Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning
importance 4/10
ai assisted
Remote-Sensing Image Classification forestry & conservation stage: research
Problem
Reliably recognizing and mapping windthrow areas in forests from very-high-resolution satellite imagery.
Why it matters
Wind disturbances affect biodiversity, forest economics and management, so accurate mapping matters for conservation.
What the AI did
A U-Net-like convolutional neural network was trained to segment and recognize windthrow areas in satellite imagery of Kunashir Island, Russia.
Results
not stated in source
Impact
Evidence supports algorithm development and training, but no quantitative accuracy is stated.
Record
| Venue | Remote Sensing |
|---|---|
| Published | 2020 |
| Work type | article |
| Authors | Dmitriy E. Kislov, Kirill Korznikov |
| Citations | 62 (OpenAlex, at import time) |
| References | 64 |
| Model or method | U-Net-like CNN |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/rs12071145 |
| Open access | gold |
| Keywords | windthrow, deep learning, U-Net, satellite imagery, forest disturbance, segmentation |
Importance rating
4
real but narrower advance
Abstract states wind disturbances affect biodiversity, forest economics and management; mapping tool aids conservation and forestry, but no area or scale figures are given.
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