Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep
  Learning on Satellite Imagery   Characterizing the processes leading to deforestation is critical to the
development and implementation of targeted forest conservation and management
policies. In this work, we develop a deep learning model called ForestNet to
classify the drivers of primary forest loss in Indonesia, a country with one of
the highest deforestation rates in the world. Using satellite imagery,
ForestNet identifies the direct drivers of deforestation in forest loss patches
of any size. We curate a dataset of Landsat 8 satellite images of known forest
loss events paired with driver annotations from expert interpreters. We use the
dataset to train and validate the models and demonstrate that ForestNet
substantially outperforms other standard driver classification approaches. In
order to support future research on automated approaches to deforestation
driver classification, the dataset curated in this study is publicly available
at https://stanfordmlgroup.github.io/projects/forestnet .

31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg