A Large-Scale Dataset and a New Method for RemoteSensing Traffic Object Segmentation

• URL: http://arxiv.org/abs/2607.03945v1
• Date: Sat, 04 Jul 2026 16:40:02 GMT
• Title: A Large-Scale Dataset and a New Method for RemoteSensing Traffic Object Segmentation
• Abstract summary: We construct a large-scale and diverse dataset for transportation object segmentation, named as NWPU-Traffic.<n>This dataset encompass four traffic object categories (car, airplane, ship, and train) and a wide range of scenes from 49 cities across 7 countries.<n>We propose a novel segmentation method that leverages spatial-channel preserving feature interaction and an adaptive feature decoder.
• Score: 6.5047827920310395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote sensing imagery plays a crucial role in evaluating regional transportation capacity. However, existing segmentation datasets often lack diversity in object categories and scenes, limiting the ability of models to comprehensively evaluate trans portation capacity in real-world scenes. To alleviate this gap, we construct a large-scale and diverse dataset for transportation object segmentation, named as NWPU-Traffic. This dataset encompass four traffic object categories (car, airplane, ship, and train) and a wide range of scenes from 49 cities across 7 countries, with instance-level annotations to ensure precise segmentation of individual objects, which bridges critical shortcomings in resolution and scene diversity in existing datasets. Leveraging this dataset, we establish a benchmark with several popular segmentation networks. Furthermore, we propose a novel segmentation method that leverages spatial-channel preserving feature interaction and an adaptive feature decoder, enabling robust segmentation across varying scales and complex environments. Extensive experiments and ablation studies validate the effectiveness of our approach. The dataset and code are publicly available at https://github.com/CVer-Yang/NWPU-Traffic.

Related papers

• Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation [4.106285392075794]
  Distinguishing between known classes and previously unseen objects is crucial in real-world environments.<n>Research in the 3D field remains limited, with most existing approaches applying post-processing techniques from 2D vision.<n>We propose a new efficient approach that directly operates in the feature space, modeling the feature distribution of inlier classes to constrain anomalous samples.
  arXiv  Detail & Related papers  (2026-04-26T08:32:07Z)
• A Large-Scale Referring Remote Sensing Image Segmentation Dataset and Benchmark [8.707197692292292]
  We introduce NWPU-Refer, the largest and most diverse RRSIS dataset to date, comprising 15,003 high-resolution images (1024-2048px) spanning 30+ countries with 49,745 annotated targets.<n>We also propose the Multi-scale Referring Network (MRSNet), a novel framework tailored for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2025-06-04T05:26:51Z)
• Scalable Learning of Segment-Level Traffic Congestion Functions [40.26253453756835]
  We propose and study a data-driven framework for identifying traffic congestion functions at global scale and segment-level granularity. In contrast to methods that estimate a separate set of parameters for each roadway, ours learns a single black-box function over all roadways in a metropolitan area.
  arXiv  Detail & Related papers  (2024-05-09T20:12:46Z)
• UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction [93.77809355002591]
  We introduce UniTraj, a comprehensive framework that unifies various datasets, models, and evaluation criteria. We conduct extensive experiments and find that model performance significantly drops when transferred to other datasets. We provide insights into dataset characteristics to explain these findings.
  arXiv  Detail & Related papers  (2024-03-22T10:36:50Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion [110.84357383258818]
  We propose a novel approach to lift 2D segments to 3D and fuse them by means of a neural field representation. The core of our approach is a slow-fast clustering objective function, which is scalable and well-suited for scenes with a large number of objects. Our approach outperforms the state-of-the-art on challenging scenes from the ScanNet, Hypersim, and Replica datasets.
  arXiv  Detail & Related papers  (2023-06-07T17:57:45Z)
• Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding [137.3719377780593]
  A new design (named Detection Hub) is dataset-aware and category-aligned. It mitigates the dataset inconsistency and provides coherent guidance for the detector to learn across multiple datasets. The categories across datasets are semantically aligned into a unified space by replacing one-hot category representations with word embedding.
  arXiv  Detail & Related papers  (2022-06-07T17:59:44Z)
• Cascaded Sparse Feature Propagation Network for Interactive Segmentation [18.584007891618096]
  We propose a cascade sparse feature propagation network that learns a click-augmented feature representation for propagating user-provided information to unlabeled regions. We validate the effectiveness of our method through comprehensive experiments on various benchmarks, and the results demonstrate the superior performance of our approach.
  arXiv  Detail & Related papers  (2022-03-10T03:47:24Z)
• Addressing Multiple Salient Object Detection via Dual-Space Long-Range Dependencies [3.8824028205733017]
  Salient object detection plays an important role in many downstream tasks. We propose a network architecture incorporating non-local feature information in both the spatial and channel spaces. We show that our approach accurately locates multiple salient regions even in complex scenarios.
  arXiv  Detail & Related papers  (2021-11-04T23:16:53Z)
• Learning Open-World Object Proposals without Learning to Classify [110.30191531975804]
  We propose a classification-free Object Localization Network (OLN) which estimates the objectness of each region purely by how well the location and shape of a region overlaps with any ground-truth object. This simple strategy learns generalizable objectness and outperforms existing proposals on cross-category generalization.
  arXiv  Detail & Related papers  (2021-08-15T14:36:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.