Runway vs. Taxiway: Challenges in Automated Line Identification and Notation Approaches

Abdullah Alqahtani; Amy Alvarez; Mohammed Abdul Hafeez Khan; Natasha Neogi; Parth Ganeriwala; Siddhartha Bhattacharyya

REVIEW 4 major objections 5 minor 27 references

Runway vs. Taxiway: Challenges in Automated Line Identification and Notation Approaches

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read A CNN pre-classification step distinguishes runway from taxiway imagery and is claimed to make the ALINA labeling pipeline resilient to environmental variations, with 99.5% validation accuracy.

desk verdict Honest negative result, unproven fix: ALINA's runway failures are real, but AssistNet's claimed benefit is never tested end-to-end. read the letter →

arxiv 2501.18494 v1 pith:UUCANVQH submitted 2025-01-30 cs.CV cs.LG

Parth Ganeriwala , Amy Alvarez , Abdullah AlQahtani , Siddhartha Bhattacharyya , Mohammed Abdul Hafeez Khan , Natasha Neogi This is my paper · ORCID

classification cs.CVcs.LG

keywords runwaydetectiontaxiwayCNNclassificationlinelabelingALINAAssistTaxicolorthresholdingaviationcomputervision

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

The paper argues that existing line-labeling algorithms tuned for taxiways fail on runways because runways use white markings, different geometry, and interference from the horizon. The authors propose a binary classification step (AssistNet) that first decides whether an image shows a runway or a taxiway, then lets the ALINA algorithm run only on taxiways while a future dedicated algorithm handles runways. On a small subset of the AssistTaxi dataset, AssistNet achieved 99.5% validation accuracy, though the paper emphasizes this is a proposed framework rather than an end-to-end evaluation.

What carries the argument

AssistNet, a convolutional neural network with convolutional layers, max-pooling, dense layers, and a sigmoid output, combined with a contextually reduced ROI that crops out the horizon and sky. In the proposed pipeline, AssistNet acts as a gate that routes runway images away from the taxiway-tuned ALINA algorithm, whose CIRCLEDAT pixel-traversal step then operates only on appropriate input.

What would settle it

Run the full AssistNet-plus-ALINA pipeline on runway videos from the AssistTaxi dataset and measure labeling precision; if horizon or background mislabeling persists, or if partially obscured or faded runway markings cause the classifier to route runway frames to the taxiway branch, the central claim is weakened.

Watch

Extended reading notes

Core claim

The central claim is that a simple CNN can reliably distinguish runway imagery from taxiway imagery based primarily on line color (white vs yellow) and a contextually reduced region of interest, and that this pre-classification makes the labeling pipeline more resilient to environmental interference. The paper also documents that modifying ALINA's color thresholds and ROI selection alone is insufficient, with the horizon and background features still being mislabeled.

Load-bearing premise

The assumption that a runway-versus-taxiway classification driven mostly by line color (white versus yellow) is sufficient, and that the 99.5% validation accuracy will generalize to real conditions with faded markings, varied lighting, and the horizon present.

Editorial extensions

If this is right

If the classification step works, ALINA can be restricted to taxiway frames, reducing false positives from horizon and background features.
A dedicated runway labeling algorithm can be developed separately, tuned to white markings and runway geometry, and inserted after the classifier.
The pipeline becomes modular: any future surface type (apron, ramp) could be handled by adding another classification branch.
The reported 99.5% validation accuracy suggests the color cue is strong, but the paper's own discussion indicates that more diverse data and end-to-end testing are needed to confirm real-world behavior.

Reading between the lines

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

The classifier's reliance on line color may fail on monochrome cameras or under strong color casts; a testable extension is augmenting with grayscale or hue-invariant features.
The proposed pre-classification approach could naturally scale to multi-class surface identification (runway, taxiway, apron, ramp) using the same architecture, a step the paper does not explore.
Because the paper explicitly notes the absence of an end-to-end test, the 99.5% figure should be read as a component-level result; measuring the full pipeline's labeling error is the immediate next step.
A dynamic ROI driven by aircraft position, which the paper says could not be implemented due to missing position data, would likely improve both classification and labeling under approach conditions.

