Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A knowledge-augmented LLM pipeline turns PowerPoint wireframes into working GIS dashboards.

desk verdict A plausible, clearly described prototype for turning PowerPoint wireframes into React GIS dashboards, but the automation claims outrun the evidence: two in-distribution case studies, no baseline, and a knowledge base built from the authors' own projects. read the letter →

arxiv 2502.08756 v1 pith:A56MCVUX submitted 2025-02-12 cs.AI cs.SE

Haowen Xu , Xiao-Ying Yu This is my paper · ORCID
classification cs.AIcs.SE
keywords knowledge-augmentedgenerationlargelanguagemodelsGISdashboardsUIwireframetocodecontext-awarevisualpromptingretrieval-augmentedReactframeworksoftwaredesignpatterns
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a knowledge-augmented pipeline can take a user's wireframe sketch, drawn in a tool like PowerPoint or Adobe Illustrator, and turn it into a working React-based GIS dashboard with little or no hand-written front-end code. The idea is to retrieve software-engineering conventions, GIS domain knowledge, and sample code from a curated knowledge base, then feed that context to a large language model through structured, chain-of-thought prompts. The authors demonstrate the pipeline on two dashboards—one for meteorological time-series and site data, one overlaying wind-turbine locations with land-cover rasters—and argue that the generated code is modular, maintainable, and follows industry patterns like MVVM and Separation of Concerns. If the claim holds, it would lower the barrier for domain scientists who need custom environmental and urban data interfaces but do not have dedicated web-development expertise.

What carries the argument

Context-Aware Visual Prompting is the method that carries the argument: Python scripts read SVG wireframes, treat each annotated rectangle as a GUI component, compute its position, size, and style, and convert the whole layout into structured prompts with plain-language annotations. Around that sits a knowledge base and code base implemented as a knowledge graph with vector embeddings, storing library metadata, package-management commands, software-design patterns, and sample code; retrieval-augmented prompts then drive an iterative LLM code-generation loop that enforces React file-structure conventions and Separation of Concerns, that is, the practice of keeping UI, logic, and data handling in distinct modules.

What would settle it

Give the framework a wireframe for a dashboard type with components and libraries not present in the knowledge base, such as a real-time 3D terrain viewer or a network-graph view, and check whether the generated React project builds and runs without any human-written code beyond the wireframe annotations. A failure to produce a working application in that setting would show that the knowledge base, rather than the LLM, is doing the load-bearing work and that the claimed generality does not extend beyond the curated examples.

Watch

Extended reading notes

Core claim

The central claim is that LLM code generation for GIS front-ends becomes reliable enough for real use when the model is augmented, not fine-tuned, with a structured knowledge base built from previous GIS projects. The framework parses SVG wireframes into a component tree with positional and styling metadata, matches each component to libraries and design patterns stored in a knowledge graph, retrieves relevant sample code, and then guides an LLM through iterative, procedure-based prompts to write a complete React project. The paper presents this as a generalizable method: users express their intent visually, and the knowledge layer supplies the software engineering expertise that general-purpose LLMs lack. Two case studies are offered as evidence that the generated applications are functional, modular, and aligned with industry-standard architecture, while the authors acknowledge that human expert review and manual backend integration are still required.

Load-bearing premise

The load-bearing premise is that the knowledge base, whose sample code comes from previous GIS projects, is general enough to guide the LLM for new dashboards built from components and libraries outside that project set; the paper never tests an unseen dashboard type.

Editorial extensions

If this is right

  • A scientist with no front-end training could sketch a dashboard in presentation software and receive a deployable React project with map, chart, and form components wired together.
  • Because the generated code follows MVVM and Separation of Concerns, it can be extended with additional dashboards in the same project without rewriting the whole application.
  • Domain knowledge stored once in the knowledge base can be reused across projects, so each new dashboard benefits from previously curated library choices and code patterns.
  • The same knowledge-augmented prompting approach could be applied to other LLMs, making the method independent of any single model.
  • Automating the repetitive parts of dashboard development would let environmental researchers spend more effort on data analysis and less on UI/UX and web programming.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A decisive test the paper does not run is to feed the pipeline a dashboard type absent from the knowledge base, such as a real-time 3D scene or a graph analytics view; whether retrieval can still assemble a working app would separate the knowledge-base contribution from the LLM's general code ability.
  • The visual prompting stage is the most portable part of the design: any tool that can export annotated SVG wireframes could feed the same pipeline, so the method may transfer to non-GIS scientific dashboards once the knowledge base is extended.
  • The framework's automation is currently front-end only; pairing this approach with an automated backend and API generator would be the natural next step to make the full wireframe-to-application promise real, but the paper intentionally leaves backend development out of scope.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The manuscript presents a knowledge-augmented code-generation framework that converts SVG-based UI wireframes (created in PowerPoint or Illustrator) into structured visual prompts, retrieves software-engineering and GIS knowledge from a graph/vector knowledge base built from previous projects, and uses iterative LLM prompting to generate React-based GIS dashboards. A 12-task pipeline (T1–T12) covering visual prompting, knowledge-base construction, package management, iterative code generation, and React file organization is described. Two case studies are reported: a meteorological data dashboard and a wind-turbine/land-cover dashboard, both illustrated with screenshots. The paper is primarily an architectural and implementation report; Section 4.5 explicitly acknowledges the lack of comparative analysis, reliance on a single LLM, the need for human expert review, and the need for manual programming of customized functions.

