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

Source: [A Large-Scale Dataset and a New Method for RemoteSensing Traffic Object Segmentation](<https://www.sota2.com/research/paper/6a4c882885dba69fef8b29de>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-07-08T05:00:13.057Z

Authors: Zhigang Yang, Huiguang Yao, Linmao Tian, Qiang Li, Qi Wang

Published: 2026-07-04T16:40:02.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on NWPU Traffic](<https://www.sota2.com/research/sota/semantic-segmentation-on-nwpu-traffic.md>): Semantic segmentation; NWPU Traffic; Background Accuracy: 98.73.

- [Semantic Segmentation on NWPU Traffic (test)](<https://www.sota2.com/research/sota/semantic-segmentation-on-nwpu-traffic-test.md>): Semantic segmentation; NWPU Traffic (test); Background Accuracy: 98.73.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2607.03945>)

[PDF](<https://arxiv.org/pdf/2607.03945.pdf>)