A Multi-Modal Remote Sensing Image Collaborative Fusion Network for Construction and Demolition Waste Extraction in Urban Areas

Construction and demolition waste (C&DW) mapping is crucial for urban environmental management, yet accurate extraction remains challenging due to complex backgrounds, spectral confusion, and irregular boundaries. To address these issues, this study presents the first optical-SAR multimodal semantic segmentation dataset based on GF-3 SAR, Sentinel-1 SAR, and Sentinel-2 optical imagery with a unified spatial resolution of 3 m for fine-scale C&DW extraction and proposes CDW-Net, a dual-branch multimodal fusion framework. The framework incorporates a Cross-Frequency Interaction (CFI) module for cross-modal feature fusion and a Multi-Source Boundary Attention Module (MS-BAM) for improved boundary representation. Experiments in Beijing show that CDW-Net achieves an IoU of 0.8470 and an F1-score of 0.9171, outperforming state-of-the-art unimodal and multimodal methods. Transfer experiments in Guangzhou achieved an IoU of 0.8247 and an F1-score of 0.9040 on manually interpreted validation samples, demonstrating good cross-regional transferability. The proposed dataset and framework provide valuable support for fine-scale C&DW monitoring in complex urban environments.