Significance. If the framework works as claimed, it would provide a useful integration of existing techniques—RAG, chain-of-thought prompting, visual wireframe parsing, and React/MVVM conventions—for scientific dashboard development, lowering the entry barrier for domain scientists. The paper's strengths include a clear set of design requirements (R1–R6), a well-structured pipeline (T1–T12), separation of concerns enforced through React file organization, multi-tool package management, and an honest limitations section. However, the evidence presented is limited to two in-distribution demonstrations with screenshots; no quantitative evaluation, baseline comparison, or ablation is provided. The abstract and conclusion claim that the approach 'significantly reduces manual effort,' but this central practical claim is not supported by any measurement in the manuscript.

major comments (4)
  1. [Abstract, Section 4, Section 5] The claim that the framework 'significantly reduces manual effort' is load-bearing and unquantified. The paper reports no development-time measurements, no count of human edits or corrections, no code compilation/execution success rates, and no comparison against direct LLM prompting, prompt-template baselines, or human development. Because this is the central practical claim of the abstract and conclusion, it should either be measured with a concrete evaluation or removed and replaced with a more limited claim about the feasibility of the pipeline.
  2. [Section 3.3.2, Table 1, Section 4] The knowledge base is populated with sample code 'excerpted and refined from previous projects,' and both case studies use the same technology stack (React, Leaflet, time-series and raster visualization) already represented in Table 1. The demonstrations are therefore in-distribution retrievals from the authors' own project set; they do not test whether the framework can handle previously unseen dashboard types. The generalizability claim in Section 3.1 and the Conclusion requires at least one out-of-distribution case study or a held-out/ablated knowledge-base experiment to be supported.
  3. [Section 4] The evaluation is limited to screenshots of the generated dashboards. There are no metrics for visual fidelity to the wireframes, functional correctness, code quality, maintainability, or user effort, and no ablation that isolates the contributions of Context-Aware Visual Prompting, the knowledge base, or the CoT/RAG prompting strategy. This makes it impossible to attribute the observed outputs to the proposed components rather than to generic capabilities of the underlying LLM. A minimal experiment comparing the framework against plain prompting on the same wireframes would substantially strengthen the paper.
  4. [Section 3.3.3, Section 4.5] The manuscript acknowledges that human experts must review, debug, and revise the generated code and that customized functions require manual programming, yet Section 4 and the abstract describe the framework as 'autonomously' generating the dashboards. These statements are in tension. The paper should either define the precise scope of automation (e.g., standard components are generated, custom logic is hand-written) or soften the language to match the acknowledged human-in-the-loop requirements.
minor comments (5)
  1. [Figures 5 and 6] The explanatory callout text in Figures 5 and 6 is nearly identical, including the same route ('/home') and the same annotation example; the caption for Figure 6 should be tailored to the meteorological dashboard wireframe so the reader can understand what is unique about that input.
  2. [Section 3.3.2] The text mentions a 'vector index search on a Neo4j-backed knowledge graph' but does not specify the embedding model, vector index parameters, or similarity metric used. Adding one sentence with these details would improve reproducibility.
  3. [Table 1] Many rows in Table 1 cite the authors' prior projects. This is legitimate for provenance, but the table would be more informative if it indicated which sample-code entries were actually used in the two case studies.
  4. [Figure 2] Figure 2 is dense and central to understanding the framework; a short caption explaining the data and control flow through T1–T12 would help readers map the architecture to the text.
  5. [General] The text contains recurring spacing artifacts such as 'e fforts' and 'o ffers' that appear to be PDF-extraction remnants; these should be cleaned in the final version.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the framework is a RAG-based code generator whose outputs are newly generated React code, not the retrieved sample code; in-distribution evaluation and unquantified effort claims are validity concerns, not circular reasoning.

full rationale

This paper is a systems/prototyping contribution rather than a derivational one, so there is no equation-level chain in which an output reduces to an input by construction. The knowledge base is populated with sample code 'excerpted and refined from previous projects' (Section 3.3.2), and Table 1 lists prior projects, several authored by the present authors. But the framework's operation is explicitly RAG-based: sample code is retrieved and embedded into prompts to guide an LLM in writing new React components. The output is not defined as the retrieved sample code; the two case-study dashboards in Section 4 are new artifacts generated from user wireframes. The evaluation is indeed in-distribution and lacks a baseline, a human-effort measurement, or a wall-clock comparison, and Section 4.5 acknowledges that expert review and manual programming remain. Those are external-validity and overclaiming concerns, not circularity. No load-bearing argument reduces to a self-citation or to a fitted parameter; the only self-citation, e.g., Xu et al. 2024b for the Neo4j-backed knowledge graph in T5, is a minor implementation reference rather than the justification of the central claim. Therefore the paper receives a circularity score of 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

