Automated Label Placement on Maps via Large Language Models

Harry Shomer; Jiejun Xu

REVIEW 3 major objections 4 minor 34 references

Automated Label Placement on Maps via Large Language Models

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Instruction-tuned LLMs, prompted with retrieved cartographic guidelines, place map labels with 28–42 pixel RMSE on the new MAPLE benchmark, versus 81–165 pixels untuned — evidence that label placement can be treated as a data-editing task.

desk verdict A genuinely new benchmark and task framing for LLM-based label placement, but the OCR-derived ground truth is unvalidated and the headline claim about cartographic alignment outruns the evidence. read the letter →

arxiv 2507.22952 v2 pith:4DYVNXTU submitted 2025-07-29 cs.HC cs.CVcs.LG

Harry Shomer , Jiejun Xu This is my paper · ORCID

classification cs.HCcs.CVcs.LG

keywords automaticlabelplacementlargelanguagemodelsretrieval-augmentedgenerationmapfinishinginstructiontuningMAPLEdatasetcartographicguidelinespoint-featurelabeling

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 tries to establish that a large language model, given the right textual labeling guidelines and a small amount of fine-tuning, can place landmark labels on real maps almost as accurately as a practiced eye. The authors build MAPLE, a benchmark of 100 OpenStreetMap-derived maps with over 1,000 landmarks from three cities, and use retrieval-augmented generation to feed each landmark the relevant section of NGA map-labeling standards. After instruction tuning with LoRA, four open-source LLMs reduce root-mean-square placement error from roughly 81–165 pixels to 28–42 pixels. The significance, if true, is that map finishing can be steered by human-readable standards instead of hand-tuned geometric rule engines.

What carries the argument

The machinery is a retrieval-augmented prompt combined with instruction tuning. NGA labeling guidelines are chunked by section, embedded with nomic-embed-text, and stored in a vector database; for each landmark, the top-k instructions are retrieved using the landmark name and type, concatenated into a prompt that also carries the landmark's boundary coordinates in one of four formats (list, JSON, CSS, XML), and the LLM outputs an (X,Y) coordinate. The response is tuned with QLoRA on (prompt, ground-truth-coordinate) pairs, so the model learns the spatial convention from the dataset rather than from a hand-written placement algorithm. Neighboring landmarks within 50 px can be appended to the prompt, though the paper finds this rarely helps.

What would settle it

Take a sample of MAPLE maps, have a cartographer or the underlying QGIS/PAL engine mark the true label positions, and recompute the RMSE of the tuned models against those positions instead of the OCR-derived union boxes; if the two error values diverge substantially, the reported accuracy is an artifact of the ground-truth extraction rather than genuine cartographic alignment.

Watch

Extended reading notes

Core claim

The central claim is that label placement, normally a geometric optimization or rule-engineering problem, can be reframed as a structured data-editing task that LLMs can learn. The paper's evidence is that on the MAPLE test set, instruction-tuned Llama3.1, Gemma2, Qwen3, and Phi-4 produce label coordinates with RMSE of 28.4–41.9 pixels, while the same models without tuning score 81.2–165.0 pixels. The authors attribute the gain to a prompt that includes landmark name, type, boundary coordinates, and the retrieved NGA guidelines, plus LoRA tuning on 883 training landmarks. They conclude LLMs can align generated outputs with expert cartographic standards in this setting.

Load-bearing premise

The benchmark assumes that the text boxes recovered by automated text detection and recognition really are where the map creator put each label; the authors never validate those boxes against human cartographer annotations, so if the recovery is biased, the reported error numbers measure the recovery pipeline rather than genuine placement quality.

Editorial extensions

If this is right

A map-labeling system can be updated by editing a text guideline instead of reconfiguring a rule engine, because the guidelines enter the model as retrieved prose.
Instruction tuning on a few hundred examples is sufficient to move open LLMs from unusable to practically accurate on this task.
Coordinate representation matters: list format is most consistent without tuning, XML wins after tuning, and CSS is worst, so prompt format is a real accuracy lever.
Performance is uneven across landmark types — shops and offices become quite accurate, while leisure and building labels remain hardest — so the method's readiness is type-dependent.
Adding neighboring landmarks as prompted context does not reliably improve placement and often degrades it, suggesting context handling needs a different design.

