AI-Generated Content in Landscape Architecture: A Survey

Philip S. Yu; Qidi Chen; Wensheng Gan; Yue Xing

REVIEW 3 major objections 4 minor 194 references

AI-Generated Content in Landscape Architecture: A Survey

T0 review · 3 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

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

Pith's one-line read This survey argues that AI-generated content can support landscape architecture across the entire design process, and it catalogs the technologies, platforms, and challenges at each stage.

desk verdict A useful but undisciplined survey: the stage-by-stage map of AIGC in landscape architecture is inflated because many cited examples are predictive or optimization ML, not content generation. read the letter →

arxiv 2503.16435 v1 pith:G3WPGFET submitted 2025-02-12 cs.HC

Yue Xing , Wensheng Gan , Qidi Chen , Philip S. Yu This is my paper · ORCID

classification cs.HC

keywords artificialintelligenceAI-generatedcontentlandscapearchitecturegenerativedesignadversarialnetworksparametricplantconfigurationconstructionmanagement

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 survey argues that AI-generated content (AIGC) is not merely a rendering aid but can support landscape architecture at every stage of the design process, from site research and analysis through concept generation, parameterized optimization, plant selection, and construction management. The paper organizes the landscape-architecture workflow into five application areas and connects each to concrete generative technologies, including GANs, diffusion models, transformers, and parametric optimization algorithms, and to deployed platforms such as Stable Diffusion, Midjourney, and BIM/LIM-based systems. It also enumerates obstacles, including data quality, professional judgment, technical limits, site sustainability, user engagement, and ethics, and sketches future trends in interdisciplinary integration and regulation. The value of the paper lies in its structured map of an emerging application space, not in experimental proof that any single tool works.

What carries the argument

The organizing object is the AIGC-based landscape design process (Figure 2), a pipeline that runs from data retrieval and analysis through concept generation, evaluation, iterative optimization, and construction simulation. The technological backbone is generative modeling—GANs, variational autoencoders, diffusion models, and transformers—combined with parametric optimization algorithms (genetic algorithms, particle swarm optimization, simulated annealing) and with building/landscape information modeling (BIM/LIM), which carries the digital model into construction and operation. Table 2 pairs each technology with its landscape use, and Table 3 catalogs deployed platforms; together these tables constitute the paper's evidence that each claimed application corresponds to an existing tool or study.

What would settle it

Audit the five categories against the cited instances: if construction management turns out to rest only on BIM/LIM systems rather than on generative content creation, or if most cited uses are confined to text-to-image concept sketching, the claim that AIGC spans the whole design process loses support. A structured survey with a documented search protocol and explicit inclusion criteria would similarly test whether the five application areas actually cover the published record.

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Extended reading notes

Core claim

The paper's central claim is that AIGC's role in landscape architecture is structural rather than ornamental: it enters the design chain at every phase. Specifically, it identifies five application areas: site research and analysis (terrain interpolation, solar radiation prediction, data integration, decision support, risk assessment); design concept and scheme generation (image synthesis, style transfer, layout generation); parameterized design optimization (parametric modeling, genetic and particle-swarm algorithms, multi-objective optimization); plant configuration and simulation (plant databases, growth simulation, pest detection, VR/AR presentation); and construction management and optimization (BIM/LIM integration, digital twins, construction risk management). These five areas are presented as connected stages of a whole-process AIGC-based design workflow, with data retrieval, concept generation, evaluation against cost and design constraints, iterative optimization, and construction simulation forming one chain.

Load-bearing premise

The paper assumes that the platforms, case studies, and examples it selected (especially in Section 4 and Table 3) fairly represent the full range of AIGC use in landscape architecture, because it states no systematic search protocol or inclusion criteria.

Editorial extensions

If this is right

Landscape firms could adopt one AI-augmented workflow that runs from site analysis to construction handover, rather than using generative tools only for client renderings.
Landscape architecture curricula would need to add AI literacy, prompt engineering, and data-quality training to prepare students for this new process.
Reliable site and plant data, data standards, and ethics or regulation become binding constraints at every stage, not just at the visualization step.
BIM/LIM integration becomes a natural extension of generative design, pointing toward digital-twin-based construction management and post-occupancy operation.
The balance between automated generation and human aesthetic judgment stays the central human factor, since the paper's own challenge list puts creativity and site judgment first.

