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ly
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c
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e
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c
c
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ra
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ty
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x
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e
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e
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ig
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re
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e
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tes
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l
n
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ra
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wo
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k
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NN
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se
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ima
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las
sifica
ti
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n
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x
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h
n
i
q
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e
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d
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ti
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y
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l
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s
t
h
a
t
c
o
n
tri
b
u
te
m
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st
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o
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e
tec
ti
o
n
d
e
c
isio
n
s.
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y
g
e
n
e
ra
ti
n
g
in
tu
i
ti
v
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v
isu
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l
e
x
p
lan
a
ti
o
n
s,
F
D
X
e
n
a
b
les
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rs
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n
d
e
rsta
n
d
,
v
a
li
d
a
te,
a
n
d
tru
st
t
h
e
m
o
d
e
l'
s
p
re
d
icti
o
n
s
,
t
h
e
re
b
y
s
u
p
p
o
rti
n
g
tran
s
p
a
re
n
t
a
n
d
a
c
c
o
u
n
ta
b
le
d
e
c
isio
n
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m
a
k
i
n
g
.
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x
p
e
rime
n
tal
e
v
a
lu
a
ti
o
n
d
e
m
o
n
stra
tes
t
h
a
t
t
h
e
p
r
o
p
o
se
d
fra
m
e
wo
rk
e
ffe
c
ti
v
e
ly
d
isti
n
g
u
is
h
e
s
wild
fire
ima
g
e
s
fro
m
n
o
n
-
fi
re
sc
e
n
e
s
wh
il
e
p
r
o
v
i
d
i
n
g
m
e
a
n
in
g
f
u
l
v
i
su
a
l
in
ter
p
re
t
a
ti
o
n
s
t
h
a
t
imp
r
o
v
e
m
o
d
e
l
tran
sp
a
re
n
c
y
with
o
u
t
c
o
m
p
ro
m
i
sin
g
d
e
tec
ti
o
n
p
e
rfo
rm
a
n
c
e
.
Th
e
fin
d
in
g
s
h
ig
h
li
g
h
t
t
h
e
p
o
ten
ti
a
l
o
f
e
x
p
l
a
in
a
b
le
AI
to
stre
n
g
t
h
e
n
t
h
e
re
li
a
b
il
it
y
,
u
sa
b
il
it
y
,
a
n
d
p
ra
c
ti
c
a
l
d
e
p
l
o
y
m
e
n
t
o
f
in
telli
g
e
n
t
wil
d
fire
m
o
n
it
o
r
in
g
sy
ste
m
s
fo
r
e
n
v
i
ro
n
m
e
n
tal
su
rv
e
il
lan
c
e
,
d
isa
ste
r
m
a
n
a
g
e
m
e
n
t,
a
n
d
e
a
rly
wa
rn
in
g
a
p
p
li
c
a
ti
o
n
s.
K
ey
w
o
r
d
s
:
C
o
m
p
u
ter
v
is
io
n
C
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
Dee
p
lear
n
in
g
E
x
p
lain
ab
le
AI
Mo
d
el
in
ter
p
r
eta
b
ilit
y
W
ild
f
ir
e
d
etec
tio
n
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
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r
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s
p
o
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ing
A
uth
o
r
:
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o
d
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Nag
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Ma
d
h
u
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Dep
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tm
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f
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h
,
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a
k
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is
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n
a
Sid
d
h
ar
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a
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n
g
in
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C
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lleg
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Vij
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a
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I
n
d
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m
ail: m
ad
h
u
litt
@
g
m
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co
m
1.
I
NT
RO
D
UCT
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O
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W
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f
ir
es
h
av
e
b
ec
o
m
e
o
n
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o
f
th
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m
o
s
t
d
estru
ctiv
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n
atu
r
al
h
az
ar
d
s
wo
r
ld
wid
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c
au
s
in
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ex
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s
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v
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m
en
tal
d
eg
r
ad
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b
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ec
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is
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u
p
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,
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d
th
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ts
to
h
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m
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if
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I
n
r
ec
en
t
y
ea
r
s
,
th
e
f
r
eq
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n
cy
,
i
n
ten
s
ity
,
an
d
g
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g
r
a
p
h
ical
d
is
tr
ib
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tio
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o
f
wild
f
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h
av
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cr
ea
s
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c
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id
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ab
ly
d
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to
clim
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ch
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g
e,
p
r
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lo
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g
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d
r
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g
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ts
,
r
is
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tem
p
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an
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an
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f
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T
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d
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b
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if
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as
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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Sci
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v
er
ag
e,
d
elay
ed
r
esp
o
n
s
e
tim
es,
h
ig
h
o
p
er
atio
n
al
co
s
ts
,
an
d
s
u
s
ce
p
tib
ilit
y
to
en
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
s
u
ch
as
s
m
o
k
e,
f
o
g
,
an
d
ch
an
g
i
n
g
illu
m
in
atio
n
.
As
wild
f
ir
e
b
eh
av
io
r
is
h
ig
h
l
y
d
y
n
am
ic
an
d
u
n
p
r
ed
ictab
le,
t
r
ad
itio
n
al
m
o
n
ito
r
in
g
ap
p
r
o
a
c
h
es
ar
e
f
r
eq
u
en
tly
u
n
ab
le
to
p
r
o
v
id
e
tim
ely
a
n
d
ac
cu
r
ate
d
etec
tio
n
o
v
er
lar
g
e
g
eo
g
r
ap
h
ical
ar
ea
s
.
T
o
ad
d
r
ess
th
ese
lim
itatio
n
s
,
r
ec
en
t
ad
v
an
ce
s
in
r
em
o
te
s
en
s
in
g
,
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
,
wir
eless
s
en
s
o
r
n
etwo
r
k
s
(
W
SNs
)
,
u
n
m
an
n
ed
ae
r
ial
v
e
h
icles
(
UAVs)
,
an
d
ar
tific
ial
in
tellig
en
ce
(
AI
)
h
av
e
en
ab
led
th
e
d
e
v
elo
p
m
e
n
t
o
f
au
to
m
ated
wild
f
ir
e
d
etec
tio
n
s
y
s
tem
s
ca
p
ab
le
o
f
co
n
tin
u
o
u
s
ly
an
aly
zin
g
lar
g
e
v
o
lu
m
es o
f
h
eter
o
g
en
eo
u
s
en
v
ir
o
n
m
en
tal
d
ata
[
2
]
,
[
3
]
.
Am
o
n
g
th
ese
tech
n
o
lo
g
ies,
AI
h
as
em
er
g
e
d
as
a
tr
an
s
f
o
r
m
ativ
e
s
o
lu
tio
n
f
o
r
in
tellig
en
t
wild
f
ir
e
m
o
n
ito
r
in
g
.
Ma
ch
in
e
lear
n
in
g
alg
o
r
ith
m
s
ca
n
a
u
to
m
atica
lly
id
en
tify
co
m
p
lex
p
atter
n
s
ass
o
ciate
d
wit
h
wild
f
ir
e
o
cc
u
r
r
en
ce
b
y
an
aly
z
in
g
s
atellite
im
ag
er
y
,
ae
r
ial
p
h
o
to
g
r
a
p
h
s
,
m
eteo
r
o
lo
g
ical
v
ar
iab
les,
an
d
s
en
s
o
r
m
ea
s
u
r
em
en
ts
.