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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 documents a real negative result: ALINA, even after color-threshold adjustments, still mislabels runway imagery, confusing the horizon and background with markings. That failure mode is described clearly with examples. The second thing is that the proposed fix, a CNN pre-classifier called AssistNet, is never shown to fix anything. The paper's central claim—that adding this classification step makes the labeling pipeline more resilient—is asserted, not tested. No experiment measures ALINA's labeling errors before and after gating with the classifier.

What's new: the negative result on runway imagery is new and useful, especially for people working on airport surface perception. The pre-classification idea is natural and low-cost. The paper is also honest about missing position data for dynamic ROI.

Soft spots: The 99.5% accuracy is on a validation set, not a held-out test. Section IV.A says 70:20:10 split; Section V describes 500 images with 125 training and 125 validation per class, no test set. Those numbers don't line up. They also give two input resolutions: 400x225 in IV.C, 150x150 in V. The classifier's decision is reduced to line color—yellow vs white—so the high accuracy is unsurprising, but they don't compare against a simple color-threshold baseline. And there's no confusion matrix or per-class precision/recall. The GitHub repo exists but no commit hash or dataset release, so the result isn't reproducible as it stands. These aren't minor quibbles; the main claim rests entirely on the classifier accuracy, and that evidence is shaky.

Who it's for: people evaluating automated runway/taxiway labeling systems, and maybe researchers looking for failure cases in aviation vision. The negative result is worth knowing, but the proposed solution isn't ready.

Recommendation: I'd send it to peer review only if the venue is willing to sit with a negative result and a thin evaluation, because the failure documentation is real and the required experiments are clearly specified. Otherwise desk reject with an invitation to resubmit after adding a held-out test, a color baseline, and end-to-end labeling metrics. For this journal, I'd lean toward sending it to a knowledgeable reviewer—they'd likely identify the same gaps, and the paper would be stronger for it.

Referee Report

4 major / 5 minor

Summary. The paper addresses automated labeling of runway versus taxiway markings in aviation imagery. It first documents attempts to adapt the existing ALINA line-labeling algorithm to runways by adjusting color thresholds and ROI selection, finding these modifications insufficient. The authors then propose AssistNet, a CNN that classifies images as runway or taxiway before applying a labeling algorithm, and report 99.5% validation accuracy. The paper argues that this classification step makes the overall detection pipeline more resilient to environmental variations and misclassifications. However, no end-to-end experiment measures whether gating ALINA with AssistNet actually improves labeling performance.

Significance. The paper identifies a genuine problem: ALINA works on taxiway markings but struggles on runway markings, and a pre-classification step that routes imagery to the appropriate labeling algorithm is a plausible architectural solution. The release of the AssistNet code on GitHub and the concrete failure analysis of ALINA on runway frames are useful contributions. The significance is conditional, however, because the central claim of improved pipeline resilience is asserted rather than demonstrated; the only quantitative evidence is a validation-set classifier accuracy with unresolved dataset-split and resolution inconsistencies.

major comments (4)

[Abstract and Section V] The central claim that the classification step makes the pipeline 'more resilient' is not tested end-to-end. No experiment measures whether gating ALINA with AssistNet changes ALINA's labeling errors on runway or taxiway frames, so the pipeline-level benefit is asserted rather than demonstrated. The 99.5% accuracy is reported on the validation set only and does not establish that the downstream labeling improves.
[Section IV.A and Section V] The dataset split is contradictory: Section IV.A states a 70:20:10 train/validation/test split, while Section V describes 500 images with 125 per class for training and validation and no test set at all. This ambiguity makes the reported 99.5% validation accuracy uninterpretable and must be resolved with a clear statement of the exact split and held-out test performance.
[Section IV.C and Section V] The input resolution is inconsistent: Section IV.C says images are resized to 400x225 pixels, while Section V initially says images were resized to 150x150 and later says the resolution was increased to 400x225. The final preprocessing protocol must be stated unambiguously, and any resolution-dependent results should be reported with the corresponding setting.
[Section V] No baseline or statistical comparison is provided for the classifier. The 99.5% accuracy lacks error bars, per-class precision/recall, and the mentioned confusion matrix is not shown. Because Section IV.B asserts that color is the key differentiating factor, a comparison against a simple color-threshold baseline is necessary to show that AssistNet adds value beyond the color cue already used by ALINA.

