Automated Road and Building Extraction -- 专题文献

专题：Automated Road and Building Extraction

This cluster of papers focuses on the automatic extraction of road networks from remote sensing images, utilizing techniques such as deep learning, high-resolution imagery, and GPS traces. The research covers areas such as GIS update, aerial images, urban road networks, and map inference, with a particular emphasis on the application of these methods to road extraction.

专题：Automated Road and Building Extraction

最新文献

BANet: Bidirectional Feature Aggregation and Adaptive Multi-Scene Perception-Based Lane Detection for Autonomous Driving

Semantic Prompt and Graph-Convolution-Structure Distillation Framework for Semantic Segmentation of Remote Sensing Images

Integrating Computer Vision and GIS for Large-Scale Morphological Mapping and Driving Force Analysis of Vernacular Courtyard Dwellings

Semantic–Spatial Feature Refinement Network for Road Extraction From Remote Sensing Images

Roadside lidar-based scene understanding toward intelligent traffic perception: A comprehensive review

ATSG: Adaptive Token Linking With Segment Anything Model Guidance for Weakly Supervised Remote Sensing Image Semantic Segmentation

Progressive uncertainty-guided network for binary segmentation in high-resolution remote sensing imagery

Position-Aware Differential Denoising Transformer for Semantic Segmentation of Remote Sensing Images

Frequency-Guided Denoising Network for Semantic Segmentation of Remote Sensing Images

GlobalBuildingAtlas: an open global and complete dataset of building polygons, heights and LoD1 3D models

近5年高被引文献