There are no numerical fitted parameters. The central claim rests on domain assumptions about knowledge-base sufficiency, LLM prompt-following, and user annotation quality, none of which are measured.

assumptions (3)
  • domain assumption The knowledge base curated from the authors' previous projects provides sufficient coverage for new dashboard generation tasks.
    Section 3.3.2 states sample code is excerpted and refined from previous projects; the framework's success depends on this coverage, which is untested beyond two case studies.
  • domain assumption LLMs can faithfully follow structured prompts to produce React code that compiles after expert review.
    The whole Step 3 pipeline assumes prompt-following reliability; the authors note in Section 4.5 that human review is still required, so this assumption is only partially satisfied.
  • domain assumption Users can express complete UI requirements through PowerPoint wireframes with text annotations.
    The visual prompting step relies on vector elements and annotations to capture layout and interactions; no evidence shows that non-expert users can create such wireframes effectively.

how reviews work

0 comments
Cite this review

Pith. "Pith review of From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework." pith.science (2026). https://pith.science/paper/A56MCVUX

@misc{pith2026250208756,
  author       = {Pith},
  title        = {Pith review of: From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs, Context-Aware Visual Prompting, and the React Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A56MCVUX}},
  note         = {Machine review of arXiv:2502.08756}
}
read the original abstract

Developing web-based GIS applications, commonly known as CyberGIS dashboards, for querying and visualizing GIS data in environmental research often demands repetitive and resource-intensive efforts. While Generative AI offers automation potential for code generation, it struggles with complex scientific applications due to challenges in integrating domain knowledge, software engineering principles, and UI design best practices. This paper introduces a knowledge-augmented code generation framework that retrieves software engineering best practices, domain expertise, and advanced technology stacks from a specialized knowledge base to enhance Generative Pre-trained Transformers (GPT) for front-end development. The framework automates the creation of GIS-based web applications (e.g., dashboards, interfaces) from user-defined UI wireframes sketched in tools like PowerPoint or Adobe Illustrator. A novel Context-Aware Visual Prompting method, implemented in Python, extracts layouts and interface features from these wireframes to guide code generation. Our approach leverages Large Language Models (LLMs) to generate front-end code by integrating structured reasoning, software engineering principles, and domain knowledge, drawing inspiration from Chain-of-Thought (CoT) prompting and Retrieval-Augmented Generation (RAG). A case study demonstrates the framework's capability to generate a modular, maintainable web platform hosting multiple dashboards for visualizing environmental and energy data (e.g., time-series, shapefiles, rasters) from user-sketched wireframes. By employing a knowledge-driven approach, the framework produces scalable, industry-standard front-end code using design patterns such as Model-View-ViewModel (MVVM) and frameworks like React. This significantly reduces manual effort in design and coding, pioneering an automated and efficient method for developing smart city software.

Figures

Figures reproduced from arXiv: 2502.08756 by the authors.

Figure 1
Figure 1. From annotated wireframe to code generation, a knowledge driven framework for automated software development of cyberGIS platform [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Overall framework architecture for LLM-driven code generation using knowledge-augmented prompting and structured domain knowl [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Structured knowledge representations for converting plain-language annotations from UI wireframes into structured prompts with tech [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: A procedure-based, knowledge-augmented code generation process that aligns with the conventions and best practices of the React [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]
Figure 5
Figure 5. Figure 5: The UI wireframe for the homepage of the web-based application includes thumbnails that serve as navigation links, directing users to [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]
Figure 6
Figure 6. Figure 6: The UI wireframe for the visual dashboard of Use Case I is designed for visualizing meteorological data, incorporating time-series data [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]
Figure 7
Figure 7. Figure 7: A demonstration of the dashboard’s capability to visualize meteorological data (e.g., shapfiles and time-series) for Use Case I. [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]
Figure 8
Figure 8. Figure 8: A demonstration of the dashboard’s capability to visualize land use and land cover raster data for Use Case II. [PITH_FULL_IMAGE:figures/full_fig_p022_8.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI

    cs.SE 2025-05 conditional novelty 3.0 of 10

    A qualitative taxonomy positions vibe coding and agentic coding as complementary paradigms rather than rivals in AI-assisted software development.