Reading between the lines

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

If the MAPLE ground truth were validated against human cartographer placements or the QGIS/PAL engine positions, and the RMSE numbers were recomputed against those, the absolute error values might shift; the relative gain from tuning would likely survive, but the claim of alignment with expert standards depends on this check.
Because the model only sees coordinates and text, the approach is most natural for point and area features; line features like roads and rivers, whose labels curve, would likely require visual input or a different coordinate representation.
The failure of neighboring context suggests that conflict resolution, the heart of classic automatic label placement, is not yet handled by this prompt; a hybrid that consults a spatial index for conflicts before prompting would be a testable extension.
The same recipe — retrieve standards, prompt with structured slots, tune on a small set — should transfer to other structured data-editing tasks such as chart annotation, diagram labeling, or CAD dimension placement.

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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

Two things to know. The paper delivers the first open benchmark for automatic label placement on real maps (MAPLE) and applies LLM+RAG to it. Instruction tuning drops RMSE from the 81–165 range to 28–42, so the method does something real. But the ground truth those numbers measure is OCR recovery of QGIS-rendered text, never checked against the actual PAL placements or human annotations. That makes the central claim about "alignment with expert cartographic standards" a bridge too far.

What's genuinely good: the dataset construction is described in enough detail to reproduce, the code and data are promised on GitHub, and the authors try multiple coordinate formats, per-type results, and a neighbor-context variant. The honest finding that neighboring context mostly hurts is a nice touch. Baselines, however, are missing entirely. There is no Maplex, no PAL, no simple heuristic, no landmark-centroid predictor. So 28–42 px is impossible to interpret. A centroid baseline would at least give a scale for what "accurate" means.

The soft spots are soft in proportion. The ground-truth issue is the load-bearing one. Section 3.2 assigns words to landmarks with a 50 px proximity threshold and 80% Levenshtein match, then unions boxes. The authors report a successful recovery for about 87% of maps and take that as validation, but success rate is not accuracy. Recovered boxes are never compared with what QGIS actually rendered, which the authors say is not exposed through their API. If OCR clips or expands boxes, or if the 13% unrecovered maps are systematically harder, every RMSE in Tables 3–5 is distorted. This is fixable: sample a few hundred maps, overlay the OCR boxes on the original images, and have a human check whether each box corresponds to the intended label. Also add error bars and at least one non-LLM baseline. The RAG component is not ablated; that should also be tested.

On the reader's take: I agree with the conditional verdict. The stress-test note lands. This is not a fatal flaw because the dataset may still be usable as a benchmark for relative comparison, but as an absolute claim of cartographic alignment it does not hold.

Bottom line: researchers working on LLM spatial reasoning or map finishing will get value from this, and it deserves a serious referee. But it needs major revisions before the results can be trusted as a measure of alignment with expert standards.

Referee Report

3 major / 4 minor

Summary. The paper proposes a new paradigm for automatic label placement (ALP) on maps using large language models (LLMs). The authors introduce MAPLE, a dataset of 100 maps from three US cities with over 1000 landmarks, where label locations are recovered by an OCR pipeline from QGIS-rendered maps. They use retrieval-augmented generation (RAG) with NGA labeling guidelines to prompt four open-source LLMs (Llama3.1, Gemma2, Qwen3, Phi-4) to output label coordinates, and they instruction-tune each model with QLoRA. The main results report RMSE between predicted label centroids and OCR-derived ground-truth label centroids, showing that instruction tuning reduces RMSE from roughly 81-165 pixels to 28-42 pixels across formats and models. The authors claim this demonstrates that LLMs, guided by structured prompts and domain-specific retrieval, can perform accurate spatial edits aligned with expert cartographic standards.