C
o
m
p
a
r
ed
with
co
n
v
en
tio
n
al
r
u
le
-
b
ased
a
p
p
r
o
ac
h
es,
AI
-
d
r
iv
en
s
y
s
tem
s
p
r
o
v
i
d
e
g
r
ea
ter
ad
ap
tab
ilit
y
,
s
ca
lab
ilit
y
,
an
d
p
r
ed
ictiv
e
ca
p
ab
ilit
y
,
en
ab
li
n
g
ea
r
lier
wild
f
ir
e
d
etec
tio
n
an
d
r
ed
u
cin
g
f
alse a
lar
m
r
ates.
R
ec
en
t
d
ev
elo
p
m
en
ts
h
av
e
d
em
o
n
s
tr
ated
th
at
AI
m
o
d
els
ca
n
s
ig
n
if
ican
tly
im
p
r
o
v
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es
f
o
r
wild
f
i
r
e
p
r
ev
e
n
tio
n
,
r
is
k
ass
ess
m
en
t,
an
d
em
er
g
en
cy
r
esp
o
n
s
e,
th
er
e
b
y
s
u
p
p
o
r
tin
g
m
o
r
e
r
esil
ien
t e
n
v
ir
o
n
m
e
n
tal
m
o
n
it
o
r
in
g
s
y
s
tem
s
[
4
]
,
[
5
]
.
T
h
e
r
ap
id
ad
v
a
n
ce
m
en
t
o
f
d
e
ep
lear
n
in
g
h
as
f
u
r
th
er
ac
ce
l
er
ated
th
e
e
v
o
lu
tio
n
o
f
i
n
tellig
en
t
wild
f
ir
e
d
etec
tio
n
.
Dee
p
n
eu
r
al
n
etwo
r
k
s
,
p
ar
ticu
la
r
ly
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NNs),
h
av
e
d
em
o
n
s
tr
ated
r
em
ar
k
ab
le
ca
p
a
b
ilit
ies
in
e
x
tr
ac
tin
g
h
ier
ar
c
h
ical
s
p
atial
f
ea
tu
r
es
f
r
o
m
v
is
u
al
d
ata
with
o
u
t
r
eq
u
ir
in
g
h
an
d
cr
a
f
ted
f
ea
tu
r
e
en
g
in
ee
r
i
n
g
.
C
NN
-
b
ased
ar
c
h
itectu
r
es
h
av
e
b
ec
o
m
e
th
e
d
o
m
in
an
t
a
p
p
r
o
ac
h
f
o
r
wild
f
ir
e
im
ag
e
class
if
icatio
n
b
ec
a
u
s
e
o
f
th
eir
ab
ilit
y
to
r
ec
o
g
n
ize
c
o
m
p
lex
f
ir
e
p
atter
n
s
,
s
m
o
k
e
ch
ar
ac
ter
is
tics
,
an
d
co
n
tex
tu
al
s
ce
n
e
in
f
o
r
m
atio
n
ac
r
o
s
s
d
iv
er
s
e
en
v
ir
o
n
m
en
ta
l
co
n
d
itio
n
s
.
Acc
o
r
d
i
n
g
t
o
th
e
W
o
r
ld
E
co
n
o
m
i
c
Fo
r
u
m
,
Fire
C
NN
co
m
b
in
es
s
atellite
im
ag
er
y
with
wea
th
er
in
f
o
r
m
atio
n
to
id
en
tif
y
r
eg
io
n
s
with
elev
ated
wild
f
ir
e
r
is
k
a
n
d
h
as
th
e
p
o
te
n
tial
to
r
ed
u
ce
wild
f
ir
e
o
cc
u
r
r
en
ce
s
b
y
ap
p
r
o
x
im
ately
5
0
–
7
6
%,
h
ig
h
lig
h
ti
n
g
th
e
s
ig
n
if
ican
t c
o
n
tr
ib
u
tio
n
o
f
d
ee
p
lear
n
in
g
to
p
r
o
ac
tiv
e
wild
f
ir
e
m
an
ag
em
e
n
t [
1
]
.
I
n
ad
d
itio
n
to
C
NN
-
b
ased
im
ag
e
class
if
icatio
n
,
m
o
d
er
n
co
m
p
u
ter
v
is
io
n
tec
h
n
iq
u
es
h
av
e
ex
p
a
n
d
e
d
wild
f
ir
e
d
etec
tio
n
ca
p
a
b
ilit
ies
th
r
o
u
g
h
m
u
ltis
o
u
r
ce
d
ata
in
te
g
r
atio
n
.
R
em
o
te
s
en
s
in
g
p
latf
o
r
m
s
eq
u
ip
p
ed
with
o
p
tical,
in
f
r
ar
ed
,
an
d
th
er
m
al
s
en
s
o
r
s
co
n
tin
u
o
u
s
ly
m
o
n
ito
r
ex
ten
s
iv
e
f
o
r
ested
r
eg
i
o
n
s
,
wh
ile
L
iDAR
tech
n
o
lo
g
y
p
r
o
v
id
es
th
r
ee
-
d
i
m
en
s
io
n
al
s
tr
u
ctu
r
al
in
f
o
r
m
a
tio
n
th
at
im
p
r
o
v
es
v
eg
etatio
n
an
aly
s
is
an
d
f
ir
e
lo
ca
lizatio
n
.
Sim
ilar
ly
,
UAV
-
b
ased
im
ag
in
g
s
y
s
tem
s
en
ab
l
e
f
lex
ib
le,
h
ig
h
-
r
eso
lu
tio
n
o
b
s
er
v
atio
n
s
in
ar
ea
s
th
at
ar
e
d
if
f
icu
lt
to
ac
ce
s
s
u
s
in
g
co
n
v
e
n
tio
n
al
m
o
n
ito
r
i
n
g
m
eth
o
d
s
.
T
h
e
in
teg
r
atio
n
o
f
th
ese
s
en
s
in
g
tech
n
o
lo
g
ies
with
d
ee
p
lear
n
i
n
g
alg
o
r
ith
m
s
h
as
s
u
b
s
tan
tially
im
p
r
o
v
ed
d
etec
tio
n
ac
c
u
r
ac
y
,
r
o
b
u
s
tn
ess
,
an
d
o
p
er
atio
n
al
ef
f
icien
c
y
,
s
u
p
p
o
r
tin
g
in
tellig
en
t
en
v
ir
o
n
m
en
tal
s
u
r
v
eillan
ce
ac
r
o
s
s
m
u
ltip
le
s
p
atial
an
d
tem
p
o
r
al
s
ca
les [
3
]
,
[
4
]
.
R
ec
en
t
s
tu
d
ies
h
av
e
p
r
o
p
o
s
ed
n
u
m
er
o
u
s
AI
-
b
ased
wild
f
ir
e
d
etec
tio
n
ap
p
r
o
ac
h
es.
Ma
ch
in
e
le
ar
n
in
g
tech
n
iq
u
es
h
a
v
e
b
ee
n
s
u
cc
es
s
f
u
lly
ap
p
lied
to
wild
f
ir
e
p
r
ed
ictio
n
,
d
etec
tio
n
,
an
d
em
e
r
g
en
cy
r
esp
o
n
s
e
b
y
ex
p
lo
itin
g
h
eter
o
g
e
n
eo
u
s
en
v
ir
o
n
m
en
tal
d
atasets
[
5
]
.
W
SN
s
in
teg
r
ated
with
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
h
av
e
d
em
o
n
s
tr
ated
im
p
r
o
v
ed
ea
r
ly
f
ir
e
d
etec
t
io
n
th
r
o
u
g
h
d
is
tr
ib
u
ted
s
en
s
in
g
an
d
in
tellig
e
n
t
d
ata
p
r
o
ce
s
s
in
g
,
en
ab
lin
g
r
a
p
id
id
en
tific
atio
n
o
f
ab
n
o
r
m
al
en
v
ir
o
n
m
e
n
tal
co
n
d
itio
n
s
[
6
]
.
