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

arXiv 2605.14651 ↗First posted 14 May 2026cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 14 May 2026.

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References · 9

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  3. 2016Pyramid Scene Parsing Networkcited by 2,479
  4. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,245
  5. 2018Fully Convolutional Siamese Networks for Change Detectioncited by 306
  6. 2017LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentationcited by 214
  7. 2021Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Imagescited by 43
  8. 2018Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Imagescited by 40
  9. 2020Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mappingcited by 22

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