Significance. If the evaluation is valid, the paper makes a useful contribution by (a) introducing a publicly available benchmark for ALP on real-world maps, a gap the authors correctly identify, and (b) demonstrating that instruction-tuned LLMs can substantially improve over zero-shot performance on coordinate regression from textual landmark descriptions. The RAG-based incorporation of human-readable labeling guidelines is novel for ALP and provides a flexible alternative to hand-configured rule engines. The code and data are promised open-source, which supports reproducibility. However, the strength of these contributions is currently undercut by the unvalidated ground-truth construction and the absence of any comparison to existing ALP systems or trivial baselines; these gaps must be addressed before the central claim of 'alignment with expert cartographic standards' is credible.

major comments (3)

[Section 3.2] The ground-truth label locations are recovered via DBNet++ detection, ABINet recognition, and a 50-pixel proximity plus 80% Levenshtein assignment rule, but these recovered boxes are never validated against the true QGIS/PAL label positions or against human cartographer annotations. The paper's only validation is that a label is recovered for about 87% of maps; success rate is not accuracy. If the OCR pipeline systematically clips, expands, merges, or misassigns label boxes, then the RMSE values in Tables 3-5 do not measure alignment with actual cartographic output. Because the central claim of the abstract rests on these targets, the authors must provide a validation study on a subset of maps (e.g., comparing OCR-recovered boxes with manually annotated label positions or with directly accessed PAL parameters), and they should quantify the error introduced by the OCR pipeline. Without this, the 28-42 pixel RMSE numbers are uninterpretable.
[Section 5.1, Table 3] The evaluation contains no comparison against any existing ALP system (e.g., QGIS PAL, ArcGIS Maplex) or even a simple baseline such as predicting the landmark centroid, a fixed offset, or a random placement within a plausible radius. The RMSE values of 28-42 pixels are only meaningful relative to such references; without them, the reader cannot judge whether the tuned LLMs are 'accurate' in any absolute sense. Adding at least a centroid baseline and, if feasible, a rule-based engine would anchor the numbers and directly support the claim of improvements over existing automated systems.
[Section 3.1 and Section 5.1] The dataset statistics are internally inconsistent. The text in Section 3.1 states there are 100 maps and '1276 total landmarks,' while Section 5.1 says 'The MAPLE dataset contains 1276 maps in total.' Table 2 sums to 1620 landmarks (605+562+453), and Table 1 sums to 1619 across the seven categories. These discrepancies undermine reproducibility and the reporting of the dataset contribution. The authors must correct the numbers and explain the discrepancy between the per-type and per-city totals.

minor comments (4)

[Section 5.2] The phrase 'their performance decreases by almost 200%' is ambiguous and numerically backwards; the RMSE values drop by roughly a factor of three after tuning. Please rephrase to 'RMSE decreases by about 65-70%' or 'the error is reduced to roughly one third.'
[Table 3 and Table 4] The text says 'The main results are shown in Table 4,' but the overall results table is labeled Table 3; Table 4 shows results by landmark type. Please correct the cross-references.
[Section 4.3] The re-ranking step after vector similarity search is described only qualitatively ('considering factors such as keyword overlap, instruction specificity, or optional metadata'). Please specify the re-ranking algorithm or state that it is heuristic; currently the description is too vague to reproduce.
[Section 5.1] The evaluation uses RMSE between the predicted point and the centroid of the ground-truth label box. The paper does not report the distribution of label box sizes or aspect ratios, which would help interpret the RMSE in pixels. Please include such statistics or discuss the sensitivity of RMSE to the centroid choice.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the LLM evaluation is a held-out supervised benchmark; the OCR-derived ground truth is a data-validity concern, not a circular reduction.

full rationale

The paper's empirical chain is a standard supervised benchmark: MAPLE targets are produced by an independent DBNet++/ABINet OCR pipeline from QGIS-rendered maps, the data is split 80/10/20 into train/validation/test, LLMs are instruction-tuned on the train split only, and RMSE is computed on the held-out test set. The reported test RMSEs are not fitted parameters or predictions derived from the test targets; no equation defines the output in terms of the ground truth, and no load-bearing claim rests on a self-citation. The only notable weakness is in Section 3.2, where the authors state that recovering a label for about 87% of maps "thus validating our overall pipeline": a recovery rate is not an accuracy validation, and the OCR-derived boxes are never compared against the actual QGIS/PAL label positions or human cartographer annotations. This undermines the abstract's phrase "aligning the generated outputs with expert cartographic standards," but it is a measurement-validity caveat rather than circular reasoning. The derivation chain does not reduce to its own inputs by construction, so the appropriate circularity score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper contributes a data pipeline and an evaluation, not a derivation. All adjustable constants (p, Levenshtein threshold, k, neighbor distance, training hyperparameters) are chosen by hand and are not justified by sensitivity analysis. The central assumptions are about the validity of OCR-derived ground truth and the suitability of RMSE-to-centroid as a metric; if either fails, the numbers in Tables 3 and 4 are not meaningful.

