Kritanai Torsri
Biographic Data
| ID | 9815140 |
|---|---|
| NAME | Kritanai Torsri |
| GIVEN NAMES | Kritanai |
| FAMILY NAME | Torsri |
| SIGNATURE | TORSRI K |
| AFFILIATIONS | Hydro-Informatics Institute, Ministry of Higher Education, Science, Research and Innovation, Bangkok 10900, Thailand |
| ORCID | 0000-0003-2795-806X |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Day–Night All-Sky Scene Classification with an Attention-Enhanced EfficientNet
All-sky cameras provide continuous hemispherical observations essential for atmospheric monitoring and observatory operations; however, automated classification of sky conditions in tropical environments remains challenging due to strong illumination variability, atmospheric scattering, and overlapping thin-cloud structures. This study proposes EfficientNet-Attention-SPP Multi-scale Network (EASMNet), a physics-aware deep learning framework for r…
Geoinformatics and Machine Learning for Shoreline Change Monitoring
Coastal erosion is a critical environmental challenge in the Upper Gulf of Thailand, driven by both natural processes and human activities. This study analyzes 35 years (1988–2023) of shoreline changes using geoinformatics, machine learning algorithms (Random Forest, Support Vector Machine, Maximum Likelihood, Minimum Distance), and the Digital Shoreline Analysis System (DSAS). The results show that the Random Forest algorithm, utilizing spectral…
Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery
Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem in small land use features. In this paper, a deep learning semantic segmentation algorithm which comprises an adjustment network architecture (LoopNet) and land use datase…
No prominent works on this page.
Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery
Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem in small land use features. In this paper, a deep learning semantic segmentation algorithm which comprises an adjustment network architecture (LoopNet) and land use datase…
Geoinformatics and Machine Learning for Shoreline Change Monitoring
Coastal erosion is a critical environmental challenge in the Upper Gulf of Thailand, driven by both natural processes and human activities. This study analyzes 35 years (1988–2023) of shoreline changes using geoinformatics, machine learning algorithms (Random Forest, Support Vector Machine, Maximum Likelihood, Minimum Distance), and the Digital Shoreline Analysis System (DSAS). The results show that the Random Forest algorithm, utilizing spectral…
Day–Night All-Sky Scene Classification with an Attention-Enhanced EfficientNet
All-sky cameras provide continuous hemispherical observations essential for atmospheric monitoring and observatory operations; however, automated classification of sky conditions in tropical environments remains challenging due to strong illumination variability, atmospheric scattering, and overlapping thin-cloud structures. This study proposes EfficientNet-Attention-SPP Multi-scale Network (EASMNet), a physics-aware deep learning framework for r…
Geography (2 works) · Remote sensing (2 works) · Remote Sensing in Agriculture (2 works) · Artificial Intelligence (1 works) · Atmospheric correction (1 works) · Coastal and Marine Dynamics (1 works) · Coastal and Marine Management (1 works) · Coastal erosion (1 works) · Computer Science (1 works) · Convolutional neural network (1 works)