Reading between the lines

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

Editorial: the five-category map can be used as a gap-finding device; the categories with the thinnest cited evidence—likely construction management—mark where the paper's claimed coverage runs ahead of the deployments it documents.
Editorial: the survey's heavy reliance on image-generation platforms suggests that AIGC's near-term practical strength in landscape architecture is visual communication, while analytical tasks are carried more by classical algorithms than by content generation.
Editorial: a natural next step would be a benchmark study that runs a fixed design brief through the platforms in Table 3 and compares outputs on feasibility, ecological fit, and client comprehension.
Editorial: if the whole-process claim is correct, the next wave of research should measure productivity and design-quality differences between AIGC-assisted and traditional workflows, something the survey itself does not attempt.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Worth a look if you need a map of where generative AI is being tried in landscape architecture, but don't cite it for the whole-process claim. The paper's real value is organizational: it collects a large set of tools, platforms, and examples, and lists them against design stages (site analysis, concept generation, parametric optimization, planting, construction). That is handy for practitioners who want names and entry points. It also gives a reasonable, if generic, catalogue of challenges and trends. Nothing here is new in method or evidence; it is a restructured combination of the authors' earlier surveys on AI in landscape architecture and AIGC generally. That overlap is not fatal by itself, but the paper never clearly positions itself relative to those sources, and it leans on them heavily.

The soft spot that matters is construct validity. The paper defines AIGC as generation of new content via GANs, diffusion models, and similar. Then it populates the application sections with examples that are not generative: an i-Tree Eco plus self-organizing map tree assessment, a CNN that predicts solar performance, a random forest biomass model, and genetic algorithms used for parametric optimization. Those are analytics, prediction, or search, not content generation. Table 3 also includes Sefaira and VIM, which are energy analysis and BIM data management, not AIGC. If you take the definition seriously, the central claim that AIGC supports every stage of landscape design does not hold for the cited evidence, at least not to the degree claimed. That is a load-bearing flaw, not a style quibble.

There is also no stated search protocol or inclusion criteria, so the selection of examples and platforms is hard to audit. Some claims like 'AIGC improves design efficiency' appear without numbers or a specific study. These are real weaknesses, but they are fixable: narrow the claims to what the evidence supports, relabel non-generative tools as adjacent AI, and document the survey method.

All told, for a reader who wants a directory of AI applications in landscape architecture, this is a passable starting point. For a reader who wants a rigorous analysis of what AIGC actually does in the field, it will disappoint. I would not desk-reject it outright: with a major revision that fixes the category conflation and adds methodology, it could be a serviceable survey. As it stands, I would not cite it for the strong version of the claim.

Referee Report

3 major / 4 minor

Summary. This paper surveys AI-generated content (AIGC) applications in landscape architecture (LA). It argues that AIGC can support the entire LA design process, covering site research and analysis, design concept and scheme generation, parametric design optimization, plant configuration and simulation, and construction management and optimization. The paper reviews key AIGC technologies, compiles related platforms and software in Table 3, and discusses challenges and future trends.

Significance. If the construct-validity and methodological issues were resolved, this survey could provide a useful structured map of how generative AI is entering landscape architecture, particularly for practitioners and researchers seeking an overview. The paper's strengths include its broad coverage of the design process, the compilation of relevant platforms in Table 3, and its summary of challenges in Section 5. However, the current conflation of predictive/optimization ML with AIGC and the absence of a stated survey methodology prevent the central claim from being accepted as written.

major comments (3)