Fu
r
th
er
m
o
r
e,
d
ee
p
lear
n
in
g
m
o
d
els
u
tili
zin
g
im
ag
er
y
f
r
o
m
th
e
H
im
awa
r
i
-
8
s
atellite
h
av
e
ac
h
i
ev
ed
r
em
a
r
k
ab
le
p
er
f
o
r
m
a
n
ce
b
y
s
im
u
ltan
e
o
u
s
ly
ex
p
lo
itin
g
s
p
atial
a
n
d
tem
p
o
r
al
in
f
o
r
m
atio
n
.
T
h
e
p
r
o
p
o
s
ed
C
NN
-
b
ased
f
r
am
ewo
r
k
s
u
b
s
tan
tially
r
ed
u
ce
d
wild
f
ir
e
r
ec
o
g
n
itio
n
tim
e
t
o
ap
p
r
o
x
im
ately
1
2
m
in
u
tes
w
h
ile
o
u
tp
e
r
f
o
r
m
in
g
co
n
v
en
tio
n
al
R
an
d
o
m
Fo
r
est
class
if
ier
s
,
ac
h
iev
in
g
an
o
v
e
r
a
ll d
et
ec
tio
n
ac
cu
r
ac
y
o
f
0
.
9
8
a
n
d
an
F1
-
s
co
r
e
o
f
0
.
7
4
[
7
]
.
B
ey
o
n
d
im
ag
e
class
if
icatio
n
,
wild
f
ir
e
r
is
k
ass
es
s
m
en
t
h
as
also
b
en
ef
ited
f
r
o
m
ad
v
an
ce
d
s
p
atial
an
aly
s
is
tech
n
iq
u
es.
Or
d
er
e
d
weig
h
ted
av
er
a
g
in
g
(
OW
A)
in
teg
r
ated
with
m
u
lti
-
cr
iter
ia
ev
alu
atio
n
(
MCE)
h
as
be
en
em
p
lo
y
ed
to
g
e
n
er
ate
m
o
r
e
r
eliab
le
wild
f
ir
e
s
u
s
ce
p
tib
ilit
y
m
ap
s
b
y
co
m
b
in
in
g
m
u
lt
ip
le
en
v
ir
o
n
m
en
tal
r
is
k
f
ac
to
r
s
in
to
a
u
n
if
ied
s
p
atial
d
ec
is
io
n
-
m
ak
in
g
f
r
am
e
wo
r
k
[
8
]
.
Me
an
wh
ile,
r
ec
en
t
p
r
o
g
r
ess
in
d
ee
p
lear
n
in
g
ar
c
h
itectu
r
e
d
esig
n
h
as
in
tr
o
d
u
ce
d
E
f
f
icie
n
tNet
m
o
d
els
f
o
r
au
to
m
atic
wild
f
ir
e
d
et
ec
tio
n
u
s
in
g
lar
g
e
-
s
ca
le
d
atasets
.
C
o
m
p
ar
ed
with
co
n
v
e
n
tio
n
al
ar
c
h
itectu
r
es
s
u
ch
as
I
n
ce
p
tio
n
V3
an
d
M
o
b
ileNetV2
,
E
f
f
icien
tNet
d
em
o
n
s
tr
ated
s
u
p
er
io
r
d
etec
tio
n
ca
p
ab
ilit
y
,
a
ch
iev
in
g
a
tr
u
e
d
etec
tio
n
r
at
e
o
f
8
9
.
2
%
wh
ile
m
ain
tain
in
g
a
r
em
ar
k
ab
ly
lo
w
f
alse
p
o
s
itiv
e
r
ate
o
f
o
n
ly
0
.
3
0
6
%,
illu
s
tr
atin
g
th
e
ef
f
ec
tiv
en
ess
o
f
o
p
tim
ized
d
ee
p
n
eu
r
al
n
etwo
r
k
s
f
o
r
lar
g
e
-
s
ca
le
wild
f
ir
e
m
o
n
ito
r
in
g
ap
p
licatio
n
s
[
9
]
.
Fu
r
th
er
m
o
r
e,
th
e
in
teg
r
atio
n
o
f
I
o
T
tech
n
o
lo
g
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with
AI
h
as
en
ab
led
th
e
d
ev
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p
m
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t
o
f
d
is
tr
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ted
wild
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ir
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m
o
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ito
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y
s
tem
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p
ab
le
o
f
co
n
tin
u
o
u
s
en
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m
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tal
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en
s
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g
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in
tellig
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ata
tr
a
n
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m
is
s
io
n
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an
d
r
ea
l
-
tim
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ec
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o
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s
u
p
p
o
r
t,
th
er
eb
y
en
h
an
cin
g
th
e
o
v
er
all
r
eliab
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an
d
r
esp
o
n
s
iv
en
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o
f
wild
f
i
r
e
m
an
ag
em
e
n
t in
f
r
astru
ct
u
r
es [
1
0
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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J
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T
h
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ellig
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tech
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iv
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Hig
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s
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s
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g
ad
v
an
ce
d
c
o
m
p
u
ter
v
is
io
n
alg
o
r
ith
m
s
.
T
h
e
in
teg
r
atio
n
o
f
A
I
with
th
ese
s
en
s
in
g
p
latf
o
r
m
s
en
ab
les
co
n
tin
u
o
u
s
en
v
ir
o
n
m
en
tal
m
o
n
ito
r
in
g
,
au
t
o
m
atic
f
ir
e
lo
ca
lizatio
n
,
a
n
d
r
a
p
id
d
ec
is
io
n
s
u
p
p
o
r
t,
s
ig
n
if
ica
n
tly
im
p
r
o
v
in
g
t
h
e
ef
f
ec
tiv
en
ess
o
f
wild
f
ir
e
s
u
r
v
eillan
ce
.
R
ec
en
t
s
tu
d
ies
h
av
e
p
r
o
p
o
s
ed
AI
-
em
b
ed
d
ed
d
ete
ctio
n
s
y
s
tem
s
th
at
co
m
b
in
e
h
ig
h
-
r
eso
lu
tio
n
s
atellite
im
ag
er
y
with
in
tellig
en
t
im
ag
e
an
aly
s
is
to
im
p
r
o
v
e
wild
f
ir
e
m
o
n
ito
r
in
g
ac
cu
r
ac
y
an
d
m
in
im
ize
d
etec
tio
n
d
elay
s
[
1
1
]
,
[
1
2
]
.
T
h
ese
in
tellig
en
t
m
o
n
ito
r
i
n
g
s
y
s
tem
s
p
r
o
v
id
e
tim
ely
s
itu
atio
n
al
awa
r
en
ess
f
o
r
e
m
er
g
en
cy
r
esp
o
n
d
er
s
an
d
s
u
p
p
o
r
t
p
r
o
ac
ti
v
e
d
is
aster
m
an
ag
em
en
t
th
r
o
u
g
h
au
to
m
ated
an
al
y
s
is
o
f
lar
g
e
-
s
c
ale
en
v
ir
o
n
m
en
tal
d
ata.
B
ey
o
n
d
wild
f
i
r
e
d
etec
tio
n
,
p
r
ed
ictiv
e
m
o
d
elin
g
h
as
b
ec
o
m
e
an
im
p
o
r
tan
t
r
esear
ch
d
i
r
ec
tio
n
f
o
r
u
n
d
er
s
tan
d
i
n
g
wild
f
ir
e
b
eh
av
i
o
r
an
d
s
u
p
p
o
r
tin
g
em
er
g
en
cy
p
lan
n
in
g
.
