NeXt2Former-CD: Efficient Remote Sensing Change Detection with Modern Vision Architectures

• URL: http://arxiv.org/abs/2602.18717v1
• Date: Sat, 21 Feb 2026 04:51:53 GMT
• Title: NeXt2Former-CD: Efficient Remote Sensing Change Detection with Modern Vision Architectures
• Authors: Yufan Wang, Sokratis Makrogiannis, Chandra Kambhamettu,
• Abstract summary: NeXt2Former-CD is an end-to-end framework that integrates a Siamese ConvNeXt encoder with DINOv3 weights, a deformable attention-based temporal fusion module, and a Mask2Former decoder.<n>Our model maintains inference latency comparable to SSM-based approaches, suggesting it is practical for high-resolution change detection tasks.
• Score: 11.733678383805897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State Space Models (SSMs) have recently gained traction in remote sensing change detection (CD) for their favorable scaling properties. In this paper, we explore the potential of modern convolutional and attention-based architectures as a competitive alternative. We propose NeXt2Former-CD, an end-to-end framework that integrates a Siamese ConvNeXt encoder initialized with DINOv3 weights, a deformable attention-based temporal fusion module, and a Mask2Former decoder. This design is intended to better tolerate residual co-registration noise and small object-level spatial shifts, as well as semantic ambiguity in bi-temporal imagery. Experiments on LEVIR-CD, WHU-CD, and CDD datasets show that our method achieves the best results among the evaluated methods, improving over recent Mamba-based baselines in both F1 score and IoU. Furthermore, despite a larger parameter count, our model maintains inference latency comparable to SSM-based approaches, suggesting it is practical for high-resolution change detection tasks.

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