I
MACHINE LEARNING
STYLE TRANSFER OF GEOMETRY FIELD ONTO XIONG’AN CITY
USE OF DIFFERENT FIELD TYPES ONTO THE SAME MAP
01
[ANXIN-L]
Different weights & number of iteration
ANXIN-L_FIELD5 [2048-10-10-500]
ANXIN-L_FIELD6.1 [2048-10-5-250]
Field 5 
Anxin-L 
Field 6.1
ANXIN-L_FIELD5 [2048-10-10-500] 
ANXIN-L_FIELD6.1 [2048-10-5-250]
[ANXIN-M]
Different weights
ANXIN-M_FIELD1 [2048-10-10-250]
ANXIN-M_FIELD6.3 [2048-5-5-250]
Field 1 
Anxin-M 
Field 6.3
ANXIN-M_FIELD1 [2048-10-10-250] 
ANXIN-M_FIELD6.3 [2048-5-5-250]
[ANXIN-S]
Different image size & number of iteration
ANXIN-S_FIELD6.4 [2048-5-5-500]
ANXIN-S_FIELD6.2-zoom [1536-5-5-200]
Field 6.4 
Anxin-S 
Field 6.2
ANXIN-S_FIELD6.4 [2048-5-5-500] 
ANXIN-S_FIELD6.2-zoom [1536-5-5-200]
DIFFERENT FIELD SCALE
02
[RONGCHENG-L]
Different weights
RONGCHENG-L_Field5 (L) [2048-10-10-500]
RONGCHENG-L_Field6.4(M) [2048-5-5-500]
RONGCHENG-L_Field1-zoom(S) [2048-5-5-500]
Rongcheng-L 
Field 5 (L) 
Field 6.4 (M) 
Field 1-zoom (S)
RONGCHENG-L_Field5 (L) [2048-10-10-500] 
RONGCHENG-L_Field6.4(M) [2048-5-5-500] 
RONGCHENG-L_Field1-zoom(S) [2048-5-5-500]
[RONGCHENG-M]
Different weights
RONGCHENG-M_Field6.2 (L) [2048-10-10-500]
RONGCHENG-M_Field6.1(M) [2048-5-5-500]
RONGCHENG-M_Field6.3(S) [2048-15-15-500]
Rongcheng-M 
Field 6.2 (L) 
Field 6.1 (M) 
Field 6.3 (S)
RONGCHENG-M_Field6.2 (L) [2048-10-10-500] 
RONGCHENG-M_Field6.1(M) [2048-5-5-500] 
RONGCHENG-M_Field6.3(S) [2048-15-15-500]
[RONGCHENG-S]
Different weights & number of iterations
RONGCHENG-S_Field5 (L) [2048-10-10-500]
RONGCHENG-S_Field6.1(M) [2048-5-5-250]
RONGCHENG-S_Field6.1-zoom(S) [2048-5-5-500]
Rongcheng-S 
Field 5 (L) Field 6.1 (M) 
Field 6.1-zoom (S)
RONGCHENG-S_Field5 (L) [2048-10-10-500] 
RONGCHENG-S_Field6.1(M) [2048-5-5-250] 
RONGCHENG-S_Field6.1-zoom(S) [2048-5-5-500]
Same Fields Different Parameters
03
[XIONG-L]
Same style data, different image size, weights & number of iteration
XIONG-L_Field5 [2048-10-10-500]
XIONG-L_Field5 [1536-5-5-250]
……………………………………………………….
XIONG-L_Field6.2 [2048-5-5-500]
XIONG-L_Field6.2 [1536-10-10-250]
Field 5 
Xiong-L 
Field 6.2
Xiong-L_Field 5 2048-10-10-500 
Xiong-L_Field 5 1536-5-5-250 
Xiong-L_Field 6.2 2048-5-5-500 
Xiong-L_Field 6.2 1536-20-10-250
[XIONG-M]
Different parameters with same style data
XIONG-M_Field1 [2048-10-5-500]
XIONG-M_Field1 [1536-5-10-250]
Xiong-M_Field1 1536-5-10-250 
Xiong-M_Field1 2048-10-5-500
[XIONG-M2]
XIONG-M2_Field6.3 [2048-10-10-500]
XIONG-M2_Field6.3 [1536-5-5-250]
Xiong-M2 Field 6.3
XIONG-M2_Field6.3 [2048-10-10-500] 
XIONG-M2_Field6.3 [1536-5-5-250]
[XIONG-S]
XIONG-S_Field5 [2048-10-10-500]
XIONG-S_Field5 [1536-5-5-250]
Xiong-S Field 5
Xiong-S_Field 5 [2048-10-10-500] 
Xiong-S_Field 5 [1536-5-5-250]
As image size increases, the clearer the image is.
As the number of iteration increase, the more dense the image is with more repetition of geometry.