Gen
etic
alg
o
r
ith
m
s
(
GAs),
f
o
r
ex
am
p
le,
h
av
e
b
ee
n
em
p
l
o
y
ed
to
o
p
tim
ize
wild
f
ir
e
s
p
r
ea
d
p
r
ed
icti
o
n
m
o
d
els
b
y
a
u
to
m
atica
lly
ca
lib
r
atin
g
m
o
d
el
p
ar
am
eter
s
b
ased
o
n
h
is
to
r
ical
f
ir
e
o
b
s
er
v
atio
n
s
an
d
e
n
v
ir
o
n
m
en
tal
v
ar
iab
les.
Su
ch
o
p
tim
izatio
n
tech
n
iq
u
e
s
im
p
r
o
v
e
th
e
ac
c
u
r
ac
y
an
d
r
e
liab
ilit
y
o
f
wild
f
ir
e
p
r
o
p
ag
ati
o
n
m
o
d
els,
en
a
b
lin
g
m
o
r
e
ef
f
ec
tiv
e
ev
ac
u
atio
n
p
lan
n
in
g
an
d
r
eso
u
r
ce
allo
ca
tio
n
d
u
r
i
n
g
em
e
r
g
en
c
y
r
esp
o
n
s
e
o
p
er
atio
n
s
[
1
3
]
.
Me
an
wh
ile,
r
ec
en
t
d
ev
elo
p
m
e
n
ts
in
d
ee
p
lea
r
n
in
g
h
av
e
e
x
ten
d
e
d
b
ey
o
n
d
im
ag
e
class
if
icatio
n
to
in
clu
d
e
in
te
g
r
ated
class
if
icatio
n
an
d
s
eg
m
en
tatio
n
f
r
am
ew
o
r
k
s
.
Hy
b
r
id
C
NN
ar
ch
itectu
r
es
ca
p
ab
le
o
f
s
i
m
u
ltan
eo
u
s
ly
id
en
tify
in
g
wild
f
ir
e
o
cc
u
r
r
e
n
ce
s
an
d
d
elin
ea
tin
g
af
f
ec
ted
r
eg
io
n
s
h
av
e
d
em
o
n
s
tr
ated
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
co
m
p
ar
ed
with
co
n
v
en
tio
n
al
m
o
d
els.
E
x
p
er
i
m
en
tal
r
esu
lts
s
h
o
w
th
at
C
N
N
-
b
ased
class
if
icatio
n
ac
h
iev
ed
an
ac
cu
r
ac
y
o
f
8
8
.
1
9
%,
wh
ile
UNe
t
o
u
tp
e
r
f
o
r
m
ed
Seg
Net
i
n
wild
f
ir
e
s
eg
m
en
tatio
n
with
a
Dice
co
e
f
f
icien
t
o
f
0
.
6
8
6
9
,
illu
s
tr
atin
g
th
e
ef
f
ec
tiv
en
ess
o
f
d
ee
p
n
eu
r
al
n
etwo
r
k
s
f
o
r
co
m
p
r
eh
en
s
iv
e
wild
f
i
r
e
s
ce
n
e
u
n
d
er
s
tan
d
in
g
[
1
4
]
.
R
ec
en
t
r
esear
ch
h
as
also
h
ig
h
lig
h
ted
t
h
e
im
p
o
r
tan
ce
o
f
i
n
co
r
p
o
r
atin
g
co
n
te
x
tu
al
e
n
v
i
r
o
n
m
en
tal
in
f
o
r
m
atio
n
in
to
wild
f
ir
e
p
r
e
d
ictio
n
.
C
lim
atic
v
ar
iab
les
s
u
ch
as
tem
p
er
atu
r
e,
h
u
m
id
it
y
,
win
d
s
p
ee
d
,
a
n
d
p
r
ec
ip
itatio
n
s
tr
o
n
g
ly
in
f
lu
e
n
ce
wild
f
ir
e
i
g
n
itio
n
an
d
p
r
o
p
ag
atio
n
.
I
n
v
esti
g
atio
n
s
co
n
d
u
cted
in
Pu
er
to
R
ico
d
em
o
n
s
tr
ated
t
h
at
wild
f
ir
e
o
c
cu
r
r
en
ce
is
clo
s
ely
ass
o
ciate
d
with
s
ea
s
o
n
al
wea
th
er
p
atter
n
s
,
wh
er
e
r
elativ
e
clim
atic
co
n
d
itio
n
s
i
n
cr
ea
s
e
t
h
e
lik
elih
o
o
d
o
f
f
ir
e
ig
n
itio
n
an
d
a
b
s
o
lu
te
en
v
ir
o
n
m
en
tal
c
o
n
d
itio
n
s
d
eter
m
in
e
wild
f
ir
e
s
ev
er
ity
an
d
s
p
atial
ex
ten
t
[
1
5
]
.
Fu
r
th
er
m
o
r
e,
c
o
n
tex
tu
al
in
f
o
r
m
atio
n
ex
t
r
ac
ted
f
r
o
m
n
eig
h
b
o
r
in
g
p
ix
els
o
r
s
u
r
r
o
u
n
d
in
g
g
e
o
g
r
ap
h
ical
r
eg
io
n
s
h
as
b
ee
n
s
h
o
wn
to
im
p
r
o
v
e
class
if
icatio
n
ac
cu
r
ac
y
.
C
o
m
p
ar
ativ
e
s
tu
d
ies
in
v
o
lv
in
g
m
u
ltil
ay
er
p
er
ce
p
tr
o
n
s
(
ML
Ps
)
an
d
C
NNs
r
ev
ea
led
th
at
c
o
n
tex
tu
al
C
NN
-
b
ased
m
o
d
els
co
n
s
is
t
en
tly
o
u
tp
er
f
o
r
m
p
i
x
el
-
b
ased
ap
p
r
o
ac
h
es
b
y
ef
f
ec
tiv
e
ly
ex
p
l
o
itin
g
s
p
atial
r
elatio
n
s
h
ip
s
with
in
r
e
m
o
te
s
en
s
in
g
im
ag
er
y
,
th
e
r
eb
y
e
n
h
an
cin
g
wild
f
ir
e
d
etec
tio
n
r
eliab
ilit
y
[
1
6
]
.
Similar
ly
,
U
AV
-
b
ased
wild
f
ir
e
m
o
n
ito
r
in
g
h
as
b
en
ef
ited
f
r
o
m
s
tate
-
of
-
th
e
-
a
r
t
o
b
jec
t
d
ete
ctio
n
alg
o
r
ith
m
s
s
u
ch
as
YO
L
Ov
5
an
d
YOL
Ov
8
,
wh
ich
p
r
o
v
id
e
ac
c
u
r
ate
r
ea
l
-
t
im
e
f
ir
e
d
etec
tio
n
wh
ile
m
ai
n
tain
in
g
h
i
g
h
c
o
m
p
u
tatio
n
al
ef
f
icien
cy
f
o
r
ae
r
ial
s
u
r
v
eillan
ce
ap
p
licatio
n
s
[
1
7
]
.
Desp
ite
th
ese
r
em
ar
k
ab
le
ad
v
an
ce
s
,
m
o
s
t
ex
is
tin
g
d
ee
p
lear
n
in
g
-
b
ase
d
wild
f
ir
e
d
etec
tio
n
s
y
s
tem
s
r
em
ain
f
u
n
d
a
m
en
tally
b
lack
-
b
o
x
m
o
d
els.
