[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 …
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 …
Large-scale deep learning based binary and semantic change detection in ultra high resolution remote sensing imagery: From benchmark datasets to urban …
With the acceleration of urban expansion, urban change detection (UCD), as a significant
and effective approach, can provide the change information with respect to geospatial …
and effective approach, can provide the change information with respect to geospatial …
Object detection and image segmentation with deep learning on Earth observation data: A review—Part II: Applications
In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …
investigating aggregated classes. The increase in data with a very high spatial resolution …
From center to surrounding: An interactive learning framework for hyperspectral image classification
Owing to rich spectral and spatial information, hyperspectral image (HSI) can be utilized for
finely classifying different land covers. With the emergence of deep learning techniques …
finely classifying different land covers. With the emergence of deep learning techniques …
SiamHYPER: Learning a hyperspectral object tracker from an RGB-based tracker
Hyperspectral videos can provide the spatial, spectral, and motion information of targets,
which makes it possible to track camouflaged targets that are similar to the background …
which makes it possible to track camouflaged targets that are similar to the background …
BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery
Automatic road extraction from high-resolution remote sensing imagery has various
applications like urban planning and automatic navigation. Existing methods for automatic …
applications like urban planning and automatic navigation. Existing methods for automatic …
Simultaneous road surface and centerline extraction from large-scale remote sensing images using CNN-based segmentation and tracing
Accurate and up-to-date road maps are of great importance in a wide range of applications.
Unfortunately, automatic road extraction from high-resolution remote sensing images …
Unfortunately, automatic road extraction from high-resolution remote sensing images …
Reconstruction bias U-Net for road extraction from optical remote sensing images
Automatic road extraction from remote sensing images plays an important role for
navigation, intelligent transportation, and road network update, etc. Convolutional neural …
navigation, intelligent transportation, and road network update, etc. Convolutional neural …