minor comments (5)

[Section I] The introduction says Section V contains a case study on a subset of the AssistTaxi dataset, but I could not identify a distinct case-study subsection; please clarify or reword.
[Figure 4] The caption reads 'Training and Validation Results over 20 epochs' but the axes and plotted metrics are not described; add axis labels and indicate whether accuracy or loss is shown.
[Section V] The text says 'the confusion matrix revealed...' but no confusion matrix is included; either include the matrix or remove the reference.
[Author affiliation, Section II.A] Minor typographical issues: 'Hampton V A' should be 'Hampton, VA', and 'UA V' should be 'UAV'.
[Section IV.B] The GitHub repository link is appreciated; consider adding a version tag or commit hash to support reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: AssistNet's 99.5% validation accuracy is a standard trained-model evaluation, and the pipeline-resilience claim is an unsupported inference rather than a construction-level reduction.

full rationale

The paper's central claim—that adding the AssistNet classification step makes the ALINA labeling pipeline more resilient—is not derived from its inputs by construction. AssistNet is trained on labeled AssistTaxi images, and its 99.5% validation accuracy is a conventional empirical evaluation of a fitted model, not a fitted parameter that is renamed as a prediction. The assertion that this accuracy transfers to improved ALINA labeling is unsupported because no end-to-end experiment measures the classifier's effect on ALINA's output, but an unsupported inference is an evidence gap, not circularity. The paper explicitly acknowledges missing position data prevented dynamic ROI implementation and lists unresolved limitations in Section VI. The prior ALINA and AssistTaxi works are self-cited, yet the paper does not use those citations to define away the target result; it reports its own modified-ALINA experiments in Figure 1 and uses the dataset only as training input. No equation or construction equates the claimed outcome to an input. The internal inconsistencies in dataset split (70:20:10 versus 125/125 training/validation) and image resolution (400x225 versus 150x150) undermine reliability but do not make any prediction equivalent to its input. Therefore, no significant circularity is present.

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

The central claim relies on fitted CNN weights, hand-tuned ALINA thresholds, manually chosen ROI crops, and the untested assumption that color-based binary classification drives labeling improvements. No new physical entities are introduced; AssistNet is a standard CNN architecture rather than an invented entity in the ontology sense.

free parameters (5)

AssistNet CNN weights = Not reported
Trained on the 500-image labeled subset; the 99.5% validation accuracy is a fitted performance value, not an independent prediction.
ALINA color thresholds for white markings = Not reported
Section III: thresholds fine-tuned by analyzing color distribution of runway markings; exact values omitted.
Reduced ROI crop parameters = Not reported
Section IV.C: cropping to isolate markings and remove horizon; crop geometry chosen by hand and not specified.
Data augmentation ranges = Not reported
Section IV.A and V: random rotations and brightness adjustments applied; ranges not quantified.
Input image resolution = 150x150 and 400x225 (inconsistent)
Section V reports resizing to 150x150; Section IV.C states 400x225; the choice is ad hoc and inconsistent.

assumptions (5)

standard math Backpropagation and standard CNN operations (convolution, max-pooling, dense layers) are valid.
Used implicitly in Section IV.B for AssistNet training.
domain assumption Runway markings are predominantly white and taxiway markings yellow, and this color difference is the key distinguishing feature.
Section IV.B: 'The key differentiating factor is the color of the line markings, yellow for taxiways and white for runways.'
domain assumption The horizon and non-surface regions can be removed by a fixed crop without losing class-relevant information.
Section IV.C: 'cropping the images to remove the horizon and isolate the road segments.' No quantitative evidence that this crop is safe across conditions.
ad hoc to paper A binary runway/taxiway label is sufficient to route images to the correct labeling algorithm.
Section III and Figure 2: the proposed framework feeds classification output into ALINA for taxiways or a future runway labeler; this routing benefit is never measured.
domain assumption The AssistTaxi subset is representative of operational runway and taxiway conditions.
Section IV.A: images from various airports and conditions, but no distribution analysis or external benchmark is given.