Alth
o
u
g
h
t
h
ey
o
f
ten
ac
h
iev
e
h
ig
h
class
if
icatio
n
ac
cu
r
ac
y
,
th
ey
p
r
o
v
id
e
lim
ited
in
s
ig
h
t
in
to
t
h
e
r
ea
s
o
n
in
g
b
eh
i
n
d
th
eir
p
r
ed
ictio
n
s
.
I
n
s
af
ety
-
c
r
itical
ap
p
licatio
n
s
s
u
ch
as
wild
f
ir
e
m
o
n
ito
r
in
g
,
em
er
g
en
cy
r
esp
o
n
s
e,
an
d
en
v
ir
o
n
m
e
n
tal
r
is
k
m
an
ag
em
en
t,
p
r
ed
ictio
n
ac
cu
r
ac
y
al
o
n
e
is
in
s
u
f
f
icien
t.
Dec
is
io
n
-
m
a
k
er
s
,
em
er
g
e
n
cy
r
esp
o
n
d
er
s
,
e
n
v
ir
o
n
m
en
tal
a
g
en
cies,
a
n
d
p
o
lic
y
m
ak
er
s
m
u
s
t
also
u
n
d
er
s
tan
d
wh
y
an
AI
m
o
d
el
id
en
tifie
s
a
p
ar
ticu
lar
r
eg
io
n
a
s
co
n
tain
in
g
wild
f
ir
e.
T
h
e
ab
s
en
ce
o
f
tr
an
s
p
a
r
en
t
d
ec
is
io
n
-
m
ak
in
g
r
ed
u
ce
s
u
s
er
co
n
f
id
e
n
ce
,
co
m
p
licates
m
o
d
el
v
alid
atio
n
,
an
d
lim
its
th
e
p
r
ac
tical
d
ep
lo
y
m
en
t
o
f
AI
s
y
s
tem
s
in
o
p
e
r
atio
n
al
d
is
aster
m
an
ag
em
e
n
t.
C
o
n
s
eq
u
en
tly
,
im
p
r
o
v
in
g
th
e
i
n
ter
p
r
etab
ilit
y
a
n
d
tr
an
s
p
ar
en
cy
o
f
d
ee
p
lear
n
i
n
g
m
o
d
els
h
as
b
ec
o
m
e
an
im
p
o
r
tan
t
r
esear
ch
ch
allen
g
e
in
in
tellig
en
t
en
v
ir
o
n
m
en
tal
m
o
n
ito
r
in
g
.
E
x
p
lain
ab
le
ar
tific
ial
in
tellig
en
ce
(
XAI
)
h
as
r
ec
en
tly
e
m
er
g
ed
as
an
ef
f
ec
tiv
e
a
p
p
r
o
ac
h
f
o
r
ad
d
r
ess
in
g
th
is
ch
allen
g
e
b
y
m
ak
in
g
AI
m
o
d
els
m
o
r
e
tr
a
n
s
p
ar
en
t,
in
ter
p
r
etab
le,
an
d
t
r
u
s
two
r
th
y
.
R
ath
er
t
h
an
p
r
o
d
u
cin
g
p
r
ed
ictio
n
s
with
o
u
t
ju
s
tific
atio
n
,
XAI
tech
n
iq
u
es
g
en
er
ate
v
is
u
al
o
r
f
ea
tu
r
e
-
b
ased
ex
p
lan
atio
n
s
th
at
r
ev
ea
l
th
e
e
v
id
en
ce
s
u
p
p
o
r
tin
g
m
o
d
el
d
ec
is
io
n
s
.
I
n
co
m
p
u
ter
v
is
io
n
ap
p
licati
o
n
s
,
ex
p
lain
a
b
ilit
y
m
eth
o
d
s
s
u
ch
as
s
alien
cy
v
is
u
aliza
tio
n
,
clas
s
ac
tiv
atio
n
m
ap
p
in
g
,
a
n
d
atten
tio
n
-
b
ased
in
ter
p
r
etatio
n
en
ab
l
e
u
s
er
s
to
id
en
tify
th
e
im
ag
e
r
e
g
io
n
s
th
at
co
n
tr
i
b
u
te
m
o
s
t
s
ig
n
if
ican
tly
t
o
th
e
c
lass
if
icatio
n
p
r
o
ce
s
s
.
T
h
ese
ca
p
ab
ilit
ies
n
o
t
o
n
l
y
f
ac
ilit
ate
m
o
d
el
v
er
if
icatio
n
an
d
d
eb
u
g
g
in
g
b
u
t
also
s
tr
en
g
th
en
u
s
er
tr
u
s
t,
im
p
r
o
v
e
a
cc
o
u
n
tab
ilit
y
,
an
d
s
u
p
p
o
r
t r
esp
o
n
s
ib
le
d
ep
l
o
y
m
e
n
t o
f
AI
tec
h
n
o
lo
g
ies in
s
af
ety
-
cr
itical
d
o
m
ain
s
.
Mo
tiv
ated
b
y
th
ese
ch
allen
g
e
s
,
th
is
r
esear
ch
p
r
o
p
o
s
es
Fire
DetXp
lain
er
(F
DX)
,
an
XAI
f
r
am
ewo
r
k
f
o
r
tr
a
n
s
p
ar
en
t
wild
f
ir
e
d
etec
t
io
n
.
Un
lik
e
c
o
n
v
e
n
tio
n
al
d
ee
p
lear
n
in
g
m
o
d
els
th
at
p
r
o
v
id
e
o
n
ly
class
if
icatio
n
o
u
tp
u
ts
,
FDX
co
m
b
i
n
es
h
ig
h
-
p
er
f
o
r
m
an
ce
c
o
m
p
u
ter
v
is
io
n
with
ex
p
lain
ab
ilit
y
m
ec
h
an
is
m
s
to
p
r
o
d
u
ce
b
o
t
h
ac
cu
r
ate
wild
f
ir
e
d
ete
ctio
n
a
n
d
in
t
u
itiv
e
v
is
u
al
e
x
p
lan
atio
n
s
o
f
t
h
e
m
o
d
el'
s
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
.
T
h
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
en
a
b
les
u
s
er
s
to
u
n
d
er
s
tan
d
w
h
ich
im
ag
e
f
ea
tu
r
es
an
d
s
p
atial
r
eg
io
n
s
in
f
lu
en
ce
wild
f
ir
e
p
r
ed
ictio
n
s
,
th
er
eb
y
im
p
r
o
v
in
g
m
o
d
el
in
ter
p
r
eta
b
ilit
y
wh
ile
m
ain
t
ain
in
g
r
eliab
le
d
etec
tio
n
p
er
f
o
r
m
an
ce
.
B
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
F
ir
eDe
tXp
la
in
er:
a
n
ex
p
la
in
a
b
le
a
r
tifi
cia
l in
tellig
en
ce
fr
a
mewo
r
k
…
(
Ja
n
jh
ya
m
V
en
k
a
ta
N
a
g
a
R
a
mesh
)
579
in
teg
r
atin
g
e
x
p
lain
ab
ilit
y
in
to
d
ee
p
lear
n
in
g
-
b
ased
wild
f
ir
e
d
etec
tio
n
,
FDX
ad
d
r
ess
es
th
e
g
r
o
win
g
d
e
m
an
d
f
o
r
tr
u
s
two
r
th
y
AI
s
y
s
tem
s
ca
p
ab
l
e
o
f
s
u
p
p
o
r
tin
g
r
ea
l
-
wo
r
ld
en
v
ir
o
n
m
en
tal
m
o
n
ito
r
in
g
an
d
em
er
g
en
cy
r
esp
o
n
s
e.
T
h
e
p
r
im
ar
y
o
b
jectiv
es
o
f
t
h
is
s
tu
d
y
ar
e
th
r
ee
f
o
ld
.
