Ditapis dengan
Machine learning technique in the north zagros earthquake prediction
Studying the changes in seismicity, and the potential of the occurrences of large earthquakes in a seismic zone is not only extremely important from the aspect of seismological research, but it is additionally significant in the decisions of crisis management. Since, nowadays Machine learning techniques have proven the high ability for analyzing information, and discovering the relations among …
- Edisi
- Vol.22, June 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 9 hlm PDF, 2.915 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Enhancing reservoir porosity prediction from acoustic impedance and lithofaci…
Inferring underground porosity and evaluating its spatial distribution is of great significance in a wide range of Earth sciences and engineering, including hydrocarbon reservoir characterization and geothermal energy exploitation. Popular methods are largely based on the analysis of lithological cores, well logs, and seismic inversion. These methods are reliable, but they are still time-consum…
- Edisi
- Vol.16, December 2022
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 10 hlm PDF, 7.361 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
A new unified framework for supervised 3D crown segmentation (TreeisoNet) usi…
Accurately defining and isolating 3D tree space is critical for extracting and analyzing tree inventory attributes, yet it remains a challenge due to the structural complexity and heterogeneity within natural forests. This study introduces TreeisoNet, a suite of supervised deep neural networks tailored for robust 3D tree segmentation across natural forest environments. These networks are specif…
- Edisi
- Vol.15, January 2025
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 13 hlm PDF, 8.039 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
Spatially autocorrelated training and validation samples inflate performance …
Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote sensing are becoming standard analytical tools in the geosciences. A series of studies has presented the seemingly outstanding performance of CNN for predictive modelling. However, the predictive performance of such models is commonly estimated using random cross-validation, which does not account for spat…
- Edisi
- Vol.5, August 2022
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 10 hlm PDF, 5.589 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
Semantic segmentation of point cloud data using raw laser scanner measurement…
Deep learning methods based on convolutional neural networks have shown to give excellent results in semantic segmentation of images, but the inherent irregularity of point cloud data complicates their usage in semantically segmenting 3D laser scanning data. To overcome this problem, point cloud networks particularly specialized for the purpose have been implemented since 2017 but finding the m…
- Edisi
- Vol.3, January 2022
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 16 hlm PDF, 8.371 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
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