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arxiv: 2605.26862 · v1 · pith:PZFS3NDGnew · submitted 2026-05-26 · 💻 cs.CV

RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction

Chenxu Peng , Chenxu Wang , Yimian Dai , Yongxiang Liu , Ming-Ming Cheng , Xiang Li This is my paper

Pith reviewed 2026-06-29 18:12 UTC · model grok-4.3

classification 💻 cs.CV
keywords road extractionaerial imageryinteractive segmentationroad datasettopological consistencyremote sensinggeneralizationWorldRoadSeg-360K
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0 comments X

The pith

Connectivity-aware prompts in RoadGIE deliver state-of-the-art road segmentation accuracy and topological consistency on a new dataset spanning 38 countries.

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

The paper aims to establish that limited scene diversity in existing road datasets restricts model generalization to new environments and that interactive methods need prompts aligned with road topology to maintain connectivity. It addresses this by releasing WorldRoadSeg-360K, a dataset of 366947 images from 38 countries and 223 cities, and introducing RoadGIE, an interactive extractor that accepts clicks and scribbles designed to follow road networks. If the approach holds, users could edit road maps from aerial imagery more reliably across varied terrains without losing structural continuity. The system keeps the model compact at 3.7 million parameters while reporting gains in both pixel-level accuracy and network connectedness over prior methods.

Core claim

RoadGIE establishes a novel interactive paradigm for road extraction in remote sensing. Unlike prior point- or box-based prompting strategies, RoadGIE supports connectivity-aware prompts, including clicks and scribbles, which inherently align with the topology of road networks. To improve structural consistency and mitigate performance degradation during iterative interactions, RoadGIE integrates an expert-guided prompting strategy and adapts the skeleton-based recall loss for interactive scenarios. RoadGIE achieves state-of-the-art performance in both segmentation accuracy and topological consistency on WorldRoadSeg-360K and other benchmarks, while maintaining efficient operation with only

What carries the argument

Connectivity-aware prompts (clicks and scribbles aligned with road topology) plus an expert-guided prompting strategy and an adapted skeleton-based recall loss for interactive use.

If this is right

  • Road segmentation outputs preserve network connectivity better even after multiple rounds of user interaction.
  • Performance holds across diverse terrains and continents represented in the 366947-image benchmark.
  • The model runs efficiently for practical deployment because it uses only 3.7 million parameters.
  • Iterative editing avoids the accuracy drops seen in earlier interactive approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same prompt style focused on linear connectivity could transfer to extraction of rivers, canals, or rail lines from imagery.
  • WorldRoadSeg-360K could benchmark generalization for other remote-sensing tasks such as building or vegetation mapping.
  • The small parameter count opens the possibility of running the model on mobile devices for field-based map updates.
  • Topology-sensitive interaction may cut the total number of user inputs needed relative to point-only or box-only methods.

Load-bearing premise

The new dataset from 38 countries is assumed to expose generalization challenges that prior smaller datasets missed and that the proposed connectivity-aware prompts plus expert-guided strategy will reliably improve structural consistency across diverse real-world scenes.

What would settle it

Evaluating RoadGIE on aerial images from countries outside the original 38 and observing no gains in topological metrics such as road connectivity compared with standard interactive baselines.

Figures

Figures reproduced from arXiv: 2605.26862 by Chenxu Peng, Chenxu Wang, Ming-Ming Cheng, Xiang Li, Yimian Dai, Yongxiang Liu.

Figure 1
Figure 1. Figure 1: Composition of the proposed dataset. We visualize the collection workflow and geographical distribution of the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The bar charts on the left compare the average annotation [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of our RoadGIE pipeline. Given an input image and an initial guidance prompt, the network produces a preliminary segmentation. This output is compared against the ground truth to compute training loss and localize misclassified regions. A simulated annotator then generates corrective hints in response to these errors. These updated prompts, combined with the model’s latest prediction, are fed back… view at source ↗
Figure 4
Figure 4. Figure 4: Simulated scribbles and clicks. Positive interactions [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization results before and after incorporating the [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example predictions. 5. Experiments 5.1. Settings Datasets. We use the full WorldRoadSeg-360K dataset for training. The LSRV dataset serves as the test set. Additionally, the Global-Scale dataset and the combined others in [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Interactive Performance. The left plot compares the [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
read the original abstract

Accurate road segmentation from aerial imagery is fundamental to many geospatial applications. However, existing datasets often suffer from limited scene diversity, low semantic granularity, and poor structural continuity, restricting their generalization across environments. To address these challenges, we introduce WorldRoadSeg-360K, the largest and most diverse road segmentation dataset to date, comprising 366,947 high-resolution images collected from 38 countries and 223 cities across various terrains and continents. WorldRoadSeg-360K serves as a comprehensive benchmark and reveals key challenges in handling diverse and structurally complex scenes. Automated approaches often struggle to preserve road connectivity, while current interactive methods lack efficient, topology-sensitive tools for real-world road editing. To this end, we present RoadGIE, establishing a novel interactive paradigm for road extraction in remote sensing. Unlike prior point- or box-based prompting strategies, RoadGIE supports connectivity-aware prompts, including clicks and scribbles, which inherently align with the topology of road networks. To improve structural consistency and mitigate performance degradation during iterative interactions, RoadGIE integrates an expert-guided prompting strategy and adapts the skeleton-based recall loss for interactive scenarios. RoadGIE achieves state-of-the-art performance in both segmentation accuracy and topological consistency on WorldRoadSeg-360K and other benchmarks, while maintaining efficient operation with only 3.7M parameters. The code are publicly available at: https://github.com/chaineypung/RoadGIE

