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arXiv · 2605.14651

TERRA-CD: Multi-Temporal Framework for Multi-class and Semantic Change Detection

Omkar Oak·Rukmini Nazre·Rujuta Budke·Suraj Sawant
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Abstract

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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Omkar Oak, Rukmini Nazre, Rujuta Budke, Suraj Sawant. 2026-05-14. TERRA-CD: Multi-Temporal Framework for Multi-class and Semantic Change Detection. https://arxiv.org/abs/2605.14651

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