Jiarui Lin

Hannuo Zhang, Huihui Li, Jiarui Lin et al.

Optical remote sensing and Synthetic Aperture Radar(SAR) remote sensing are crucial for earth observation, offering complementary capabilities. While optical sensors provide high-quality images, they are limited by weather and lighting conditions. In contrast, SAR sensors can operate effectively under adverse conditions. This letter proposes a GAN-based SAR-to-optical image translation method named Seg-CycleGAN, designed to enhance the accuracy of ship target translation by leveraging semantic information from a pre-trained semantic segmentation model. Our method utilizes the downstream task of ship target semantic segmentation to guide the training of image translation network, improving the quality of output Optical-styled images. The potential of foundation-model-annotated datasets in SAR-to-optical translation tasks is revealed. This work suggests broader research and applications for downstream-task-guided frameworks. The code will be available at https://github.com/NPULHH/

Jin Han, Xin-Zheng Lu, Jia-Rui Lin

Large Foundation Models (LFMs) have demonstrated significant advantages in civil engineering, but they primarily focus on textual and visual data, overlooking the rich semantic, spatial, and topological features in BIM (Building Information Modelling) models. Therefore, this study develops the first large-scale graph neural network (GNN), BIGNet, to learn, and reuse multidimensional design features embedded in BIM models. Firstly, a scalable graph representation is introduced to encode the "semantic-spatial-topological" features of BIM components, and a dataset with nearly 1 million nodes and 3.5 million edges is created. Subsequently, BIGNet is proposed by introducing a new message-passing mechanism to GraphMAE2 and further pretrained with a node masking strategy. Finally, BIGNet is evaluated in various transfer learning tasks for BIM-based design checking. Results show that: 1) homogeneous graph representation outperforms heterogeneous graph in learning design features, 2) considering local spatial relationships in a 30 cm radius enhances performance, and 3) BIGNet with GAT (Graph Attention Network)-based feature extraction achieves the best transfer learning results. This innovation leads to a 72.7% improvement in Average F1-score over non-pretrained models, demonstrating its effectiveness in learning and transferring BIM design features and facilitating their automated application in future design and lifecycle management.