构建多时相城市植被变化检测数据集，支持细粒度变化分析
TERRA-CD: Multi-Temporal Framework for Multi-class and Semantic Change Detection
- 基于哨兵2号影像构建跨时相数据集，覆盖美欧232座城市
- 提供4类、3类和13类三套标注体系，支持多粒度变化检测
- 适用于城市环境监测、遥感算法评估与变化分析研究者
城市植被监测在理解环境变化中至关重要，但相关高质量数据集仍十分有限。为弥补这一空白，我们提出了用于分析变化检测的时序遥感数据仓库TERRA-CD，包含2019年与2024年共5,221对哨兵2号（Sentinel-2）图像，覆盖美国和欧洲232个城市的区域。该数据集包含三种不同的标注方案：4类地表覆盖映射掩码、3类植被变化掩码，以及13类语义变化掩码，可捕捉所有可能的地表覆盖转变。我们采用多种深度学习方法（包括孪生网络、STANet变体、Bi-SRNet、Changemask、后分类比较与HRSCD策略），评估了该数据集在多类别变化检测及语义变化检测任务中的有效性。相关数据集与方法已在GitHub开源。
原文摘要 · Abstract (English)
Urban vegetation monitoring plays a vital role in understanding environmental changes, yet comprehensive datasets for this purpose remain limited. To address this gap, we present the Temporal Remote-sensing Repository for Analyzing Change Detection (TERRA-CD), a benchmark dataset comprising 5,221 Sentinel-2 image pairs from 2019 and 2024, covering 232 cities across the USA and Europe. The dataset features three distinct annotation schemes: 4-class land cover mapping masks, 3-class vegetation change masks, and 13-class semantic change masks capturing all possible land cover transitions. Using various deep learning approaches including Siamese networks, STANet variants, Bi-SRNet, Changemask, Post-Classification Comparison, and HRSCD strategies, we evaluated the dataset's effectiveness for both vegetation Multi-class Change Detection as well as Semantic Change Detection. The proposed dataset and methods are available at https://github.com/omkarsoak/TERRA-CD.
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