[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 …
Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives
Water body classification from high-resolution optical remote sensing (RS) images, aiming at
classifying whether each pixel of the image is water or not, has become a hot issue in the …
classifying whether each pixel of the image is water or not, has become a hot issue in the …
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 …
[HTML][HTML] Space-time super-resolution for satellite video: A joint framework based on multi-scale spatial-temporal transformer
Satellite video is an emerging type of earth observation tool, which has attracted increasing
attention because of its application in dynamic analysis. However, most studies only focus …
attention because of its application in dynamic analysis. However, most studies only focus …
Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels
Large-scale high-resolution land-cover mapping is a way to comprehend the Earth's surface
and resolve the ecological and resource challenges facing humanity. High-resolution (≤ 1 …
and resolve the ecological and resource challenges facing humanity. High-resolution (≤ 1 …
Terrain feature-aware deep learning network for digital elevation model superresolution
Neural networks (NNs) have demonstrated the potential to recover finer textural details from
lower-resolution images by superresolution (SR). Given similar grid-based data structures …
lower-resolution images by superresolution (SR). Given similar grid-based data structures …
[HTML][HTML] SEG-Road: a segmentation network for road extraction based on transformer and CNN with connectivity structures
Acquiring road information is important for smart cities and sustainable urban development.
In recent years, significant progress has been made in the extraction of urban road …
In recent years, significant progress has been made in the extraction of urban road …
[HTML][HTML] Cost-efficient information extraction from massive remote sensing data: When weakly supervised deep learning meets remote sensing big data
With many platforms and sensors continuously observing the earth surface, the large
amount of remote sensing data presents a big data challenge. While remote sensing data …
amount of remote sensing data presents a big data challenge. While remote sensing data …
A global-information-constrained deep learning network for digital elevation model super-resolution
X Han, X Ma, H Li, Z Chen - Remote Sensing, 2023 - mdpi.com
High-resolution DEMs can provide accurate geographic information and can be widely used
in hydrological analysis, path planning, and urban design. As the main complementary …
in hydrological analysis, path planning, and urban design. As the main complementary …
Large-scale agricultural greenhouse extraction for remote sensing imagery based on layout attention network: A case study of China
Rapid and accurate agricultural greenhouse extraction with remote sensing imagery is
essential for providing spatial information for precision agriculture. Benefiting from local …
essential for providing spatial information for precision agriculture. Benefiting from local …