Reference graph

Works this paper leans on

79 extracted references · 70 canonical work pages · cited by 1 Pith paper

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

  2. [2]

    GIS Copilot: Towards an Autonomous GIS Agent for Spatial Analysis

    author Akinboyewa, T. , author Li, Z. , author Ning, H. , author Lessani, M.N. , year 2024 . title Gis copilot: Towards an autonomous gis agent for spatial analysis . journal arXiv preprint arXiv:2411.03205

    work page Pith review arXiv 2024

  3. [3]

    , author Pouke, M

    author Alatalo, T. , author Pouke, M. , author Koskela, T. , author Hurskainen, T. , author Florea, C. , author Ojala, T. , year 2017 . title Two real-world case studies on 3d web applications for participatory urban planning , in: booktitle Proceedings of the 22nd International Conference on 3D Web Technology , pp. pages 1--9

    work page 2017

  4. [4]

    , author Bellomarini, L

    author Baldazzi, T. , author Bellomarini, L. , author Ceri, S. , author Colombo, A. , author Gentili, A. , author Sallinger, E. , year 2023 . title Fine-tuning large enterprise language models via ontological reasoning , in: booktitle International Joint Conference on Rules and Reasoning , organization Springer . pp. pages 86--94

    work page 2023

  5. [5]

    , author LaClair, T.J

    author Berres, A.S. , author LaClair, T.J. , author Wang, C. , author Xu, H. , author Ravulaparthy, S. , author Todd, A. , author Tennille, S.A. , author Sanyal, J. , year 2021 a. title Multiscale and multivariate transportation system visualization for shopping district traffic and regional traffic . journal Transportation Research Record volume 2675 , p...

    work page 2021

  6. [6]

    , author Xu, H

    author Berres, A.S. , author Xu, H. , author Tennille, S.A. , author Severino, J. , author Ravulaparthy, S. , author Sanyal, J. , year 2021 b. title Explorative visualization for traffic safety using adaptive study areas . journal Transportation research record volume 2675 , pages 51--69

    work page 2021

  7. [7]

    , author Mann, B

    author Brown, T. , author Mann, B. , author Ryder, N. , author Subbiah, M. , author Kaplan, J.D. , author Dhariwal, P. , author Neelakantan, A. , author Shyam, P. , author Sastry, G. , author Askell, A. , et al., year 2020 . title Language models are few-shot learners . journal Advances in neural information processing systems volume 33 , pages 1877--1901

    2020

  8. [8]

    , author Hasan, S.S

    author Chinthavali, S. , author Hasan, S.S. , author Yoginath, S. , author Xu, H. , author Nugent, P. , author Jones, T. , author Engebretsen, C. , author Olatt, J. , author Tansakul, V. , author Christopher, C. , et al., year 2022 . title An alternative timing and synchronization approach for situational awareness and predictive analytics , in: booktitle...

    work page 2022

Show all 79 references
  1. [9]

    , author Hernandez-Suarez, A

    author Corona-Fraga, P. , author Hernandez-Suarez, A. , author Sanchez-Perez, G. , author Toscano-Medina, L.K. , author Perez-Meana, H. , author Portillo-Portillo, J. , author Olivares-Mercado, J. , author Garc \' a Villalba, L.J. , year 2025 . title Question--answer methodolo...

    2025

  2. [10]

    , author W \"o ssner, U

    author Dembski, F. , author W \"o ssner, U. , author Letzgus, M. , author Ruddat, M. , author Yamu, C. , year 2020 . title Urban digital twins for smart cities and citizens: The case study of herrenberg, germany . journal Sustainability volume 12 , pages 2307

    2020

  3. [11]

    , author Gokkaya, B

    author Fan, A. , author Gokkaya, B. , author Harman, M. , author Lyubarskiy, M. , author Sengupta, S. , author Yoo, S. , author Zhang, J.M. , year 2023 . title Large language models for software engineering: Survey and open problems , in: booktitle 2023 IEEE/ACM International ...

    2023

  4. [12]

    , author Sanchez, H.A

    author Fayad, M. , author Sanchez, H.A. , author Hegde, S.G. , author Basia, A. , author Vakil, A. , year 2015 . title Software patterns, knowledge maps, and domain analysis . publisher CRC Press

    2015

  5. [13]

    , author Casals, M

    author Ferr \'e -Bigorra, J. , author Casals, M. , author Gangolells, M. , year 2022 . title The adoption of urban digital twins . journal Cities volume 131 , pages 103905

    2022

  6. [14]

    , author Aryal, J

    author Garg, S. , author Aryal, J. , author Wang, H. , author Shah, T. , author Kecskemeti, G. , author Ranjan, R. , year 2018 . title Cloud computing based bushfire prediction for cyber--physical emergency applications . journal Future Generation Computer Systems volume 79 , ...

    2018

  7. [15]

    , author Team, D.A

    author Ghosh, D.P. , author Team, D.A. , year 2024 . title Design orchestration of conveying system

    2024

  8. [16]

    , author Pragathi, R

    author Ghosh, R. , author Pragathi, R. , author Ullas, S. , author Borra, S. , year 2017 . title Intelligent transportation systems: A survey , in: booktitle 2017 International Conference on Circuits, Controls, and Communications (CCUBE) , organization IEEE . pp. pages 160--165

    2017

  9. [17]

    , author Chen, M

    author Gu, X. , author Chen, M. , author Lin, Y. , author Hu, Y. , author Zhang, H. , author Wan, C. , author Wei, Z. , author Xu, Y. , author Wang, J. , year 2023 . title On the effectiveness of large language models in domain-specific code generation . journal ACM Transactio...