free parameters (5)

proximity threshold p = 50 px
Used in Section 3.2 to assign OCR-recognized words to landmarks; chosen by hand ("we find that p=50px works well").
Levenshtein match threshold = 80%
Used in Section 3.2 to match OCR output to landmark names; chosen by hand.
RAG retrieval top-k
Section 4.3 says the top-k entries are returned, but the paper never states the value of k.
neighbor distance threshold = 50 px
Section 4.5 includes only landmarks within 50 px as neighbors; chosen by hand.
instruction tuning hyperparameters = 5 epochs, lr=1e-5, weight decay=1e-5
Section 5.1 lists these settings; all reported results depend on them.

assumptions (5)

domain assumption OpenStreetMap data and QGIS renderings accurately represent real-world maps and landmark geometry.
Section 3.1 relies on OSM as the source of maps and landmark metadata.
domain assumption The DBNet++/ABINet OCR pipeline correctly detects and recognizes map label text.
Section 3.2 uses OCR output to define ground-truth label locations; the paper does not report OCR accuracy on this style of imagery.
domain assumption Labels rendered by QGIS/PAL are a valid proxy for expert cartographic standards.
Section 3.2 defines ground truth from QGIS-rendered maps; the conclusion calls these "expert cartographic standards".
domain assumption RMSE between a predicted point and the centroid of the ground-truth label union is a valid quality metric for label placement.
Section 5.1 defines evaluation as RMSE to the centroid; no perceptual or cartographic validation of this metric is given.
domain assumption NGA guideline sections can be embedded and retrieved meaningfully with nomic-embed-text.
Section 4.2-4.3 assumes vector similarity retrieves relevant instructions; retrieval quality is never measured.

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Cite this review

Pith. "Pith review of Automated Label Placement on Maps via Large Language Models." pith.science (2026). https://pith.science/paper/4DYVNXTU

@misc{pith2026250722952,
  author       = {Pith},
  title        = {Pith review of: Automated Label Placement on Maps via Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4DYVNXTU}},
  note         = {Machine review of arXiv:2507.22952}
}

read the original abstract

Label placement is a critical aspect of map design, serving as a form of spatial annotation that directly impacts clarity and interpretability. Despite its importance, label placement remains largely manual and difficult to scale, as existing automated systems struggle to integrate cartographic conventions, adapt to context, or interpret labeling instructions. In this work, we introduce a new paradigm for automatic label placement (ALP) that formulates the task as a data editing problem and leverages large language models (LLMs) for context-aware spatial annotation. To support this direction, we curate MAPLE, the first known benchmarking dataset for evaluating ALP on real-world maps, encompassing diverse landmark types and label placement annotations from open-source data. Our method retrieves labeling guidelines relevant to each landmark type leveraging retrieval-augmented generation (RAG), integrates them into prompts, and employs instruction-tuned LLMs to generate ideal label coordinates. We evaluate four open-source LLMs on MAPLE, analyzing both overall performance and generalization across different types of landmarks. This includes both zero-shot and instruction-tuned performance. Our results demonstrate that LLMs, when guided by structured prompts and domain-specific retrieval, can learn to perform accurate spatial edits, aligning the generated outputs with expert cartographic standards. Overall, our work presents a scalable framework for AI-assisted map finishing and demonstrates the potential of foundation models in structured data editing tasks. The code and data can be found at https://github.com/HarryShomer/MAPLE.

Figures

Figures reproduced from arXiv: 2507.22952 by the authors.

**Figure 2.** The pipeline for extracting the label text from a map. For a map we first detect the location of all text in the map. Then [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** The pipeline for determining the true label location for a single landmark. We first (1) extract the area around the [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Overall framework for performing ALP using LLM+RAG. We first retrieve the correct instructions using both the [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 6.** Figure 6: Format of instruction tuning strategy. The LLM is [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 7.** Figure 7: Format of the prompt when including neighboring [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

**Figure 8.** Figure 8: Mean performance across LLMs by type of coordi [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

Discussion (0). Continue with ORCID to comment.