First,
it
d
ev
elo
p
s
a
r
o
b
u
s
t
d
ee
p
lear
n
in
g
f
r
am
ewo
r
k
f
o
r
au
to
m
ated
wild
f
ir
e
d
etec
tio
n
u
s
in
g
ad
v
an
ce
d
c
o
m
p
u
ter
v
is
io
n
tech
n
iq
u
es.
Seco
n
d
,
i
t
in
co
r
p
o
r
ates
ex
p
lain
a
b
le
AI
m
ec
h
an
is
m
s
th
at
p
r
o
v
id
e
tr
a
n
s
p
ar
en
t
a
n
d
in
ter
p
r
eta
b
le
v
i
s
u
al
ex
p
lan
atio
n
s
,
en
ab
lin
g
u
s
er
s
to
v
alid
ate
an
d
u
n
d
er
s
tan
d
th
e
m
o
d
el'
s
p
r
e
d
ictio
n
s
.
T
h
ir
d
,
it
co
m
p
r
eh
en
s
iv
ely
ev
alu
ates
th
e
p
r
o
p
o
s
ed
FDX
f
r
am
ewo
r
k
u
s
in
g
wild
f
ir
e
im
ag
e
d
atasets
to
ass
es
s
b
o
th
p
r
e
d
ictiv
e
p
er
f
o
r
m
an
ce
a
n
d
in
ter
p
r
etab
ilit
y
.
T
h
e
co
n
tr
i
b
u
t
io
n
s
o
f
th
is
r
esear
ch
e
x
ten
d
b
ey
o
n
d
im
p
r
o
v
in
g
wild
f
ir
e
d
et
ec
tio
n
ac
cu
r
ac
y
b
y
d
em
o
n
s
tr
atin
g
h
o
w
XAI
ca
n
en
h
an
ce
tr
an
s
p
ar
e
n
cy
,
r
el
iab
ilit
y
,
an
d
u
s
er
tr
u
s
t
in
i
n
tellig
en
t
wild
f
ir
e
m
o
n
ito
r
in
g
s
y
s
tem
s
.
C
o
n
s
eq
u
en
tly
,
FDX
r
ep
r
esen
ts
a
s
ig
n
if
ican
t
s
t
ep
to
war
d
th
e
d
ev
elo
p
m
en
t
o
f
tr
u
s
two
r
th
y
AI
-
en
ab
led
en
v
ir
o
n
m
en
tal
s
u
r
v
eillan
ce
s
y
s
tem
s
th
at
ef
f
ec
tiv
ely
s
u
p
p
o
r
t
d
is
aster
m
an
a
g
em
en
t,
ea
r
ly
war
n
in
g
,
an
d
s
u
s
tain
ab
le
ec
o
s
y
s
tem
p
r
o
tectio
n
.
2.
M
E
T
H
O
D
FDX
u
s
es
a
r
ev
o
lu
tio
n
ar
y
c
o
m
b
in
atio
n
o
f
th
e
lear
n
i
n
g
with
o
u
t
f
o
r
g
ettin
g
(
L
wF)
ar
ch
itectu
r
e,
tr
an
s
f
er
lear
n
in
g
,
an
d
f
in
e
-
tu
n
in
g
to
o
v
er
c
o
m
e
ca
tast
r
o
p
h
i
c
f
o
r
g
ettin
g
an
d
im
p
r
o
v
e
th
e
m
o
d
el'
s
ab
ilit
y
to
r
etain
an
d
ac
q
u
ir
e
n
ew
in
f
o
r
m
atio
n
.
FDX
ac
c
u
r
ately
cl
ass
if
ies
wild
f
ir
e
im
ag
er
y
u
s
in
g
th
e
p
r
etr
ai
n
ed
Mo
b
ileNetV3
m
o
d
el,
k
n
o
w
n
f
o
r
im
a
g
e
class
if
icatio
n
.
T
h
is
ad
v
an
ce
s
th
e
u
s
e
o
f
p
r
e
-
t
r
ain
ed
m
o
d
els
f
o
r
en
v
ir
o
n
m
en
tal
ch
allen
g
es.
C
o
n
v
o
lu
tio
n
al
b
lo
ck
s
a
n
d
ex
te
n
s
iv
e
p
ictu
r
e
p
r
ep
r
o
ce
s
s
in
g
ar
e
u
s
ed
to
im
p
r
o
v
e
t
h
e
m
o
d
el'
s
ca
p
ac
ity
to
id
en
tif
y
c
o
m
p
lex
wild
f
i
r
e
im
ag
er
y
p
att
er
n
s
.
As
a
p
io
n
ee
r
in
g
wild
f
ir
e
d
etec
tio
n
m
o
d
el,
FDX
u
s
es
E
x
p
lain
ab
le
AI
tech
n
o
lo
g
ies
lik
e
Gr
ad
-
C
AM
an
d
lo
ca
l
in
ter
p
r
etab
le
m
o
d
el
-
a
g
n
o
s
tic
ex
p
lan
atio
n
s
(
L
I
ME
)
to
p
r
o
v
id
e
clea
r
an
d
ac
ce
s
s
ib
le
ex
p
lan
atio
n
s
f
o
r
it
s
f
o
r
ec
asts
,
b
o
o
s
tin
g
u
s
er
co
n
f
id
en
ce
an
d
m
o
d
el
in
ter
p
r
etab
ilit
y
.
I
m
ag
es
ar
e
u
s
ed
b
y
FDX
to
m
o
d
el
wild
f
ir
e
d
etec
tio
n
.
T
h
e
2
,
9
7
4
f
ir
e
class
if
icatio
n
p
h
o
to
s
ar
e
d
iv
id
ed
in
t
o
two
g
r
o
u
p
s
.
T
h
e
f
ir
s
t
s
et
s
h
o
ws
ac
tiv
e
f
o
r
est
f
ir
es,
wh
er
ea
s
th
e
s
ec
o
n
d
s
h
o
ws
f
ir
e
-
f
r
ee
f
o
r
ests
[
1
8
]
.
Fire
f
o
r
est
ca
teg
o
r
izatio
n
u
s
es
8
0
%
tr
ain
in
g
a
n
d
2
0
%
v
alid
atio
n
d
ata.
T
h
e
c
o
llectio
n
h
as
1
2
7
5
n
o
n
-
f
ir
e
an
d
1
6
7
2
f
ir
e
p
h
o
to
s
.
T
h
e
FDX
m
o
d
el,
a
u
n
i
q
u
e
wild
f
ir
e
d
etec
tio
n
m
eth
o
d
,
ad
v
a
n
ce
s
m
ac
h
in
e
lear
n
in
g
f
o
r
en
v
ir
o
n
m
en
tal
p
r
o
tectio
n
.
FDX
is
p
r
ec
is
ely
d
esig
n
ed
to
b
len
d
ad
v
a
n
ce
d
n
e
u
r
al
n
etwo
r
k
f
ea
tu
r
es
with
th
e
p
r
ess
in
g
n
ee
d
f
o
r
ac
c
u
r
ate
wild
f
ir
e
id
en
tific
atio
n
an
d
ca
teg
o
r
izatio
n
.
T
h
e
FDX
m
o
d
el
u
s
es
Mo
b
ileNetV3
f
o
r
tr
an
s
f
er
lear
n
in
g
an
d
p
r
io
r
itiz
es
p
er
f
o
r
m
an
ce
an
d
co
m
p
u
tat
io
n
al
ef
f
icien
cy
f
o
r
r
ea
l
-
tim
e
ap
p
licatio
n
s
.