    2023

  10. [18]

    , author Zhu, Q

    author Guo, D. , author Zhu, Q. , author Yang, D. , author Xie, Z. , author Dong, K. , author Zhang, W. , author Chen, G. , author Bi, X. , author Wu, Y. , author Li, Y. , et al., year 2024 . title Deepseek-coder: When the large language model meets programming--the rise of co...

    2024 arXiv

  11. [19]

    , author Chakraborty, T

    author Hadid, A. , author Chakraborty, T. , author Busby, D. , year 2024 . title When geoscience meets generative ai and large language models: Foundations, trends, and future challenges . journal Expert Systems , pages e13654

    2024

  12. [20]

    , author Shen, Z

    author Hou, S. , author Shen, Z. , author Liang, J. , author Zhao, A. , author Gui, Z. , author Li, R. , author Wu, H. , year 2024 a. title Can large language models generate geospatial code? journal arXiv preprint arXiv:2410.09738

    2024 arXiv

  13. [21]

    , author Shen, Z

    author Hou, S. , author Shen, Z. , author Zhao, A. , author Liang, J. , author Gui, Z. , author Guan, X. , author Li, R. , author Wu, H. , year 2024 b. title Geocode-gpt: A large language model for geospatial code generation tasks . journal arXiv preprint arXiv:2410.17031

    2024 arXiv

  14. [22]

    , author Zhao, Y

    author Hou, X. , author Zhao, Y. , author Liu, Y. , author Yang, Z. , author Wang, K. , author Li, L. , author Luo, X. , author Lo, D. , author Grundy, J. , author Wang, H. , year 2023 . title Large language models for software engineering: A systematic literature review . jou...

    2023

  15. [23]

    , author Nweke, H.F

    author Ikegwu, A.C. , author Nweke, H.F. , author Anikwe, C.V. , author Alo, U.R. , author Okonkwo, O.R. , year 2022 . title Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions . journal Cluster Computing...

    2022

  16. [24]

    , author Komeily, A

    author Jia, M. , author Komeily, A. , author Wang, Y. , author Srinivasan, R.S. , year 2019 . title Adopting internet of things for the development of smart buildings: A review of enabling technologies and applications . journal Automation in Construction volume 101 , pages 111--126

    2019

  17. [25]

    , year 2022

    author Kadupitige, J.C.S.K. , year 2022 . title Enhancing Molecular Dynamics Simulations with Machine Learning and Advanced Cyberinfrastructure . publisher Indiana University

    2022

  18. [26]

    , author Alrifaee, B

    author Kampmann, A. , author Alrifaee, B. , author Kohout, M. , author W \"u stenberg, A. , author Woopen, T. , author Nolte, M. , author Eckstein, L. , author Kowalewski, S. , year 2019 . title A dynamic service-oriented software architecture for highly automated vehicles , i...

    2019

  19. [27]

    , author Yoon, Y

    author Kim, D. , author Yoon, Y. , author Lee, J. , author Mago, P.J. , author Lee, K. , author Cho, H. , year 2022 . title Design and implementation of smart buildings: A review of current research trend . journal Energies volume 15 , pages 4278

    2022

  20. [28]

    , author Zimmermann, T

    author Kim, M. , author Zimmermann, T. , author DeLine, R. , author Begel, A. , year 2017 . title Data scientists in software teams: State of the art and challenges . journal IEEE Transactions on Software Engineering volume 44 , pages 1024--1038

    2017

  21. [29]

    , author Janssen, P

    author Lei, B. , author Janssen, P. , author Stoter, J. , author Biljecki, F. , year 2023 . title Challenges of urban digital twins: A systematic review and a delphi expert survey . journal Automation in Construction volume 147 , pages 104716

    2023

  22. [30]

    , author Zhang, H

    author Li, R. , author Zhang, H. , author Liu, C. , author Qian, Z.C. , author Zhang, L. , year 2022 . title Bibliometric and visualized analysis of user experience design research: From 1999 to 2019 . journal Sage Open volume 12 , pages 21582440221087266

    2022

  23. [31]

    , author Xu, H

    author Li, X. , author Xu, H. , author Huang, X. , author Guo, C. , author Kang, Y. , author Ye, X. , year 2021 . title Emerging geo-data sources to reveal human mobility dynamics during covid-19 pandemic: Opportunities and challenges . journal Computational Urban Science volu...

    2021

  24. [32]

    , author Xu, H

    author Li, X. , author Xu, H. , author Tupayachi, J. , author Omitaomu, O. , author Wang, X. , year 2024 . title Empowering cognitive digital twins with generative foundation models: Developing a low-carbon integrated freight transportation system . journal arXiv preprint arXi...

