Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Attention-Based System for Damage Assessment Using Satellite Imagery   When disaster strikes, accurate situational information and a fast, effective
response are critical to save lives. Widely available, high resolution
satellite images enable emergency responders to estimate locations, causes, and
severity of damage. Quickly and accurately analyzing the extensive amount of
satellite imagery available, though, requires an automatic approach. In this
paper, we present Siam-U-Net-Attn model - a multi-class deep learning model
with an attention mechanism - to assess damage levels of buildings given a pair
of satellite images depicting a scene before and after a disaster. We evaluate
the proposed method on xView2, a large-scale building damage assessment
dataset, and demonstrate that the proposed approach achieves accurate damage
scale classification and building segmentation results simultaneously.

17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci