Vision Transformers, a new approach for high-resolution and large-scale
mapping of canopy heights
- URL: http://arxiv.org/abs/2304.11487v1
- Date: Sat, 22 Apr 2023 22:39:03 GMT
- Title: Vision Transformers, a new approach for high-resolution and large-scale
mapping of canopy heights
- Authors: Ibrahim Fayad, Philippe Ciais, Martin Schwartz, Jean-Pierre Wigneron,
Nicolas Baghdadi, Aur\'elien de Truchis, Alexandre d'Aspremont, Frederic
Frappart, Sassan Saatchi, Agnes Pellissier-Tanon and Hassan Bazzi
- Abstract summary: We present a new vision transformer (ViT) model optimized with a classification (discrete) and a continuous loss function.
This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
- Score: 50.52704854147297
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Accurate and timely monitoring of forest canopy heights is critical for
assessing forest dynamics, biodiversity, carbon sequestration as well as forest
degradation and deforestation. Recent advances in deep learning techniques,
coupled with the vast amount of spaceborne remote sensing data offer an
unprecedented opportunity to map canopy height at high spatial and temporal
resolutions. Current techniques for wall-to-wall canopy height mapping
correlate remotely sensed 2D information from optical and radar sensors to the
vertical structure of trees using LiDAR measurements. While studies using deep
learning algorithms have shown promising performances for the accurate mapping
of canopy heights, they have limitations due to the type of architectures and
loss functions employed. Moreover, mapping canopy heights over tropical forests
remains poorly studied, and the accurate height estimation of tall canopies is
a challenge due to signal saturation from optical and radar sensors, persistent
cloud covers and sometimes the limited penetration capabilities of LiDARs.
Here, we map heights at 10 m resolution across the diverse landscape of Ghana
with a new vision transformer (ViT) model optimized concurrently with a
classification (discrete) and a regression (continuous) loss function. This
model achieves better accuracy than previously used convolutional based
approaches (ConvNets) optimized with only a continuous loss function. The ViT
model results show that our proposed discrete/continuous loss significantly
increases the sensitivity for very tall trees (i.e., > 35m), for which other
approaches show saturation effects. The height maps generated by the ViT also
have better ground sampling distance and better sensitivity to sparse
vegetation in comparison to a convolutional model. Our ViT model has a RMSE of
3.12m in comparison to a reference dataset while the ConvNet model has a RMSE
of 4.3m.
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