    2024 arXiv

  25. [33]

    , author Ning, H

    author Li, Z. , author Ning, H. , year 2023 . title Autonomous GIS: the next-generation AI-powered GIS . journal International Journal of Digital Earth volume 16 , pages 4668--4686

    2023

  26. [34]

    , author Badea, C

    author Liang, J.T. , author Badea, C. , author Bird, C. , author DeLine, R. , author Ford, D. , author Forsgren, N. , author Zimmermann, T. , year 2024 . title Can gpt-4 replicate empirical software engineering research? journal Proceedings of the ACM on Software Engineering v...

    2024

  27. [35]

    , author Padmanabhan, A

    author Liu, Y. , author Padmanabhan, A. , author Wang, S. , year 2015 . title Cybergis gateway for enabling data-rich geospatial research and education . journal Concurrency and Computation: Practice and Experience volume 27 , pages 395--407

    2015

  28. [36]

    a l \"a , M. , author H \

    author Liukko, V. , author Knappe, A. , author Anttila, T. , author Hakala, J. , author Ketola, J. , author Lahtinen, D. , author Poranen, T. , author Ritala, T.M. , author Set \"a l \"a , M. , author H \"a m \"a l \"a inen, H. , et al., year 2024 . title Chatgpt as a full-sta...

    2024

  29. [37]

    , author Camboim, S

    author Mahmoudi, H. , author Camboim, S. , author Brovelli, M.A. , year 2023 . title Development of a voice virtual assistant for the geospatial data visualization application on the web . journal ISPRS International Journal of Geo-Information volume 12 , pages 441

    2023

  30. [38]

    , author Zou, L

    author Mandal, D. , author Zou, L. , author Wilkho, R.S. , author Baig, F. , author Abedin, J. , author Zhou, B. , author Cai, H. , author Gharaibeh, N. , author Lam, N. , year 2024 . title Prime: A cybergis platform for resilience inference measurement and enhancement . journ...

    2024

  31. [39]

    , author Oucheikh, R

    author Mansourian, A. , author Oucheikh, R. , year 2024 . title Chatgeoai: Enabling geospatial analysis for public through natural language, with large language models . journal ISPRS International Journal of Geo-Information volume 13 , pages 348

    2024

  32. [40]

    , year 2024

    author Manuardi, D. , year 2024 . title From Images to Code: Leveraging Computer Vision and Large Language Models for Front-End Automation . Ph.D. thesis. Politecnico di Torino

    2024

  33. [41]

    , year 2002

    author McBreen, P. , year 2002 . title Software craftsmanship: The new imperative . publisher Addison-Wesley Professional

    2002

  34. [42]

    , year 2024

    author Mendoza Juan, Y. , year 2024 . title Development of a multi-agent, LLM-driven system to enhance human-machine interaction: integrating DSPy with modular agentic strategies and logical reasoning layers for the autonomous generation of smart contracts . Master's thesis. U...

    2024

  35. [43]

    , author Stadler, C

    author Meyer, L.P. , author Stadler, C. , author Frey, J. , author Radtke, N. , author Junghanns, K. , author Meissner, R. , author Dziwis, G. , author Bulert, K. , author Martin, M. , year 2023 . title Llm-assisted knowledge graph engineering: Experiments with chatgpt , in: b...

    2023

  36. [44]

    , author Carson, A

    author Muste, M. , author Carson, A. , author Xu, H. , author Mocanu, M. , year 2017 . title Community engagement in water resources planning using serious gaming , in: booktitle 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP...

    2017

  37. [45]

    , author Cabrero-Daniel, B

    author Nguyen-Duc, A. , author Cabrero-Daniel, B. , author Przybylek, A. , author Arora, C. , author Khanna, D. , author Herda, T. , author Rafiq, U. , author Melegati, J. , author Guerra, E. , author Kemell, K.K. , et al., year 2023 . title Generative artificial intelligence ...

    2023 arXiv

  38. [46]

    , author Lazarova-Molnar, S

    author Niloofar, P. , author Lazarova-Molnar, S. , author Omitaomu, F. , author Xu, H. , author Li, X. , year 2023 . title A general framework for human-in-the-loop cognitive digital twins , in: booktitle 2023 Winter Simulation Conference (WSC) , organization IEEE . pp. pages ...

    2023

  39. [47]

    , author Simonet, A

    author Parashar, M. , author Simonet, A. , author Rodero, I. , author Ghahramani, F. , author Agnew, G. , author Jantz, R. , author Honavar, V. , year 2019 . title The virtual data collaboratory: A regional cyberinfrastructure for collaborative data-driven research . journal C...

