Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Activation Regression for Continuous Domain Generalization with
  Applications to Crop Classification   Geographic variance in satellite imagery impacts the ability of machine
learning models to generalise to new regions. In this paper, we model
geographic generalisation in medium resolution Landsat-8 satellite imagery as a
continuous domain adaptation problem, demonstrating how models generalise
better with appropriate domain knowledge. We develop a dataset spatially
distributed across the entire continental United States, providing macroscopic
insight into the effects of geography on crop classification in multi-spectral
and temporally distributed satellite imagery. Our method demonstrates improved
generalisability from 1) passing geographically correlated climate variables
along with the satellite data to a Transformer model and 2) regressing on the
model features to reconstruct these domain variables. Combined, we provide a
novel perspective on geographic generalisation in satellite imagery and a
simple-yet-effective approach to leverage domain knowledge. Code is available
at: \url{https://github.com/samar-khanna/cropmap}

23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: math-ph