Review on Convolutional Neural Networks (CNN) in vegetation remote sensing
Identifying and characterizing vascular plants in time and space is required in various
disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …
disciplines, eg in forestry, conservation and agriculture. Remote sensing emerged as a key …
Uncovering ecological patterns with convolutional neural networks
Using remotely sensed imagery to identify biophysical components across landscapes is an
important avenue of investigation for ecologists studying ecosystem dynamics. With high …
important avenue of investigation for ecologists studying ecosystem dynamics. With high …
Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks
The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-
flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods …
flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods …
Seasonal Arctic sea ice forecasting with probabilistic deep learning
Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice
extent. This has far-reaching consequences for indigenous and local communities, polar …
extent. This has far-reaching consequences for indigenous and local communities, polar …
Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery
Recent technological advances in remote sensing sensors and platforms, such as high-
resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of …
resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of …
Individual tree crown segmentation and crown width extraction from a heightmap derived from aerial laser scanning data using a deep learning framework
Deriving individual tree crown (ITC) information from light detection and ranging (LiDAR)
data is of great significance to forest resource assessment and smart management. After …
data is of great significance to forest resource assessment and smart management. After …
Land use land cover classification with U-net: Advantages of combining sentinel-1 and sentinel-2 imagery
The U-net is nowadays among the most popular deep learning algorithms for land use/land
cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar …
cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar …
[HTML][HTML] Spatially autocorrelated training and validation samples inflate performance assessment of convolutional neural networks
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 …
sensing are becoming standard analytical tools in the geosciences. A series of studies has …
Deep learning segmentation and classification for urban village using a worldview satellite image based on U-Net
Z Pan, J Xu, Y Guo, Y Hu, G Wang - Remote Sensing, 2020 - mdpi.com
Unplanned urban settlements exist worldwide. The geospatial information of these areas is
critical for urban management and reconstruction planning but usually unavailable …
critical for urban management and reconstruction planning but usually unavailable …
Individual tree detection and species classification of Amazonian palms using UAV images and deep learning
MP Ferreira, DRA de Almeida… - Forest Ecology and …, 2020 - Elsevier
Abstract Information regarding the spatial distribution of palm trees in tropical forests is
crucial for commercial exploitation and management. However, spatially continuous …
crucial for commercial exploitation and management. However, spatially continuous …