学术成果
- 1.
Foundation model for generalist remote sensing intelligence: Potentials and prospects — Supplementary Material
Science Bulletin. 2024 Sep 19.
Zhang M, Yang B, Hu X, Gong J#, Zhang Z.
The paper systematically analyzes the progress of remote sensing big models in scene classification/retrieval, object detection, land cover classification (semantic segmentation), change detection, video tracking, and geoscience applications in the past 5 years at home and abroad. It proposes a unified computing framework for remote sensing big models, including domain specific deep learning frameworks and sample libraries, fusion and fine-tuning of multimodal geoscience domain knowledge, bidirectional human-machine feedback mechanism, quality and reliability evaluation, and transparent downstream application models. Based on this framework, the research team has developed a multimodal multi task remote sensing large model LuoJia with 2.8 billion parameters SmartSensing (Luojia The prototype system was successfully deployed at Shandong Haiyang Dongfang Aerospace Port, inspired by the project.
- 2.
Class-Agnostic Topological Directional Graph Extraction From Remote Sensing Images
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 1265-1274
Bingnan Yang, Mi Zhang, Zhan Zhang, Zhili Zhang, Xiangyun Hu
Rapid development in automatic vector extraction from remote sensing images has been witnessed in recent years. However, the vast majority of existing works concentrate on a specific target, fragile to category variety, and hardly achieve stable performance crossing different categories. In this work, we propose an innovative class-agnostic model, namely TopDiG, to directly extract topological directional graphs from remote sensing images and solve these issues. Firstly, TopDiG employs a topology-concentrated node detector (TCND) to detect nodes and obtain compact perception of topological components. Secondly, we propose a dynamic graph supervision (DGS) strategy to dynamically generate adjacency graph labels from unordered nodes. Finally, the directional graph (DiG) generator module is designed to construct topological directional graphs from predicted nodes. Experiments on the Inria, CrowdAI, GID, GF2 and Massachusetts datasets empirically demonstrate that TopDiG is class-agnostic and achieves competitive performance on all datasets.