[Sections 4.1, 4.3, 4.4 and Table 3] Many cited applications are not AIGC under the paper's own definition in Section 3.1 (content generation via GANs, pre-trained models, etc.). Examples include the Taipei tree study using i-Tree Eco and self-organizing maps [161], the CNN-based solar radiation prediction [61], the random-forest biomass and canopy-cover model [8], and the genetic-algorithm-based optimization on Grasshopper [63]; these are clustering, prediction, and search methods rather than content generation. Table 3 also lists Sefaira (energy analysis) and VIM (BIM data management) as platforms without noting that they are not generative AI tools. Consequently, the central claim that AIGC supports the entire LA design process is not established for the affected stages; either reclassify these examples as broader 'AI-enabled' applications or restrict the survey's claims to genuinely generative methods.
[Section 1] The paper announces a review but does not state a survey methodology: there is no description of the databases searched, search terms, inclusion/exclusion criteria, year range, or quality assessment used to select the examples and platforms in Sections 3 and 4 and Table 3. Without such a protocol, the representativeness of the sample is an unexamined assumption, and the survey's conclusions cannot be reproduced.
[Sections 1 and 3.2] Repeated assertions that AIGC 'improves design efficiency,' 'optimizes design solutions,' and 'automates key decision-making' are not supported with quantitative evidence or specific effect sizes from the cited primary studies. As a survey, the paper should either cite empirical evaluations (e.g., time savings, accuracy comparisons) or explicitly frame these statements as qualitative themes in the literature rather than established facts.

minor comments (4)

[Section 3.1] The definition of AIGC includes 'literature indexing' as a technology; this is non-standard and should be clarified or removed.
[Table 3] The caption describes 'platforms, systems, and software' but does not distinguish between genuine AIGC tools (e.g., Midjourney, Stable Diffusion) and adjacent non-generative tools (e.g., Sefaira, VIM); consider adding a 'Type' column to clarify each platform's relationship to AIGC.
[Section 4.4(2)] The StyleGAN flower-image application [168] is presented under plant generation design and simulation, but it is an image-generation study rather than a plant configuration or simulation study; please clarify the direct connection to landscape architecture.
[Section 4.2(1)] Figure 4 is referenced but not fully discussed in the text; please cite it at the specific point where the diffusion-based image synthesis workflow is described.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: this survey's claims rest on externally cited, independently published case studies, and it contains no equations, fits, or predictions that could reduce to its inputs. The dense self-citation is definitional and framing-only; the over-inclusive labeling of predictive ML tools as AIGC is a construct-validity caveat, not circularity.

full rationale

This paper is an organizing survey rather than a derivation: it maps externally published applications onto stages of the landscape-architecture design process (Sections 4.1-4.5) and summarizes challenges and trends (Section 5). There are no equations, no fitted parameters, and no first-principles predictions, so the classical circularity patterns (self-definitional equations; fitted inputs renamed as predictions) cannot arise. The central claim, that AIGC supports site analysis, scheme generation, parametric optimization, plant configuration, and construction management, is carried by roughly fifty external references to concrete systems and studies (e.g., conditional-GAN terrain interpolation [186], StyleGAN/Pix2Pix street-view synthesis [143], Graph2Plan [72], FloorplanGAN [103]), all independent of the present authors. The citations to the authors' own prior work ([165], [166], [169], [175], [56], [91], [184]) are confined to definitional and framing roles: the definition of AIGC anchors to the authors' own survey [165] (Section 3.1), and the statement that LA 'has begun to focus on and apply AI technology' cites their own LA survey [169] (Section 1). These anchors do not reduce any LA-specific claim to an input, because the AIGC definition (GANs, pre-trained models, content generation) is standard and externally verifiable, and Section 4's evidence stands independently of it. The closest concern is category over-inclusion: Section 4 labels predictive and analytical tools as AIGC even though they do not generate content under the paper's own Section 3.1 definition (CNN-based solar-performance prediction [61,67], i-Tree Eco with self-organizing maps [161], random-forest biomass and canopy-cover modeling [8], genetic-algorithm optimization [63]). That mismatch is a construct-validity and correctness problem, not circularity: the cited studies are real external work, so the 'whole-process AIGC' claim is over- rather than under-determined by its evidence. The paper also candidly limits its own subject in Section 5.1.3 ('Currently, AI is primarily used for generating images and cannot perform fine-grained modeling'), confirming that the survey is an organization of external findings rather than a conclusion forced by definition or self-citation. Score 2 reflects the noticeable but non-load-bearing self-citation; no step warrants 6 or higher, since nothing is fitted and no derivation is self-referential.