    2019

  40. [48]

    , author Nguyen, P.X

    author Phan, H.N. , author Nguyen, P.X. , author Bui, N.D. , year 2024 . title Hyperagent: Generalist software engineering agents to solve coding tasks at scale . journal arXiv preprint arXiv:2409.16299

    2024 arXiv

  41. [49]

    , author Zhang, Y

    author Sandberg, J. , author Zhang, Y. , year 2024 . title Evaluating the capabilities of gpt-4 in full-stack web development: A practical approach

    2024

  42. [50]

    , author Al-Mohammad, R

    author Shah, H. , author Al-Mohammad, R. , year 2024 . title Optimizing software validation efficiency and scalability through mass parallel testing techniques in complex development environments . journal International Journal of Intelligent Automation and Computing volume 7 ...

    2024

  43. [51]

    , author Pengpeng, Z

    author Shanjun, M. , author Pengpeng, Z. , author Haoyuan, Z. , author Jinchuan, C. , author Mei, L. , author Huazhou, C. , year 2024 . title Design and key technology research of industrial geographic information system . journal National Remote Sensing Bulletin volume 28 , p...

    2024

  44. [52]

    , author Wang, C

    author Shao, Y. , author Wang, C. , author Berres, A. , author Yoshioka, J. , author Cook, A. , author Xu, H. , year 2022 . title Computer vision-enabled smart traffic monitoring for sustainable transportation management , in: booktitle International Conference on Transportati...

    2022

  45. [53]

    , author Mead, C.J

    author Siddiqui, F.M. , author Mead, C.J. , year 2024 . title Digital twin conops as a platform for airport master planning , in: booktitle 2024 New Trends in Civil Aviation (NTCA) , organization IEEE . pp. pages 105--111

    2024

  46. [54]

    , author Hamilton, A

    author Skarlatidou, A. , author Hamilton, A. , author Vitos, M. , author Haklay, M. , year 2019 . title What do volunteers want from citizen science technologies? a systematic literature review and best practice guidelines . journal JCOM: Journal of Science Communication volume 18

    2019

  47. [55]

    , author Kesler, C

    author Souffront Alcantara, M.A. , author Kesler, C. , author Stealey, M.J. , author Nelson, E.J. , author Ames, D.P. , author Jones, N.L. , year 2018 . title Cyberinfrastructure and web apps for managing and disseminating the national water model . journal JAWRA Journal of th...

    2018

  48. [56]

    , author Yu, X.Y

    author Steckler, M. , author Yu, X.Y. , author Birdwell, K. , author Xu, H. , year 2025 . title Five years of quality-controlled meteorological surface data at oak ridge reserve in tennessee . https://doi.org/10.5281/zenodo.14744006, :10.5281/zenodo.14744006

    2025 doi

  49. [57]

    , author Li, Z.M

    author Sun, Y.X. , author Li, Z.M. , author Huang, J.Z. , author Yu, N.z. , author Long, X. , year 2023 . title GPT-4: the future of cosmetic procedure consultation? journal Aesthetic Surgery Journal volume 43 , pages NP670--NP672

    2023

  50. [58]

    , author Sparks, K

    author Thakur, G. , author Sparks, K. , author Berres, A. , author Tansakul, V. , author Chinthavali, S. , author Whitehead, M. , author Schmidt, E. , author Xu, H. , author Fan, J. , author Spears, D. , et al., year 2020 . title Covid-19 joint pandemic modeling and analysis p...

    2020

  51. [59]

    , author Xu, H

    author Tupayachi, J. , author Xu, H. , author Omitaomu, O.A. , author Camur, M.C. , author Sharmin, A. , author Li, X. , year 2024 . title Towards next-generation urban decision support systems through ai-powered construction of scientific ontology using large language models—...

    2024

  52. [60]

    , author Deng, Y

    author Xia, C.S. , author Deng, Y. , author Dunn, S. , author Zhang, L. , year 2024 . title Agentless: Demystifying llm-based software engineering agents . journal arXiv preprint arXiv:2407.01489

    2024 arXiv

  53. [61]

    , author Chen, Y

    author Xiao, S. , author Chen, Y. , author Li, J. , author Chen, L. , author Sun, L. , author Zhou, T. , year 2024 . title Prototype2code: End-to-end front-end code generation from ui design prototypes , in: booktitle International Design Engineering Technical Conferences and ...

    2024

  54. [62]

    , author Berres, A

    author Xu, H. , author Berres, A. , author Liu, Y. , author Allen-Dumas, M.R. , author Sanyal, J. , year 2022 a. title An overview of visualization and visual analytics applications in water resources management . journal Environmental Modelling & Software volume 153 , pages 105396

    2022

  55. [63]

    , author Berres, A

    author Xu, H. , author Berres, A. , author Shao, Y. , author Wang, C.R. , author New, J.R. , author Omitaomu, O.A. , year 2023 a. title Toward a smart metaverse city: Immersive realism and 3d visualization of digital twin cities . journal Advances in Scalable and Intelligent G...

