Yumin Tan
Biographic Data
| ID | 4418356 |
|---|---|
| NAME | Yumin Tan |
| GIVEN NAMES | Yumin |
| FAMILY NAME | Tan |
| SIGNATURE | TAN Y |
| AFFILIATIONS | Beihang University |
| ORCID | 0000-0003-0447-8223 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
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…
A hybrid remote sensing-based framework for high-resolution population mapping and reconstruction in Ecuador (2000–2024)
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…
Modelling human settlement growth and fringe patterns in the Andes through remote sensing and deep learning
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…
Remote sensing-based changes in the Ukhia Forest, Bangladesh
Remote sensing-based changes in the Ukhia Forest, Bangladesh
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…
Modelling human settlement growth and fringe patterns in the Andes through remote sensing and deep learning
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…
A hybrid remote sensing-based framework for high-resolution population mapping and reconstruction in Ecuador (2000–2024)
Geography (3 works) · Impact of Light on Environment and Health (3 works) · Land cover (3 works) · Land Use and Ecosystem Services (3 works) · Remote sensing (3 works) · Remote Sensing in Agriculture (3 works) · Deep learning (2 works) · Environmental Science (2 works) · Land use (2 works) · Multispectral image (2 works)