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[HTML][HTML] Road extraction in remote sensing data: A survey
Automated extraction of roads from remotely sensed data come forth various usages ranging
from digital twins for smart cities, intelligent transportation, urban planning, autonomous …
from digital twins for smart cities, intelligent transportation, urban planning, autonomous …
A global context-aware and batch-independent network for road extraction from VHR satellite imagery
Road extraction is to automatically label the pixels of roads in satellite imagery with specific
semantic categories based on the extraction of the topographical meaningful features. For …
semantic categories based on the extraction of the topographical meaningful features. For …
Satlaspretrain: A large-scale dataset for remote sensing image understanding
Remote sensing images are useful for a wide variety of planet monitoring applications, from
tracking deforestation to tackling illegal fishing. The Earth is extremely diverse---the amount …
tracking deforestation to tackling illegal fishing. The Earth is extremely diverse---the amount …
RSSFormer: Foreground saliency enhancement for remote sensing land-cover segmentation
High spatial resolution (HSR) remote sensing images contain complex foreground-
background relationships, which makes the remote sensing land cover segmentation a …
background relationships, which makes the remote sensing land cover segmentation a …
Segment anything model for road network graph extraction
We propose SAM-Road an adaptation of the Segment Anything Model (SAM) for extracting
large-scale vectorized road network graphs from satellite imagery. To predict graph …
large-scale vectorized road network graphs from satellite imagery. To predict graph …
Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery
Geospatial object segmentation, as a particular semantic segmentation task, always faces
with larger-scale variation, larger intra-class variance of background, and foreground …
with larger-scale variation, larger intra-class variance of background, and foreground …
CoANet: Connectivity attention network for road extraction from satellite imagery
Extracting roads from satellite imagery is a promising approach to update the dynamic
changes of road networks efficiently and timely. However, it is challenging due to the …
changes of road networks efficiently and timely. However, it is challenging due to the …
A review of high-definition map creation methods for autonomous driving
Autonomous driving has been among the most popular and challenging topics in the past
few years. Among all modules in autonomous driving, High-definition (HD) map has drawn …
few years. Among all modules in autonomous driving, High-definition (HD) map has drawn …
Split depth-wise separable graph-convolution network for road extraction in complex environments from high-resolution remote-sensing images
Road information from high-resolution remote-sensing images is widely used in various
fields, and deep-learning-based methods have effectively shown high road-extraction …
fields, and deep-learning-based methods have effectively shown high road-extraction …
Structured bird's-eye-view traffic scene understanding from onboard images
Autonomous navigation requires structured representation of the road network and instance-
wise identification of the other traffic agents. Since the traffic scene is defined on the ground …
wise identification of the other traffic agents. Since the traffic scene is defined on the ground …