Preprints
https://doi.org/10.5194/essd-2026-759
https://doi.org/10.5194/essd-2026-759
05 Nov 2026
 | 05 Nov 2026
Status: this preprint is currently under review for the journal ESSD.

ContrailStruct30: a 30 m three-band thermal-infrared dataset for contrail segmentation

Jianing Yu, Lixing Zhao, Xiaoxuan Zhou, Liyuan Li, Xinyue Ni, Yuqing Xue, Kefang Wang, Ying Li, Guanzheng Du, Hanwei Lu, Chaoshuai Tan, Mingxin Dai, Zhuoyue Hu, Xiaoyan Li, and Fansheng Chen

Abstract. Aircraft contrails are an important source of aviation's non-CO2 climate effects, and fine-scale satellite observations provide a means of characterizing their spatial structure. Distinguishing narrow, adjacent, and overlapping contrails from complex backgrounds requires reference data with explicit pixel-level annotations. We present ContrailStruct30, a three-band thermal-infrared contrail segmentation dataset derived from Sustainable Development Science Satellite 1 (SDGSAT-1) observations at 30 m nominal sampling. The dataset contains 19,455 patches of 256 × 256 pixels from 819 source-scene entries acquired on 305 dates between August 2022 and September 2024, including 9,023 positive patches and 10,432 deliberately selected difficult negatives. Each sample provides an enhanced RGB image, corresponding three-band source values stored as uint16, and a human-corrected binary mask. Construction began with 300 manually annotated patches identified through ADS-B-assisted searches and continued through four rounds of model-assisted annotation, manual correction, and expert review. Fixed training, validation, and test partitions separate both source scenes and acquisition dates. As an application baseline, we developed a MaxViT–U-Net segmentation model and compared it with three public models under a shared training protocol to evaluate foreground recovery, centerline agreement, and difficult-background discrimination. Across three training seeds, the baseline achieved a mean positive-patch test Dice of 0.8230 ± 0.0056 (mean ± standard deviation), with strong foreground overlap and centerline agreement in the common evaluation. By combining fine-scale thermal-infrared observations, diverse contrail morphologies, expert-reviewed labels, and corresponding image representations, ContrailStruct30 provides a reusable resource for extracting fine contrail structures and developing and evaluating high-resolution segmentation methods. The dataset presented in this study corresponds to Version 3, which is publicly available via Figshare at https://doi.org/10.6084/m9.figshare.33435496.v3 (Yu et al., 2026).

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Jianing Yu, Lixing Zhao, Xiaoxuan Zhou, Liyuan Li, Xinyue Ni, Yuqing Xue, Kefang Wang, Ying Li, Guanzheng Du, Hanwei Lu, Chaoshuai Tan, Mingxin Dai, Zhuoyue Hu, Xiaoyan Li, and Fansheng Chen

Status: open (until 12 Dec 2026)

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Jianing Yu, Lixing Zhao, Xiaoxuan Zhou, Liyuan Li, Xinyue Ni, Yuqing Xue, Kefang Wang, Ying Li, Guanzheng Du, Hanwei Lu, Chaoshuai Tan, Mingxin Dai, Zhuoyue Hu, Xiaoyan Li, and Fansheng Chen

Model code and software

Baseline Segmentation Models and Evaluation Pipeline for ContrailStruct30 Jianing Yu https://github.com/jianingyu777/contrail-structural-benchmark

Jianing Yu, Lixing Zhao, Xiaoxuan Zhou, Liyuan Li, Xinyue Ni, Yuqing Xue, Kefang Wang, Ying Li, Guanzheng Du, Hanwei Lu, Chaoshuai Tan, Mingxin Dai, Zhuoyue Hu, Xiaoyan Li, and Fansheng Chen
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Latest update: 05 Nov 2026
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Short summary
Aviation contributes to climate change through high-altitude cloud trails. However, tracking these thin clouds with satellites remains difficult due to a lack of detailed reference data. To solve this, we created a high-resolution satellite dataset to identify individual aircraft trails. Using advanced deep learning models, we showed that these trails can be mapped with precision. This work bridges satellite monitoring and climate research, helping build greener aviation technologies.
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