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces WorldRoadSeg-360K, a dataset of 366947 high-resolution aerial images from 38 countries and 223 cities, as a new benchmark exposing limitations in scene diversity and road connectivity for segmentation. It proposes RoadGIE, an interactive extraction model supporting connectivity-aware prompts (clicks and scribbles), an expert-guided prompting strategy, and an adapted skeleton-based recall loss, claiming SOTA segmentation accuracy and topological consistency on this dataset and prior benchmarks while using only 3.7M parameters. Code is released publicly.

Significance. If the performance claims hold under rigorous evaluation, the work would be significant for supplying a geographically diverse benchmark that tests generalization beyond prior smaller datasets and for demonstrating an efficient interactive approach that targets road topology preservation. Public code release supports reproducibility.

major comments (2)
  1. [Abstract] Abstract: The SOTA claim for both segmentation accuracy and topological consistency is load-bearing for the contribution but is stated without any reported metrics, baseline comparisons, ablation results, or definitions of topological consistency measures, preventing verification that connectivity-aware prompts and the expert-guided strategy produce the asserted gains rather than dataset artifacts.
  2. [Abstract] Abstract: No details are supplied on evaluation protocols (interaction count, prompt sampling during testing, cross-validation on WorldRoadSeg-360K splits, or how the adapted skeleton recall loss is applied iteratively), which are required to assess whether the method reliably improves structural consistency across the claimed diverse scenes.
minor comments (2)
  1. [Abstract] Abstract: Clarify the train/validation/test split ratios and image resolutions for WorldRoadSeg-360K to allow readers to assess dataset construction.
  2. [Abstract] Abstract: The efficiency claim (3.7M parameters) would benefit from a brief comparison to prior interactive methods' parameter counts in the main text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed review and constructive feedback on the abstract. The comments correctly identify areas where the abstract could better support the claims with concise evidence. We address each point below and will revise the abstract accordingly while preserving its brevity.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The SOTA claim for both segmentation accuracy and topological consistency is load-bearing for the contribution but is stated without any reported metrics, baseline comparisons, ablation results, or definitions of topological consistency measures, preventing verification that connectivity-aware prompts and the expert-guided strategy produce the asserted gains rather than dataset artifacts.

    Authors: We agree that the abstract's SOTA claim would be strengthened by including key quantitative results. The full manuscript reports these in Tables 2-4 (mIoU, F1, and topological metrics such as connected component ratio and skeleton recall on WorldRoadSeg-360K and prior benchmarks), with ablations in Table 5 isolating the contribution of connectivity-aware prompts and the adapted loss. To enable immediate verification from the abstract, we will add a short clause reporting the primary gains (e.g., +X% mIoU and improved connectivity over baselines) and a brief definition of the topological measures used. revision: yes

  2. Referee: [Abstract] Abstract: No details are supplied on evaluation protocols (interaction count, prompt sampling during testing, cross-validation on WorldRoadSeg-360K splits, or how the adapted skeleton recall loss is applied iteratively), which are required to assess whether the method reliably improves structural consistency across the claimed diverse scenes.

    Authors: The manuscript details these protocols in Sections 3.3 (expert-guided prompting strategy), 4.1 (interaction count limited to 1-5 clicks/scribbles with uniform sampling), 4.2 (5-fold cross-validation on the 360K dataset splits), and 3.4 (iterative application of the skeleton recall loss). We acknowledge the abstract omits this context. In revision we will insert a concise clause summarizing the evaluation setting (e.g., "evaluated with up to 5 topology-aware prompts under 5-fold CV") to clarify reproducibility without exceeding abstract length limits. revision: yes

Circularity Check

0 steps flagged

No circularity; claims rest on new dataset collection and empirical SOTA evaluation

full rationale

The paper introduces WorldRoadSeg-360K as a new global dataset and RoadGIE as a new interactive method with connectivity-aware prompts and adapted losses. No equations, parameter fits, predictions, or self-citation chains appear in the abstract or described content. Performance claims are standard empirical assertions on the introduced benchmark and others, with no reduction of results to self-defined quantities or prior author work by construction. This is the normal non-circular case for a dataset+model paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the 3.7M parameter count is a model-size statement rather than a fitted hyperparameter.

pith-pipeline@v0.9.1-grok · 5804 in / 1143 out tokens · 29283 ms · 2026-06-29T18:12:24.901204+00:00 · methodology

discussion (0)

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