    2023

  56. [64]

    , author Berres, A

    author Xu, H. , author Berres, A. , author Tennille, S.A. , author Ravulaparthy, S.K. , author Wang, C. , author Sanyal, J. , year 2021 a. title Continuous emulation and multiscale visualization of traffic flow using stationary roadside sensor data . journal IEEE Transactions ...

    2021

  57. [65]

    , author Berres, A

    author Xu, H. , author Berres, A. , author Thakur, G. , author Sanyal, J. , author Chinthavali, S. , year 2021 b. title Episemblevis: A geo-visual analysis and comparison of the prediction ensembles of multiple covid-19 models . journal Journal of biomedical informatics volume...

    2021

  58. [66]

    , author Berres, A

    author Xu, H. , author Berres, A. , author Wang, C.R. , author LaClair, T.J. , author Sanyal, J. , year 2021 c. title Visualizing vehicle acceleration and braking energy at intersections along a major traffic corridor , in: booktitle Proceedings of the Twelfth ACM Internationa...

    2021

  59. [67]

    , author Berres, A

    author Xu, H. , author Berres, A. , author Yoginath, S.B. , author Sorensen, H. , author Nugent, P.J. , author Severino, J. , author Tennille, S.A. , author Moore, A. , author Jones, W. , author Sanyal, J. , year 2023 b. title Smart mobility in the cloud: Enabling real-time si...

    2023

  60. [68]

    , author Boyaci, A

    author Xu, H. , author Boyaci, A. , author Lian, J. , author Wilson, A. , year 2024 a. title Explainable ai for multivariate time series pattern exploration: Latent space visual analytics with temporal fusion transformer and variational autoencoders in power grid event diagnos...

    2024

  61. [69]

    , author Demir, I

    author Xu, H. , author Demir, I. , author Koylu, C. , author Muste, M. , year 2019 a. title A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation . journal Science of the Total Environment volume 692 , pages 806--817

    2019

  62. [70]

    , author Li, X

    author Xu, H. , author Li, X. , author Tupayachi, J. , author Lian, J.J. , author Omitaomu, O.A. , year 2024 b. title Automating bibliometric analysis with sentence transformers and retrieval-augmented generation (rag): A pilot study in semantic and contextual search for custo...

    2024

  63. [71]

    , author Liu, X

    author Xu, H. , author Liu, X. , author Lian, J. , author Li, Y. , author Malhotra, M. , author Omitaomu, O.A. , year 2022 b. title A geo-visual analysis for exploring the socioeconomic benefits of the heating electrification using geothermal energy , in: booktitle Proceedings...

    2022

  64. [72]

    , author Muste, M

    author Xu, H. , author Muste, M. , author Demir, I. , year 2019 b. title Web-based geospatial platform for the analysis and forecasting of sedimentation at culverts . journal Journal of Hydroinformatics volume 21 , pages 1064--1081

    2019

  65. [73]

    , author Shao, Y

    author Xu, H. , author Shao, Y. , author Chen, J. , author Wang, C. , author Berres, A. , year 2024 c. title Semi-automatic geographic information system framework for creating photo-realistic digital twin cities to support autonomous driving research . journal Transportation ...

    2024

  66. [74]

    , author Wang, C

    author Xu, H. , author Wang, C. , author Berres, A. , author LaClair, T. , author Sanyal, J. , year 2022 c. title Interactive web application for traffic simulation data management and visualization . journal Transportation research record volume 2676 , pages 274--292

    2022

  67. [75]

    , author Windsor, M

    author Xu, H. , author Windsor, M. , author Muste, M. , author Demir, I. , year 2020 . title A web-based decision support system for collaborative mitigation of multiple water-related hazards using serious gaming . journal Journal of environmental management volume 255 , pages 109887

    2020

  68. [76]

    , author Yuan, J

    author Xu, H. , author Yuan, J. , author Berres, A. , author Shao, Y. , author Wang, C. , author Li, W. , author LaClair, T.J. , author Sanyal, J. , author Wang, H. , year 2023 c. title A mobile edge computing framework for traffic optimization at urban intersections through c...

    2023

  69. [77]

    , author Ibarra, J.E

    author Yu, Y. , author Ibarra, J.E. , author Kumar, K. , author Chergarova, V. , year 2021 . title Coevolution of cyberinfrastructure development and scientific progress . journal Technovation volume 100 , pages 102180

    2021

  70. [78]

    , author Chen, Z

    author Zhang, S. , author Chen, Z. , author Shen, Y. , author Ding, M. , author Tenenbaum, J.B. , author Gan, C. , year 2023 . title Planning with large language models for code generation . journal arXiv preprint arXiv:2303.05510

    2023 arXiv

  71. [79]

    , author Wang, Z

    author Zhang, Y. , author Wang, Z. , author He, Z. , author Li, J. , author Mai, G. , author Lin, J. , author Wei, C. , author Yu, W. , year 2024 . title Bb-geogpt: A framework for learning a large language model for geographic information science . journal Information Process...

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.