T
h
is
s
ec
tio
n
d
is
cu
s
s
es
FD
X
'
s
ar
ch
itectu
r
e
an
d
h
o
w
it
s
o
lv
es
th
e
co
m
p
lex
wild
f
i
r
e
d
etec
tio
n
p
r
o
b
lem
.
Fig
u
r
e
1
s
h
o
ws th
e
FDX
ar
ch
itectu
r
e
an
d
o
p
e
r
atio
n
s
in
d
etail.
T
h
e
p
r
e
-
tr
ain
e
d
Mo
b
ileNetV3
m
o
d
el
is
ad
ju
s
ted
f
o
r
f
ea
tu
r
e
s
an
d
f
in
e
-
t
u
n
in
g
u
tili
zin
g
wild
f
ir
e
d
ata.
T
h
e
m
o
d
el
is
tr
ain
ed
an
d
ev
alu
ated
u
s
in
g
E
x
p
lain
ab
le
AI
ap
p
r
o
ac
h
es
as
Gr
ad
-
C
AM
a
n
d
L
I
ME
to
g
i
v
e
in
s
ig
h
tf
u
l
v
is
u
aliza
tio
n
s
an
d
im
p
r
o
v
e
in
ter
p
r
etab
ilit
y
,
r
esu
lt
in
g
in
p
e
r
f
o
r
m
an
ce
in
d
icato
r
s
an
d
tr
ain
e
d
m
o
d
els.
Fin
e
-
tu
n
in
g
tar
g
e
ts
Mo
b
ileNetV3
m
o
d
el
to
p
lev
els
in
th
e
F
DX
ar
ch
itectu
r
e.
T
h
is
s
tep
is
n
ec
ess
ar
y
to
ad
ju
s
t
th
e
m
o
d
el
to
id
en
tif
y
an
d
ca
teg
o
r
ize
n
ew
d
ata,
esp
ec
ially
wild
f
ir
e
p
h
o
to
g
r
ap
h
s
.
T
h
is
f
in
e
-
tu
n
in
g
aim
s
to
b
alan
ce
wid
e
an
d
tar
g
eted
l
ea
r
n
in
g
.
T
h
is
m
eth
o
d
em
p
h
a
s
izes
o
n
th
e
m
o
d
el'
s
h
ig
h
est
lay
er
s
,
wh
ich
ca
n
ac
co
m
m
o
d
ate
n
ew
d
ata
ty
p
es
well.
E
n
h
a
n
cin
g
th
ese
lay
e
r
s
h
elp
s
FDX
g
r
asp
wild
f
ir
e
im
ag
e
n
u
an
ce
s
.
T
h
is
s
tr
ateg
y
en
s
u
r
es
th
at
th
e
m
o
d
el
ca
n
h
a
n
d
le
v
ar
io
u
s
im
a
g
e
k
in
d
s
an
d
im
p
r
o
v
es
its
wild
f
ir
e
d
etec
tio
n
ca
p
ac
ity
.
T
h
e
m
ai
n
g
o
al
is
to
m
ain
tai
n
th
e
m
o
d
el'
s
p
ictu
r
e
r
ec
o
g
n
itio
n
ab
ilit
y
wh
ile
b
o
o
s
tin
g
its
wild
f
ir
e
f
ea
tu
r
e
r
ec
o
g
n
itio
n
.
Hy
p
er
-
p
ar
am
ete
r
o
p
tim
izatio
n
is
cr
u
cial
to
m
o
d
el
ef
f
ec
tiv
en
ess
.
T
h
e
m
o
d
el'
s
lear
n
in
g
r
ate,
b
atch
s
ize,
an
d
tr
ai
n
in
g
ep
o
c
h
s
ar
e
ac
cu
r
ately
co
n
tr
o
l
led
[
1
9
]
,
[
2
0
]
.
T
h
ese
p
ar
a
m
eter
s
s
tr
o
n
g
ly
im
p
ac
t
m
o
d
el
lear
n
in
g
a
n
d
p
r
ec
is
io
n
.
T
h
is
o
p
tim
izatio
n
s
ee
k
s
th
e
o
p
tim
u
m
eq
u
ilib
r
iu
m
th
at
a
v
o
id
s
o
v
e
r
f
itti
n
g
th
e
m
o
d
el
t
o
th
e
tr
ain
in
g
d
ata
o
r
u
n
d
e
r
ca
p
tu
r
in
g
k
e
y
d
ata
p
atter
n
s
.
T
h
e
L
wF
ap
p
r
o
ac
h
p
lay
s
a
v
i
tal
r
o
le
in
m
ai
n
tain
in
g
th
e
m
o
d
el'
s
o
r
ig
in
al
ab
ilit
ies
wh
ile
it
ac
q
u
ir
es
n
ew
s
k
ills
.
I
n
th
e
co
n
tex
t
o
f
F
DX,
th
is
in
v
o
lv
es
p
r
eser
v
in
g
th
e
m
o
d
el'
s
o
v
er
all
im
ag
e
p
r
o
c
ess
in
g
ca
p
ab
ilit
ies
wh
ile
tailo
r
in
g
it
to
m
ee
t
th
e
u
n
iq
u
e
d
em
a
n
d
s
o
f
wild
f
ir
e
d
etec
tio
n
.
T
h
is
ap
p
r
o
ac
h
is
cr
u
cial
f
o
r
s
af
eg
u
a
r
d
in
g
th
e
f
u
n
d
am
en
tal
s
tr
en
g
th
s
o
f
th
e
m
o
d
el.
T
h
is
ap
p
r
o
ac
h
en
ab
les
FDX
to
m
ain
tain
its
f
o
u
n
d
atio
n
al
lear
n
ed
b
eh
av
io
r
s
,
g
u
ar
an
teein
g
th
at
a
r
o
b
u
s
t
b
ase
o
f
k
n
o
wled
g
e
r
e
m
ain
s
av
ailab
le,
ev
en
as
th
e
m
o
d
el
ad
ap
ts
to
th
e
in
tr
icate
ch
allen
g
e
o
f
d
etec
tin
g
wild
f
ir
es.
Data
p
r
e
p
ar
atio
n
is
cr
u
cial
f
o
r
e
n
s
u
r
in
g
ef
f
ec
tiv
e
m
o
d
el
tr
ai
n
in
g
.
T
h
is
en
co
m
p
ass
es
th
e
u
s
e
o
f
d
iv
er
s
e
im
ag
e
m
an
ip
u
latio
n
m
eth
o
d
s
,
in
clu
d
in
g
r
o
tatio
n
s
,
f
lip
s
,
an
d
co
lo
r
ad
ju
s
tm
en
ts
.
B
y
ar
tific
ially
ex
p
an
d
i
n
g
th
e
d
ataset,
FDX
ca
n
g
ain
d
ee
p
e
r
in
s
ig
h
ts
an
d
im
p
r
o
v
e
its
ab
ilit
y
to
ap
p
ly
k
n
o
wled
g
e
f
r
o
m
th
e
tr
ain
in
g
d
ata
t
o
ac
tu
al
wild
f
ir
e
s
itu
atio
n
s
,
th
er
eb
y
b
o
o
s
tin
g
its
d
etec
tio
n
ca
p
ab
ilit
ies.
T
h
ese
p
r
o
ce
s
s
es
p
lay
a
cr
u
cial
r
o
le
in
e
n
s
u
r
i
n
g
u
n
i
f
o
r
m
ity
i
n
im
ag
e
q
u
ali
ty
an
d
f
o
r
m
at.
