Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities
The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …
promises significant advances to support the Sustainable Development Goals (SDGs). New …
LoveDA: A remote sensing land-cover dataset for domain adaptive semantic segmentation
Deep learning approaches have shown promising results in remote sensing high spatial
resolution (HSR) land-cover mapping. However, urban and rural scenes can show …
resolution (HSR) land-cover mapping. However, urban and rural scenes can show …
Satellite video super-resolution via multiscale deformable convolution alignment and temporal grouping projection
As a new earth observation tool, satellite video has been widely used in remote-sensing
field for dynamic analysis. Video super-resolution (VSR) technique has thus attracted …
field for dynamic analysis. Video super-resolution (VSR) technique has thus attracted …
FactSeg: Foreground activation-driven small object semantic segmentation in large-scale remote sensing imagery
The small object semantic segmentation task is aimed at automatically extracting key objects
from high-resolution remote sensing (HRS) imagery. Compared with the large-scale …
from high-resolution remote sensing (HRS) imagery. Compared with the large-scale …
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 …
Transferring CNN with adaptive learning for remote sensing scene classification
Accurate classification of remote sensing (RS) images is a perennial topic of interest in the
RS community. Recently, transfer learning, especially for fine-tuning pretrained …
RS community. Recently, transfer learning, especially for fine-tuning pretrained …
Cross-sensor domain adaptation for high spatial resolution urban land-cover mapping: From airborne to spaceborne imagery
Urban land-cover information is essential for resource allocation and sustainable urban
development. Recently, deep learning algorithms have shown promising results in land …
development. Recently, deep learning algorithms have shown promising results in land …
EMTCAL: Efficient multiscale transformer and cross-level attention learning for remote sensing scene classification
In recent years, convolutional neural network (CNN)-based methods have been widely used
for remote sensing (RS) scene classification tasks and have achieved excellent results …
for remote sensing (RS) scene classification tasks and have achieved excellent results …
SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search
The scene classification approaches using deep learning have been the subject of much
attention for remote sensing imagery. However, most deep learning networks have been …
attention for remote sensing imagery. However, most deep learning networks have been …