搜索结果: 1-6 共查到“林学 Neural”相关记录6条 . 查询时间(0.339 秒)
Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery
Convolutional Neural Networks (CNN) deep learning invasive species orthophoto remote sensing tree species Unmanned Aerial Vehicles (UAV) vegetation succession
<
2024/1/19
Unmanned Aerial Vehicles (UAV) greatly extended our possibilities to acquire high resolution remote sensing data for assessing the spatial distribution of species composition and vegetation characteri...
Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series
Synthetic Aperture Radar Sentinel-1 forest mapping deforestation monitoring deep learning convolutional neural networks
<
2023/12/1
The monitoring of land cover and land use patterns is pivotal in the joint effort to fight deforestation in the Amazon and study its relation to climate change effects with respect to anthropogenic ac...
Comparison of partial least squares-discriminant analysis, support vector machines and deep neural networks for spectrometric classification of seed vigour in a broad range of tree species
least squares-discriminant analysis spectrometric classification seed tree species
<
2023/6/5
Seed vigour significantly influences the seed production and plant regeneration performance. The capability of NIR spectroscopy to identify seed vigour across multiple tree species rapidly and cost-ef...
Vegetation Cover Estimation Using Convolutional Neural Networks
CNN segmentation UAV vegetation cover estimation
<
2024/2/4
Vegetation is an important parameter in all bio- and ecosystems, and it should be monitored to conserve and restore the environment. This paper presents a design and implementation of a compact system...
Predicting the earthwork width and determining the annual growth loss due to forest road construction using artificial neural network and ArcGIS
area of road earthwork hillside slope roadside rock share
<
2016/8/2
The area of forest destruction as well as the annual growth loss due to road construction before constructing a road was predicted. To do this, road cross sections of 88 points along the 10 km propose...