Deep learning modelling techniques: current progress, applications, advantages, and challenges
Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
A review of landcover classification with very-high resolution remotely sensed optical images—Analysis unit, model scalability and transferability
As an important application in remote sensing, landcover classification remains one of the
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …
[HTML][HTML] Comparison of multi-source satellite images for classifying marsh vegetation using DeepLabV3 Plus deep learning algorithm
The accurate classification of wetland vegetation is essential for rapid assessment and
management. The Honghe National Nature Reserve (HNNR), located in Northeast China …
management. The Honghe National Nature Reserve (HNNR), located in Northeast China …
Land use land cover classification of remote sensing images based on the deep learning approaches: a statistical analysis and review
Over the last few years, deep learning (DL) techniques have gained popularity and have
become the new standard for data processing in remote sensing analysis. Deep learning …
become the new standard for data processing in remote sensing analysis. Deep learning …
Latest trends on tree classification and segmentation using UAV data—a review of agroforestry applications
When it comes to forest management and protection, knowledge is key. Therefore, forest
mapping is crucial to obtain the required knowledge towards profitable resource exploitation …
mapping is crucial to obtain the required knowledge towards profitable resource exploitation …
Application of deep learning in multitemporal remote sensing image classification
X Cheng, Y Sun, W Zhang, Y Wang, X Cao, Y Wang - Remote Sensing, 2023 - mdpi.com
The rapid advancement of remote sensing technology has significantly enhanced the
temporal resolution of remote sensing data. Multitemporal remote sensing image …
temporal resolution of remote sensing data. Multitemporal remote sensing image …
Retinal disease detection using deep learning techniques: a comprehensive review
S Muchuchuti, S Viriri - Journal of Imaging, 2023 - mdpi.com
Millions of people are affected by retinal abnormalities worldwide. Early detection and
treatment of these abnormalities could arrest further progression, saving multitudes from …
treatment of these abnormalities could arrest further progression, saving multitudes from …
A deep learning model for automatic plastic mapping using unmanned aerial vehicle (UAV) data
Although plastic pollution is one of the most noteworthy environmental issues nowadays,
there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which …
there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which …
Mapping salt marsh along coastal South Carolina using U-Net
Coastal wetland mapping is often difficult because of the heterogeneous vegetation
compositions and associated tidal effects. In this study, we employed the U-Net and …
compositions and associated tidal effects. In this study, we employed the U-Net and …
Deep learning semantic segmentation for water level estimation using surveillance camera
The interest in visual-based surveillance systems, especially in natural disaster applications,
such as flood detection and monitoring, has increased due to the blooming of surveillance …
such as flood detection and monitoring, has increased due to the blooming of surveillance …