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Datasets:
liuhangbiao
/
China-Building-Footprints-CMAB-Mirror

Modalities:
Geospatial
Text
Formats:
csv
Languages:
Chinese
English
Size:
< 1K
Tags:
geospatial
vector-data
building-footprints
building-rooftops
china
gis
Libraries:
Datasets
pandas
Polars
License:
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Name
stringlengths
3
10
Description
stringlengths
10
61
Value meaning
stringclasses
7 values
Shape_id
ID of the building
β€”
Rect_Area
Minimum Bounding Rectangle Area
m2
Aspect_Rat
Building Length-to-Width Ratio
β€”
Length
Building Length
m
Width
Building Width
m
Area_Ratio
Ratio of Contour Area to Ideal Shape Area
β€”
Shape_A
Building Base Area
m2
Shape_L
Building Perimeter
m
NPI
Building Compactness
β€”
MinCircle
Minimum Enclosing Circle Area
m2
XzCity_id
Administrative City Level
β€”
XzCity_Nme
Administrative Province
β€”
XzCity_Nm
Administrative City
β€”
XzCity_Nmx
Administrative County/District
β€”
Sum_spbu
Total Building Base Area
m2
Num_bu
Number of Buildings
β€”
Den_bu
Building Density
β€”
Dis_Road
Distance to Nearest Secondary Road
m
IfNearRoad
Is Roadside Building or not
0: not near roadοΌ›1: near road
CityFun
Distance to Urban Functional Center
m
QHCITY_id
Natural Climatic Subdivision
β€”
pred_h_r
Building Height
m
Floor
Building Floor
β€”
Bu_Area
Building Area
m2
Block_id
ID of the block on which the building is located
β€”
Block_area
Block Area
m2
Block_leng
Block Perimeter
m
StCity_id
ID of the spatial city on which the building is located
β€”
ISWATER
Contain Water Bodies or not
0: contains no water bodies οΌ›1:contains water bodies
NBL1
Number of beauty salon types poi
β€”
DBL1
Density of beauty salon types poi
Per m2
NBL2
Number of transportation facility types poi
β€”
DBL2
Density of transportation facility types poi
Per m2
NBL3
Number of leisure and entertainment types poi
β€”
DBL3
Density of leisure and entertainment types poi
Per m2
NBL4
Number of company enterprise types poi
β€”
DBL4
Density of company enterprise types poi
Per m2
NBL5
Number of inlet and outlet types poi
β€”
DBL5
Density of inlet and outlet types poi
Per m2
NBL6
Number of medical treatment types poi
β€”
DBL6
Density of medical treatment types poi
Per m2
NBL7
Number of real estate types poi
β€”
DBL7
Density of real estate types poi
Per m2
NBL8
Number of governmental agencies types poi
β€”
DBL8
Density of governmental agencies types poi
Per m2
NBL9
Number of educational training types poi
β€”
DBL9
Density of educational training types poi
Per m2
NBL10
Number of cultural media types poi
β€”
DBL10
Density of cultural media types poi
Per m2
NBL11
Number of cultural media types poi
β€”
DBL11
Density of cultural media types poi
Per m2
NBL12
Number of car service types poi
β€”
DBL12
Density of car service types poi
Per m2
NBL13
Number of life service types poi
β€”
DBL13
Density of life service types poi
Per m2
NBL14
Number of restaurant types poi
β€”
DBL14
Density of restaurant types poi
Per m2
NBL15
Number of administrative landmark poi
β€”
DBL15
Density of administrative landmark poi
Per m2
NBL16
Number of shopping poi
β€”
DBL16
Density of shopping poi
Per m2
NBL17
Number of sports poi
β€”
DBL17
Density of sports poi
Per m2
NBL18
Number of hotel poi
β€”
DBL18
Density of hotel poi
Per m2
NBL19
Number of finance poi
β€”
DBL19
Density of finance poi
Per m2
DBL
Total POI density
Per m2
RBL1
Proportion of beauty salon types poi
β€”
RBL2
Proportion of transportation facility types poi
β€”
RBL3
Proportion of leisure and entertainment types poi
β€”
RBL4
Proportion of company enterprise types poi
β€”
RBL5
Proportion of inlet and outlet types poi
β€”
RBL6
Proportion of medical treatment types poi
β€”
RBL7
Proportion of real estate types poi
β€”
RBL8
Proportion of governmental agencies types poi
β€”
RBL9
Proportion of educational training types poi
β€”
RBL10
Proportion of cultural media types poi
β€”
RBL11
Proportion of cultural media types poi
β€”
RBL12
Proportion of car service types poi
β€”
RBL13
Proportion of life service types poi
β€”
RBL14
Proportion of restaurant types poi
β€”
RBL15
Proportion of administrative landmark poi
β€”
RBL16
Proportion of shopping poi
β€”
RBL17
Proportion of sports poi
β€”
RBL18
Proportion of hotel poi
β€”
RBL19
Proportion of finance poi
β€”
M_Block
POI diversity index
β€”
Sum_Bu
Total building area
m2
Plot_rat
Plot ratio
β€”
Mean_H
Average Height of Buildings
m
Mean_F
Average Floor of Buildings
β€”
type_2023
Building functions identified by AOI data
β€”
predict
Predicted building function
β€”
Age_IS
Number of pixel identified by GAIA data
β€”
Age
Building age
β€”
StrVi_100
name of existing street view observation points in the buffer
β€”
mFa_13_100
The disorder score of Buildings with damaged facades in 2013
N: There were no street view images at the site during that year. M: There are no observation points in the buffer. 0-1:1 means the disorder type exist, 0 means none. There are severl street views in a point, contributing the value in 0-1.
mFa_14_100
The disorder score of Buildings with damaged facades in 2014
null
mFa_15_100
The disorder score of Buildings with damaged facades in 2015
null
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Origin Data 

@misc{Zhang2025CMAB,
  author       = {Zhang, Yecheng and Zhao, Huimin and Long, Ying},
  title        = {{CMAB-The World's First National-Scale Multi-Attribute Building Dataset}},
  year         = {2025},
  month        = apr,
  publisher    = {figshare},
  doi          = {10.6084/m9.figshare.27992417},
  url          = {https://doi.org/10.6084/m9.figshare.27992417},
  howpublished = {dataset}
}

Paper 

@article{Zhang2025SciData,
  author  = {Zhang, Y. and Zhao, H. and Long, Y.},
  title   = {{CMAB: A Multi-Attribute Building Dataset of China}},
  journal = {Scientific Data},
  volume  = {12},
  number  = {430},
  year    = {2025},
  doi     = {10.1038/s41597-025-04730-5},
  url     = {https://doi.org/10.1038/s41597-025-04730-5}
}
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