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Works this paper leans on

34 extracted references · 22 canonical work pages

[1]

Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J Hewett, Mojan Javaheripi, Piero Kauff- mann, et al. 2024. Phi-4 technical report. arXiv preprint arXiv:2412.08905 (2024)

arXiv 2024
[2]

Petr Bobák, Ladislav Čmolík, and Martin Čadík. 2023. Reinforced Labels: Multi- agent deep reinforcement learning for point-feature label placement. IEEE Trans- actions on Visualization and Computer Graphics 30, 9 (2023), 5908–5922

work page 2023
[3]

Petr Bobák, Ladislav Čmolík, and Martin Čadík. 2024. From Top-Right to User- Right: Perceptual Prioritization of Point-Feature Label Positions. arXiv preprint arXiv:2407.11996 (2024)

work page Pith review arXiv 2024
[4]

Wen Cao, Jiaqi Xu, Yong Zhang, Siqi Zhao, Chu Xu, and Xiaofeng Wu. 2023. A hybrid discrete artificial bee colony algorithm based on label similarity for solving point-feature label placement problem. ISPRS International Journal of Geo-Information 12, 10 (2023), 429

work page 2023
[5]

Jon Christensen and Joe Marks. 1995. An empirical study of algorithms for point feature label placement. (1995)

work page 1995
[6]

Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. Qlora: Efficient finetuning of quantized llms. Advances in neural information processing systems 36 (2023), 10088–10115

2023
[7]

Hamer Center for Maps and Geospatial Information

Donald W. Hamer Center for Maps and Geospatial Information. 2025. Maps & Geospatial. https://www.e-education.psu.edu/geog486/node/557 Accessed: 2025-05-02

work page 2025
[8]

Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, et al. 2022. A survey on in-context learning. arXiv preprint arXiv:2301.00234 (2022)

arXiv 2022

Show all 34 references

[9]

Esri. 2024. Label with the Maplex Label Engine - ArcGIS Pro. https://pro.arcgis.com/ en/pro-app/latest/help/mapping/text/label-with-the-maplex-label-engine.htm Accessed April 27, 2025

2024
[10]

Shancheng Fang, Hongtao Xie, Yuxin Wang, Zhendong Mao, and Yongdong Zhang. 2021. Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 7098–7107

2021
[11]

Weixi Feng, Wanrong Zhu, Tsu-jui Fu, Varun Jampani, Arjun Akula, Xuehai He, Sugato Basu, Xin Eric Wang, and William Yang Wang. 2023. Layoutgpt: Compo- sitional visual planning and generation with large language models. Advances in Neural Information Processing Systems 36 (2023...

2023
[12]

Kenneth Field. 2018. Cartography. Esri Press

2018
[13]

Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems 27 (2014)

2014
[14]

Google. 2025. Google Maps API v3 Reference. https://developers.google.com/ maps/documentation/javascript/reference Accessed: 2025-05-03

2025
[15]

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)

2024 arXiv
[16]

Mordechai Haklay and Patrick Weber. 2008. Openstreetmap: User-generated street maps. IEEE Pervasive computing 7, 4 (2008), 12–18

2008
[17]

Yikun Han, Chunjiang Liu, and Pengfei Wang. 2023. A comprehensive survey on vector database: Storage and retrieval technique, challenge. arXiv preprint arXiv:2310.11703 (2023)

2023
[18]

Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2022. LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations

2022
[19]

Eduard Imhof. 1975. Positioning names on maps. The American Cartographer 2, 2 (1975), 128–144

1975
[20]

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing s...

2020
[21]

Minghui Liao, Zhaoyi Wan, Cong Yao, Kai Chen, and Xiang Bai. 2020. Real-time scene text detection with differentiable binarization. In Proceedings of the AAAI conference on artificial intelligence , Vol. 34. 11474–11481

2020
[22]

Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. Visual in- struction tuning. Advances in neural information processing systems 36 (2023), 34892–34916

2023
[23]

National Geospatial-Intelligence Agency. 2024. Sequoia: NGA’s largest data labeling effort to date. https://www.nga.mil/news/NGA_announces_$708M_ data_labeling_RFP.html Accessed: 2025-05-02

2024
[24]