B
y
st
an
d
ar
d
izin
g
th
e
in
p
u
t
d
ata,
FDX
ca
n
en
h
an
ce
its
ab
ilit
y
to
lear
n
an
d
id
en
tif
y
p
atter
n
s
,
wh
ich
is
ess
en
tial
f
o
r
p
r
ec
is
e
wild
f
ir
e
d
etec
tio
n
.
T
h
i
s
s
tep
g
u
ar
an
tees
th
at
f
lu
ctu
atio
n
s
in
im
ag
e
b
r
ig
h
tn
ess
,
co
n
tr
ast,
o
r
co
lo
r
d
o
n
o
t
im
p
ed
e
th
e
m
o
d
el’
s
lear
n
in
g
an
d
p
e
r
f
o
r
m
an
ce
.
T
h
e
tr
ai
n
in
g
p
r
o
ce
s
s
is
cr
af
ted
to
b
e
o
r
g
an
ized
,
allo
win
g
th
e
m
o
d
el
to
p
r
o
g
r
ess
iv
ely
ab
s
o
r
b
k
n
o
wled
g
e
f
r
o
m
th
e
en
h
an
ce
d
d
ataset.
T
h
is
m
eth
o
d
in
teg
r
ates
th
e
estab
li
s
h
ed
in
s
ig
h
ts
f
r
o
m
Mo
b
ileNetV3
with
u
n
iq
u
e
c
h
ar
ac
ter
is
tics
p
er
tin
en
t
to
wild
f
ir
e
im
a
g
er
y
,
f
o
s
ter
in
g
a
s
tr
o
n
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
5
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I
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Sci
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2
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Au
g
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:
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lear
n
in
g
f
r
a
m
ewo
r
k
.
T
h
e
p
r
i
m
ar
y
g
o
al
o
f
th
is
tr
ain
in
g
p
h
ase
is
to
g
u
ar
an
tee
th
at
th
e
m
o
d
el
is
ac
q
u
ir
in
g
k
n
o
wled
g
e
in
a
m
an
n
er
t
h
at
is
b
o
th
im
p
ac
tf
u
l
a
n
d
r
eso
u
r
c
ef
u
l.
T
h
is
i
n
v
o
lv
es
e
n
h
an
ci
n
g
th
e
e
f
f
icien
cy
o
f
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
wh
ile
g
u
ar
an
teein
g
th
at
th
e
m
o
d
e
l
attain
s
a
h
ig
h
lev
el
o
f
p
r
ec
is
io
n
in
id
en
tify
in
g
wild
f
ir
es.
T
h
e
ap
p
r
o
ac
h
is
m
eticu
lo
u
s
ly
ad
ju
s
ted
to
ac
h
ie
v
e
an
eq
u
ilib
r
iu
m
b
etwe
en
s
wif
t
ac
q
u
is
itio
n
o
f
k
n
o
wled
g
e
an
d
c
o
m
p
r
e
h
en
s
iv
e
in
s
ig
h
t,
en
ab
lin
g
th
e
m
o
d
el
t
o
ex
ce
l in
id
e
n
tify
in
g
v
ar
io
u
s
f
ir
e
s
itu
atio
n
s
.
Fig
u
r
e.
1
.
Desig
n
o
f
FDX
T
h
e
d
ataset
is
f
ir
s
t
d
iv
id
ed
in
t
o
s
ep
ar
ate
s
u
b
s
ets
f
o
r
tr
ain
in
g
,
v
alid
atio
n
,
an
d
test
in
g
,
ad
h
e
r
in
g
to
an
8
0
:1
0
:1
0
r
atio
.
T
h
is
ap
p
r
o
ac
h
is
wid
ely
ad
o
p
te
d
to
g
u
ar
a
n
t
ee
th
at
th
e
m
o
d
el
en
c
o
u
n
te
r
s
a
d
iv
er
s
e
r
a
n
g
e
o
f
d
ata
wh
ile
also
allo
win
g
f
o
r
p
r
ec
is
e
v
alid
atio
n
a
n
d
test
in
g
o
f
its
p
r
e
d
ictio
n
s
.
T
h
e
d
iv
is
i
o
n
o
f
d
ata
i
n
th
is
m
an
n
er
allo
ws f
o
r
a
m
o
d
el
to
b
e
ev
alu
ated
n
o
t ju
s
t o
n
t
h
e
d
a
ta
it h
as p
r
ev
io
u
s
ly
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co
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n
te
r
e
d
,
b
u
t a
ls
o
o
n
n
ew,
u
n
f
am
iliar
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ata
th
at
it
h
as
n
o
t
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ee
n
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p
o
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ed
to
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ef
o
r
e.
Hy
p
er
-
p
ar
a
m
eter
tu
n
i
n
g
p
la
y
s
a
cr
u
cial
r
o
le
in
th
e
tr
ain
in
g
o
f
d
ee
p
lear
n
in
g
m
o
d
els,
v
ital
f
o
r
attain
in
g
p
ea
k
p
er
f
o
r
m
an
ce
.
I
n
th
is
s
tu
d
y
,
e
s
s
en
tial
p
ar
am
eter
s
s
u
ch
as
th
e
lear
n
in
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r
ate,
b
atc
h
s
ize,
an
d
n
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m
b
er
o
f
e
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o
ch
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wer
e
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r
ef
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lly
o
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ized
.
T
h
e
lear
n
in
g
r
ate
was
in
ten
tio
n
ally
s
et
to
a
l
o
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al
u
e
o
f
0
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litate
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ad
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o
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icatio
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o
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weig
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ts
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u
r
i
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e
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y
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o
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o
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er
s
h
o
o
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g
.
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h
e
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atch
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ize,
wh
ich
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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esian
J
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ile
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e
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ain
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r
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ce
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tili
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th
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at
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atr
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ee
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ar
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ee
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ain
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s
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er
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eter
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atin
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atasets
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
FDX
m
o
d
el
d
e
m
o
n
s
tr
ate
s
r
em
ar
k
ab
le
ca
p
a
b
ilit
ies
th
at
d
is
tin
g
u
is
h
it
in
th
e
f
ield
o
f
wild
f
ir
e
d
etec
tio
n
.
T
h
e
ac
cu
r
ac
y
o
f
9
9
.
9
1
%
an
d
a
r
ec
all
r
ate
o
f
9
9
.
9
3
%
d
em
o
n
s
tr
ate
an
o
u
ts
tan
d
in
g
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p
ab
ilit
y
to
ac
cu
r
ately
d
is
tin
g
u
is
h
b
etwe
e
n
f
ir
e
an
d
n
o
n
-
f
ir
e
s
itu
atio
n
s
with
v
er
y
f
ew
m
is
tak
es.
T
h
e
F1
-
s
co
r
e
o
f
9
9
.
9
2
%
r
ein
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ce
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ates
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ig
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t th
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Gr
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ates
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ates
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I
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ates
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B
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AUTH
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a
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jh
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k
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m
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sh
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p
a
rtme
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t
o
f
CS
E,
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ra
p
h
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Era
Hill
Un
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v
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ra
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m
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to
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i
v
e
rsity
,
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h
ra
d
u
n
,
Uttara
k
h
a
n
d
In
d
ia.
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is
h
a
v
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ts.
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s
p
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li
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m
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h
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n
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5
Article
s
in
IEE
E/
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s/Wo
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telli
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c
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c
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tac
ted
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:
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sh
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m
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c
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m
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Dr
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Bh
a
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tl
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h
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r
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h
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c
a
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tac
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m
a
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:
m
a
d
h
u
li
t
t@g
m
a
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.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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d
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J
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g
&
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ta
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g
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mesh
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585
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c
a
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b
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m
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Evaluation Warning : The document was created with Spire.PDF for Python.