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

This is a survey paper with no free parameters and no invented entities. Its central claims rest on domain assumptions about the value of AI in design and on an unverified assumption about representativeness of the surveyed examples.

assumptions (3)

domain assumption Traditional landscape design relies on subjective experience and lacks objective evaluation criteria.
Stated in the Abstract and Section 2.1 as the central motivation for why AIGC is needed. Presented as fact without empirical evidence.
domain assumption Data-driven AI provides an objective and rational design process.
Repeated in the Abstract and Section 2.1. This assumed contrast between subjective humans and objective AI underpins the entire positive framing of AIGC.
ad hoc to paper The selected examples and platforms are a representative sample of AIGC applications in landscape architecture.
No search protocol or selection criteria are given. The choice of case studies and the platforms in Table 3 appears arbitrary and is an unexamined assumption.

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Cite this review

Pith. "Pith review of AI-Generated Content in Landscape Architecture: A Survey." pith.science (2026). https://pith.science/paper/G3WPGFET

@misc{pith2026250316435,
  author       = {Pith},
  title        = {Pith review of: AI-Generated Content in Landscape Architecture: A Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G3WPGFET}},
  note         = {Machine review of arXiv:2503.16435}
}

read the original abstract

Landscape design is a complex process that requires designers to engage in intricate planning, analysis, and decision-making. This process involves the integration and reconstruction of science, art, and technology. Traditional landscape design methods often rely on the designer's personal experience and subjective aesthetics, with design standards rooted in subjective perception. As a result, they lack scientific and objective evaluation criteria and systematic design processes. Data-driven artificial intelligence (AI) technology provides an objective and rational design process. With the rapid development of different AI technologies, AI-generated content (AIGC) has permeated various aspects of landscape design at an unprecedented speed, serving as an innovative design tool. This article aims to explore the applications and opportunities of AIGC in landscape design. AIGC can support landscape design in areas such as site research and analysis, design concepts and scheme generation, parametric design optimization, plant selection and visual simulation, construction management, and process optimization. However, AIGC also faces challenges in landscape design, including data quality and reliability, design expertise and judgment, technical challenges and limitations, site characteristics and sustainability, user needs and participation, the balance between technology and creativity, ethics, and social impact. Finally, this article provides a detailed outlook on the future development trends and prospects of AIGC in landscape design. Through in-depth research and exploration in this review, readers can gain a better understanding of the relevant applications, potential opportunities, and key challenges of AIGC in landscape design.

Figures

Figures reproduced from arXiv: 2503.16435 by the authors.

**Figure 1.** The outline of our overview. With the acceleration of digital transformation, LA needs to adopt digital tools and technologies such as building information modeling (BIM) [75, 174], landscape information modeling (LIM) [2, 3], and AIGC to improve efficiency, accuracy, and visualization capabilities [16]. Unified standards and specifications are beneficial for project management, better coordination among relevant … view at source ↗

**Figure 2.** LA design process based on AIGC. recognize objects, scenes, and motion in images, thereby enabling applications such as image search, image recognition, image restoration, intelligent monitoring, scene reconstruction, and augmented reality. The Vision Transformer (ViT) architecture [86] has demonstrated more powerful capabilities compared to traditional CNN-based techniques. Building upon the general architecture,… view at source ↗

**Figure 3.** Site analysis extraction of street view greening images may inevitably have errors, such as when RGB color recognition confuses plant shadows with artificial colors in the street (e.g., billboards and wall surfaces). Generative adversarial network (GAN)-based models optimize satellite image resolution and address cloud cover issues. Satellite imagery has shown its indispensable value in large-scale landscape planni… view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Image synthesis of various technologies, such as machine learning combined with other techniques, deep learning, CNN, generative models, image recognition, image transfer, and more. Examples of these techniques include conditional GANs [108], flow-based models [82], …

**Figure 5.** Figure 5: Parametric modeling generates floor plans in vector format and uses CNN to visually differentiate the generated floor plans in a grid format. The native vector format generated by this algorithm ensures accuracy in editing, so the generated samples are represented by …

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