National Geospatial-Intelligence Agency/Foundation GEOINT Group. 2022. NGA Standardization Document, Data Product Specification (DPS). https://www.nga. mil/resources/Print_on_Demand_(PoD).html

2022
[25]

Benjamin Niedermann. 2017. Automatic Label Placement in Maps and Figures: Models, Algorithms and Experiments . Ph. D. Dissertation. Dissertation, Karlsruhe, Karlsruher Institut für Technologie (KIT), 2017

2017
[26]

Zach Nussbaum, John X Morris, Brandon Duderstadt, and Andriy Mulyar. 2024. Nomic embed: Training a reproducible long context text embedder.arXiv preprint arXiv:2402.01613 (2024)

2024 arXiv
[27]

Rachid Oucheikh and Lars Harrie. 2024. A feasibility study of applying generative deep learning models for map labeling. Cartography and Geographic Information Science 51, 1 (2024), 168–191

2024
[28]

QGIS Project. 2024. QgsLabelingEngine Class Reference - QGIS API Documentation . https://api.qgis.org/api/classQgsLabelingEngine.html Accessed April 27, 2025

2024
[29]

Jingwei Qu, Pingshun Zhang, Enyu Che, Yinan Chen, and Haibin Ling. 2024. Graph Transformer for Label Placement. IEEE Transactions on Visualization and Computer Graphics (2024)

2024
[30]

Jaelle Scheuerman, Jason L Harman, Rebecca R Goldstein, Dina Acklin, and Chris J Michael. 2023. Visual preferences in map label placement. Discover Psychology 3, 1 (2023), 27

2023
[31]

Chenglei Si, Yanzhe Zhang, Zhengyuan Yang, Ruibo Liu, and Diyi Yang. 2024. Design2code: How far are we from automating front-end engineering? arXiv e-prints (2024), arXiv–2403

2024
[32]

Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al. 2024. Gemma 2: Improving open language models at a practical size. arXiv preprint arXiv:2408.00118 (2024)

2024 arXiv
[33]

Huafei Yu, Tinghua Ai, Min Yang, Rachid Oucheikh, Bo Kong, Hao Wu, Zhenyu Zhang, and Lars Harrie. 2024. A Deep Learning Segmentation Approach for Road Label Placement. A vailable at SSRN 4940850 (2024)

2024
[34]

Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, et al. 2023. Instruction tuning for large language models: A survey. arXiv preprint arXiv:2308.10792 (2023)

2023

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J Hewett, Mojan Javaheripi, Piero Kauff- mann, et al. 2024. Phi-4 technical report. arXiv preprint arXiv:2412.08905 (2024)

arXiv 2024

[2] [2]

Petr Bobák, Ladislav Čmolík, and Martin Čadík. 2023. Reinforced Labels: Multi- agent deep reinforcement learning for point-feature label placement. IEEE Trans- actions on Visualization and Computer Graphics 30, 9 (2023), 5908–5922

work page 2023

[3] [3]

Petr Bobák, Ladislav Čmolík, and Martin Čadík. 2024. From Top-Right to User- Right: Perceptual Prioritization of Point-Feature Label Positions. arXiv preprint arXiv:2407.11996 (2024)

work page Pith review arXiv 2024

[4] [4]

Wen Cao, Jiaqi Xu, Yong Zhang, Siqi Zhao, Chu Xu, and Xiaofeng Wu. 2023. A hybrid discrete artificial bee colony algorithm based on label similarity for solving point-feature label placement problem. ISPRS International Journal of Geo-Information 12, 10 (2023), 429

work page 2023

[5] [5]

Jon Christensen and Joe Marks. 1995. An empirical study of algorithms for point feature label placement. (1995)

work page 1995

[6] [6]

Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. Qlora: Efficient finetuning of quantized llms. Advances in neural information processing systems 36 (2023), 10088–10115

2023

[7] [7]

Hamer Center for Maps and Geospatial Information

Donald W. Hamer Center for Maps and Geospatial Information. 2025. Maps & Geospatial. https://www.e-education.psu.edu/geog486/node/557 Accessed: 2025-05-02

work page 2025

[8] [8]

Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, et al. 2022. A survey on in-context learning. arXiv preprint arXiv:2301.00234 (2022)

arXiv 2022