DIFFERENT FIELD SCALE
04
[SHAOCHEDIAN-L]
SHAOCHEDIAN-L_FIELD 5 (L) [2048-10-10-500]
SHAOCHEDIAN-L_FIELD 1 (M) [2048-5-5-500]
SHAOCHEDIAN-L_FIELD 6.1 (S) [2048-5-5-500]
Shaochedian-L Field 5 
Field 1 Field 6.1
SHAOCHEDIAN-L_FIELD 5 (L) [2048-10-10-500] 
SHAOCHEDIAN-L_FIELD 1 (M) [2048-5-5-500] 
SHAOCHEDIAN-L_FIELD 6.1 (S) [2048-5-5-500]
II
MACHINE LEARNING
STYLE TRANSFER OF REFERENCE CITIES ONTO XIONG’AN
[Anxin-M_SG-M]
1024-10-10-500
Anxin-M 
SG-M
Anxin-M_SG-M 1024-10-10-500
[ANXIN-L_TOKYOCHIYODA-L]
2048-5-5-500
Anxin-L 
Tokyo Chiyoda-L
Anxin-L_TokyoChiyoda-L 2048-5-5-500
[Baiyangdian-L_SG-S]
1024-5-5-500
Baiyangdian-S 
SG-S
Baiyangdian-S_SG-S 1024-5-5-500
[Intersection1_TokyoChiyoda-L]
1024-5-5-500
Intersection-1 
TokyoChiyoda-L
INTERSECTION1_TOKYOCHIYODA-L 1024-5-5-500
[Rongcheng-M_Barcelona-L]
2048-5-5-500
Intersection-1 
TokyoChiyoda-L
RONGCHENG-M_BARCELONA-L 2048-5-5-500
[Shaochedian-L_HK-S]
1024-5-5-500
Intersection-1 
TokyoChiyoda-L
SHAOCHEDIAN-L_HK-S 1024-5-5-500
[Xiong-L_TokyoChiyoda-L]
1024-5-5-200
Xiong-L 
TokyoChiyoda-L
SHAOCHEDIAN-L_HK-S 1024-5-5-500
[Xiong-S_Canberra-S]
2048-5-5-500
Xiong-S 
Canberra-S
XIONG-S_CANBERRA-S 2048-5-5-500
III
MACHINE LEARNING
STYLE TRANSFER OF GEOMETRY FIELD ONTO REFERENCE CITIES
[ABERDEEN-L]
DIFFERENT FIELD WITH DIFFERENT SCALES
Abd-L Field 1 (M) Field 5 (L) 
Forld 6.2(L)
Abl-L_Field1(M) 2048-10-10-500 
Abl-L_Field5(L) 2048-10-10-500 
Abl-L_Field6.2(L) 2048-5-5-250 Clearer Sturcture
[ABERDEEN-S]
DIFFERENT FIELD WITH DIFFERENT SCALES
Field 1 (L) 
Abd-S 
Field 5 (L) Field 6.3 (M) Field 6.4 (S)
[HAMBURG-M]
DIFFERENT FIELD WITH DIFFERENT SCALES
Hamburg-M Field 5 (L) Forld 6.2(M) Field 1-zoom (S)
Hamburg-M_Field 
Hamburg-M_Field 
Hamburg-M_Field
[HAMBURG-S]
DIFFERENT FIELD WITH DIFFERENT SCALES
Field 1 (L) Field 6.1 (L) 
Field 6.1-zoom (S) 
Hamburg-S Field 6.3 (M)
Hamburg-S_Field1 (L) 2048-10-10-500 
Hamburg-S_Field6.1 (L) 2048-5-5-250 
Hamburg-S_Field6.3 (M) 2048-10-10-500 
Hamburg-S_Field6.1-zoom (S) 2048-5-5-250
[SG-L]
DIFFERENT FIELD WITH DIFFERENT SCALES
SG-L Field 1 (L) Field 6.1 (M) Field 6.4 (S)
SG-L_Field1(L) 2048-10-10-500 
SG-L_Field6.1(M) 2048-10-10-500 
SG-L_Field6.4(S) 2048-10-10-500
[SG-M]
DIFFERENT FIELD WITH DIFFERENT SCALES
Field 1 (L) Field 5 (L) Field 6.4 (M) Field 1-zoom (S)
[SG-S]
DIFFERENT FIELD WITH DIFFERENT SCALES
SG-S Field 6.4 (L) 
Field 5-zoom (M) 
Field 6.4-zoom (S)
SG-S_Field6.4 (L) 2048-5-5-250 
SG-S_Field5-zoom (M) 2048-5-5-250 
SG-S_Field6.4-zoom (S) 2048-5-5-250
[VENICE-S]
DIFFERENT FIELD WITH DIFFERENT SCALES
Field 6.2 (L) 
Venice-S 
Field 6.3-zoom (S)
Venice-S_Field6.2(L) 2048-10-10-500 
Venice-S_Field6.3-zoom (S) 2048-10-10-500
IV
MACHINE LEARNING
STYLE TRANSFER OF REFERENCE CITIES ONTO XIONG’AN
[Anxin-L+ABD-S-Field6.4 2048-10-10-50]
Anxin-L+ABD-S-Field6.4 2048-10-10-500
[Anxin-M_SG-S-Field5 2048-10-10-500]
SG-S 
Field 5 
Anxin-M
Anxin-M_SG-S-Field5 2048-10-10-500
[Baiyangdian-S_SG-S-Field5-zoom 1536-5-5-200]
SG-S Field 5-zoom Baiyangdian-S
Baiyangdian-S_SG-S-Field5-zoom 1536-5-5-200
[Xiong-M2_ABD-S-Field6.4 1536-5-5-200]
SG-S Field 5-zoom 
Baiyangdian-S
Xiong-M2_ABD-S-Field6.4 1536-5-5-200
[Xiong-S_SG-S-Field5-zoom 1536-5-5-200]
SG-S Field 5-zoom Xiong-S
XIONG-S_SG-S-FIELD5-ZOOM 1536-5-5-200
A{AI} 