[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 …
[HTML][HTML] Deep learning approaches applied to remote sensing datasets for road extraction: A state-of-the-art review
One of the most challenging research subjects in remote sensing is feature extraction, such
as road features, from remote sensing images. Such an extraction influences multiple …
as road features, from remote sensing images. Such an extraction influences multiple …
Segment anything is not always perfect: An investigation of sam on different real-world applications
Abstract Recently, Meta AI Research approaches a general, promptable segment anything
model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B) …
model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B) …
[HTML][HTML] Agricultural greenhouses detection in high-resolution satellite images based on convolutional neural networks: Comparison of faster R-CNN, YOLO v3 and …
M Li, Z Zhang, L Lei, X Wang, X Guo - Sensors, 2020 - mdpi.com
Agricultural greenhouses (AGs) are an important facility for the development of modern
agriculture. Accurately and effectively detecting AGs is a necessity for the strategic planning …
agriculture. Accurately and effectively detecting AGs is a necessity for the strategic planning …
VNet: An end-to-end fully convolutional neural network for road extraction from high-resolution remote sensing data
A Abdollahi, B Pradhan, A Alamri - Ieee Access, 2020 - ieeexplore.ieee.org
One of the most important tasks in the advanced transportation systems is road extraction.
Extracting road region from high-resolution remote sensing imagery is challenging due to …
Extracting road region from high-resolution remote sensing imagery is challenging due to …
[HTML][HTML] Semantic segmentation-based building footprint extraction using very high-resolution satellite images and multi-source GIS data
Automatic extraction of building footprints from high-resolution satellite imagery has become
an important and challenging research issue receiving greater attention. Many recent …
an important and challenging research issue receiving greater attention. Many recent …
Segment anything, from space?
Recently, the first foundation model developed specifically for image segmentation tasks
was developed, termed the" Segment Anything Model"(SAM). SAM can segment objects in …
was developed, termed the" Segment Anything Model"(SAM). SAM can segment objects in …
Scribble-based weakly supervised deep learning for road surface extraction from remote sensing images
Road surface extraction from remote sensing images using deep learning methods has
achieved good performance, while most of the existing methods are based on fully …
achieved good performance, while most of the existing methods are based on fully …
[HTML][HTML] Building extraction based on U-Net with an attention block and multiple losses
M Guo, H Liu, Y Xu, Y Huang - Remote Sensing, 2020 - mdpi.com
Semantic segmentation of high-resolution remote sensing images plays an important role in
applications for building extraction. However, the current algorithms have some semantic …
applications for building extraction. However, the current algorithms have some semantic …
Road extraction methods in high-resolution remote sensing images: A comprehensive review
R Lian, W Wang, N Mustafa… - IEEE Journal of Selected …, 2020 - ieeexplore.ieee.org
Road extraction from high-resolution remote sensing images is a challenging but hot
research topic in the past decades. A large number of methods are invented to deal with this …
research topic in the past decades. A large number of methods are invented to deal with this …