Ditapis dengan
Evaluating deep-learning models for debris-covered glacier mapping
In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accur…
- Edisi
- Vol.12, December 2021
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 17 hlm PDF, 31.040 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Using three dimensional convolutional neural networks for denoising echosound…
It is estimated that over 80% of the world’s oceans are unexplored and unmapped limiting our understanding of ocean systems. Due to data collection rates of modern survey technologies such as swathe multibeam echosounders (MBES) and initiatives such as Seabed 2030, there is ever-increasing increasing volume of seafloor data collected. These large data volumes present significant challenges ar…
- Edisi
- Vol.5, March 2020
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 10 hlm PDF, 2.691 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
Semantic segmentation of raw multispectral laser scanning data from urban env…
Real-time semantic segmentation of point clouds has increasing importance in applications related to 3D city modelling and mapping, automated inventory of forests, autonomous driving and mobile robotics. Current state-of-the-art point cloud semantic segmentation methods rely heavily on the availability of 3D laser scanning data. This is problematic in regards of low-latency, real-time applicati…
- Edisi
- Vol.12, April 2024
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 17 hlm PDF, 21.188 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
Improving spatial transferability of deep learning models for small-field cro…
Predicting crop yield using deep learning (DL) and remote sensing is a promising technique in agriculture. In smallholder agriculture (
- Edisi
- Vol.12, April 2024
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 11 hlm PDF, 7.586 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
Transfer learning from citizen science photographs enables plant species iden…
Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that Convolutional Neural Networks (CNNs) accurately predict plant species and communities in high-resolution remote sensing data, in particular with data at the centimeter sca…
- Edisi
- Vol.5, August 2022
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 23 hlm PDF, 83.917 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
Comparison of neural networks and k-nearest neighbors methods in forest stand…
In the remote sensing of forests, point cloud data from airborne laser scanning contains high-value information for predicting the volume of growing stock and the size of trees. At the same time, laser scanning data allows a very high number of potential features that can be extracted from the point cloud data for predicting the forest variables. In some methods, the features are first extracte…
- Edisi
- Vol.4, April 2022
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 17 hlm PDF, 13.215 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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