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Computer Science > Computer Vision and Pattern Recognition

arXiv:2604.12315 (cs)
[Submitted on 14 Apr 2026]

Title:GTPBD-MM: A Global Terraced Parcel and Boundary Dataset with Multi-Modality

Authors:Zhiwei Zhang, Xingyuan Zeng, Xinkai Kong, Kunquan Zhang, Haoyuan Liang, Bohan Shi, Juepeng Zheng, Jianxi Huang, Yutong Lu, Haohuan Fu
View a PDF of the paper titled GTPBD-MM: A Global Terraced Parcel and Boundary Dataset with Multi-Modality, by Zhiwei Zhang and 9 other authors
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Abstract:Agricultural parcel extraction plays an important role in remote sensing-based agricultural monitoring, supporting parcel surveying, precision management, and ecological assessment. However, existing public benchmarks mainly focus on regular and relatively flat farmland scenes. In contrast, terraced parcels in mountainous regions exhibit stepped terrain, pronounced elevation variation, irregular boundaries, and strong cross-regional heterogeneity, making parcel extraction a more challenging problem that jointly requires visual recognition, semantic discrimination, and terrain-aware geometric understanding. Although recent studies have advanced visual parcel benchmarks and image-text farmland understanding, a unified benchmark for complex terraced parcel extraction under aligned image-text-DEM settings remains absent. To fill this gap, we present GTPBD-MM, the first multimodal benchmark for global terraced parcel extraction. Built upon GTPBD, GTPBD-MM integrates high-resolution optical imagery, structured text descriptions, and DEM data, and supports systematic evaluation under Image-only, Image+Text, and Image+Text+DEM settings. We further propose Elevation and Text guided Terraced parcel network (ETTerra), a multimodal baseline for terraced parcel delineation. Extensive experiments demonstrate that textual semantics and terrain geometry provide complementary cues beyond visual appearance alone, yielding more accurate, coherent, and structurally consistent delineation results in complex terraced scenes.
Comments: 15 pages, 11 figures. Submitted to ACM Multimedia 2026 Dataset Track
Subjects: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
Cite as: arXiv:2604.12315 [cs.CV]
  (or arXiv:2604.12315v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2604.12315
arXiv-issued DOI via DataCite

Submission history

From: Zhiwei Zhang [view email]
[v1] Tue, 14 Apr 2026 05:45:55 UTC (14,575 KB)
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