one-shot training. This adjustment resulted in an improvement in accuracy, underscoring SAM's potential for deployment in remote sensing imagery and reducing the need for manual annotation. Despite the limitations encountered with lower spatial resolution images, SAM exhibits promising adaptability to remote sensing data analysis. We recommend future research to enhance the model's proficiency through integration with supplementary fine-tuning techniques and other networks. Furthermore, we provide the open-source code of our modifications on online repositories, encouraging further and broader adaptations of SAM to the remote sensing domain.

cs.CV↗