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Cite this review

Pith. "Pith review of Runway vs. Taxiway: Challenges in Automated Line Identification and Notation Approaches." pith.science (2026). https://pith.science/paper/UUCANVQH

@misc{pith2026250118494,
  author       = {Pith},
  title        = {Pith review of: Runway vs. Taxiway: Challenges in Automated Line Identification and Notation Approaches},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UUCANVQH}},
  note         = {Machine review of arXiv:2501.18494}
}

read the original abstract

The increasing complexity of autonomous systems has amplified the need for accurate and reliable labeling of runway and taxiway markings to ensure operational safety. Precise detection and labeling of these markings are critical for tasks such as navigation, landing assistance, and ground control automation. Existing labeling algorithms, like the Automated Line Identification and Notation Algorithm (ALINA), have demonstrated success in identifying taxiway markings but encounter significant challenges when applied to runway markings. This limitation arises due to notable differences in line characteristics, environmental context, and interference from elements such as shadows, tire marks, and varying surface conditions. To address these challenges, we modified ALINA by adjusting color thresholds and refining region of interest (ROI) selection to better suit runway-specific contexts. While these modifications yielded limited improvements, the algorithm still struggled with consistent runway identification, often mislabeling elements such as the horizon or non-relevant background features. This highlighted the need for a more robust solution capable of adapting to diverse visual interferences. In this paper, we propose integrating a classification step using a Convolutional Neural Network (CNN) named AssistNet. By incorporating this classification step, the detection pipeline becomes more resilient to environmental variations and misclassifications. This work not only identifies the challenges but also outlines solutions, paving the way for improved automated labeling techniques essential for autonomous aviation systems.

Figures

Figures reproduced from arXiv: 2501.18494 by the authors.

**Figure 2.** Proposed Labeling Framework with Initial Classification Step [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Instances from the AssistTaxi Dataset [17] [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Training and Validation Results over 20 epochs [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Classification Results The selected images represented challenging conditions, such as deteriorated markings due to wear, blurred areas from motion, and overlapping signs. Each image was resized to 150x150 pixels for consistency across the dataset. To improve generaliz…

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.

[1] [1]

A robust airport runway detection network based on r-cnn using remote sensing images

Rasna A Amit and C Krishna Mohan. A robust airport runway detection network based on r-cnn using remote sensing images. IEEE Aerospace and Electronic Systems Magazine , 36(11):4–20, 2021

work page 2021

[2] [2]

Robust lane detection and tracking algorithm for steering assist systems

Mihail-Alexandru Andrei, Costin-Anton Boiangiu, Nicolae Tarb ˘a, and Mihai-Lucian V oncil˘a. Robust lane detection and tracking algorithm for steering assist systems. Machines, 10(1):10, 2022

work page 2022

[3] [3]

Aerodrome taxiway line detection and cross-track error estimation using computer vision techniques

Aman Batra and Jason Gauci. Aerodrome taxiway line detection and cross-track error estimation using computer vision techniques. In 2020 IEEE Aerospace Conference, pages 1–9, 2020

work page 2020

[4] [4]

Automated ground operation for an unmanned cargo gyrocopter

Sebastian Benders and Sven Lorenz. Automated ground operation for an unmanned cargo gyrocopter. In AIAA SciTech 2022 Forum, page 2160, 2022

work page 2022

[5] [5]

Robust horizon detection using segmentation for uav applications

Nasim Sepehri Boroujeni, S Ali Etemad, and Anthony Whitehead. Robust horizon detection using segmentation for uav applications. In 2012 ninth conference on computer and robot vision , pages 346–352. IEEE, 2012

work page 2012

[6] [6]

An image-based runway detection method for fixed-wing aircraft based on deep neural network

Mingqiang Chen and Yuzhou Hu. An image-based runway detection method for fixed-wing aircraft based on deep neural network. IET Image Processing, 2024

work page 2024

[7] [7]

Road marking detection and classification using machine learning algorithms

Tairui Chen, Zhilu Chen, Quan Shi, and Xinming Huang. Road marking detection and classification using machine learning algorithms. In 2015 IEEE Intelligent Vehicles Symposium (IV) , pages 617–621, 2015

work page 2015

[8] [8]

Explaining autonomous driving by learning end- to-end visual attention

Luca Cultrera, Lorenzo Seidenari, Federico Becattini, Pietro Pala, and Alberto Del Bimbo. Explaining autonomous driving by learning end- to-end visual attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2020

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