Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Country-wide high-resolution vegetation height mapping with Sentinel-2   Sentinel-2 multi-spectral images collected over periods of several months
were used to estimate vegetation height for Gabon and Switzerland. A deep
convolutional neural network (CNN) was trained to extract suitable spectral and
textural features from reflectance images and to regress per-pixel vegetation
height. In Gabon, reference heights for training and validation were derived
from airborne LiDAR measurements. In Switzerland, reference heights were taken
from an existing canopy height model derived via photogrammetric surface
reconstruction. The resulting maps have a mean absolute error (MAE) of 1.7 m in
Switzerland and 4.3 m in Gabon (a root mean square error (RMSE) of 3.4 m and
5.6 m, respectively), and correctly estimate vegetation heights up to >50 m.
They also show good qualitative agreement with existing vegetation height maps.
Our work demonstrates that, given a moderate amount of reference data (i.e.,
2000 km$^2$ in Gabon and $\approx$5800 km$^2$ in Switzerland), high-resolution
vegetation height maps with 10 m ground sampling distance (GSD) can be derived
at country scale from Sentinel-2 imagery.

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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: q-alg