THE ISPRS BENCHMARK ON URBAN OBJECT CLASSIFICATION AND 3D BUILDING RECONSTRUCTION

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257. Masked Topology Convolutional Network for Classification and Segmentation of Remote Sensing Images F. Wang et al.
258. Building Extraction from Airborne Laser Scanning Data: An Analysis of the State of the Art I. Tomljenovic et al.
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260. 3D Lidar Point Cloud Segmentation for Automated Driving R. Abbasi et al.
261. Large-Scale 3-D Building Reconstruction in LoD2 From ALS Point Clouds G. Kong et al.
262. A Hierarchical Building Detection Method for Very High Resolution Remotely Sensed Images Combined with DSM Using Graph Cut Optimization R. Qin & W. Fang
263. Confidence-Weighted Dual-Teacher Networks With Biased Contrastive Learning for Semi-Supervised Semantic Segmentation in Remote Sensing Images Y. Xin et al.
264. Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review P. Chen et al.
265. Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs M. Awrangjeb & C. Fraser
266. LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas Z. Ye et al.
267. Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works C. Tao et al.
268. Integrating Gate and Attention Modules for High-Resolution Image Semantic Segmentation Z. Zheng et al.
269. ESDINet: Efficient Shallow-Deep Interaction Network for Semantic Segmentation of High-Resolution Aerial Images X. Zhang et al.
270. Research on Multi-Stage Optimization for High-Precision Digital Surface Model and True Digital Orthophoto Map Generation Methods Y. Ge et al.
271. MoViM: A Hybrid CNN Vision Mamba Network for Lightweight Semantic Segmentation of Multimodal Remote Sensing Images W. Du et al.
272. A Dual-Branch Network Leveraging Heterogeneous Features for Semantic Segmentation of Large-Scale Point Clouds K. Liu et al.
273. A new weakly supervised approach for ALS point cloud semantic segmentation P. Wang & W. Yao
274. Global Context-Aware Point Cloud Semantic Segmentation Method Based on KPConv 明. 余