Research and Development of Spatial Distribution Data of
Buildings in Haibei Prefecture, Qinghai Province (2020)
Ma, M. F.1 Liu, F. G.1,2* Zhou, Q.1,2 Che, Q.1,2 MA, X. Y.3 Gao, Y.1 Chen, Y. M.1 Gao, H. X.1 Ding, Y. H.1
1. School of Geographical Sciences,
Qinghai Normal University, Xining 810008, China;
2. Academy of Plateau Science
and Sustainability, Xining 810008, China;
3. Hualong Qunke New Area
Middle School, Haidong 810900, China;
Abstract: Haibei Tibetan Autonomous Prefecture (hereinafter
referred to as Haibei Prefecture) is located in the northeastern part of
Qinghai Province, and is one of the regions where more earthquakes occur in
Qinghai Province, Among them, Menyuan has been the site of several major
earthquakes, the most recent of which occurred on January 8, 2022 with a
magnitude of 6.9 in Menyuan.In earthquakes, there is a lack of data on
buildings as one of the most important substrates of disaster-bearing
bodies.Based on AI Earth platform of Ali cloud and ESA’s 10 m resolution land use
data, the buildings within the counties of Haibei Prefecture were extracted by
using the 0.6 m high resolution image of July 2020 and combined with the
construction land use data in the land use.Finally, the spatial distribution
dataset of regional buildings in Haibei Prefecture (2020) was obtained,
including building data of Qilian County, Menyuan County, Guncha County and
Haiyan County. The dataset is stored in .ship format with 37 MB of data (compressed into 1 file, 5.35 MB in total).
Keywords: Haibei Prefecture
Qinghai Province; building; Spatial distribution; 2020
DOI: https://doi.org/10.3974/geodp.
2022.04.15
CSTR: https://cstr.escience.org.cn/CSTR:20146.14.2022.04.15
Dataset Availability Statement:
The dataset supporting this paper
was published and is accessible through the Digital Journal of Global Change Data Repository
at: https://doi.org/10.3974/geodb.2021.11.01.V1 or
https://cstr.escience.org.cn/CSTR:20146.11.2022.11.01.V1.
1 Introduction
Earthquake
disasters are characterized by high suddenness, destructiveness and
uncertainty. Directly affecting human life safety issues, about 60% of global
land-based destructive seismic hazards occur in the Mediterranean-Himalayan
seismic zone [1], Earthquakes occur frequently in the northeastern
region of Qinghai Province, and Haibei Prefecture is one of the more frequent
regions in Qinghai Province, On January 21, 2016, a 6.4 magnitude earthquake
occurred in Menyuan County, Haibei Prefecture, Qinghai Province, with a depth
of 10 km, various economic losses were estimated to reach 76 million yuan, 600
houses were damaged, and 9 people were injured.At 1:00 on January 8, 2022, a
6.9 magnitude earthquake occurred in Menyuan County, Haibei Prefecture, with a
depth of 10 km, in which 5,831 people from 1,662 households were affected,
4,052 houses were damaged, 3 bridges were damaged, 17 culverts were damaged,
and drainage, heating and roads were damaged to varying degrees.On August 15,
2022, a 4.7 magnitude earthquake occurred in Qilian County, Haibei Prefecture, Qinghai,
with a depth of 8 km, and some houses and ecological environment were damaged
to different degrees.where about 95% of the casualties in earthquakes are
caused by building damage and collapse [2].
The total area of Haibei Prefecture is
34068.44 square kilometers, with a population of 265,300 people, the highest
altitude is 5287 meters, the lowest altitude is 2180 meters, and the altitude
of more than 3000 meters accounts for 85% of the state.There are river valleys,
basins, hills, etc.The ecological environment of the region is relatively
fragile.In 2017, the state established the Qilian Mountain National Park in
Haibei Prefecture, as one of the ten pilot national parks in China, which is of
great significance for the regional ecological security barrier and water
connotation, and can better protect the biodiversity and natural ecological
environment of the Qilian Mountain region.
This paper obtains the spatial distribution
of building contours and building footprint in the study area by using AI Earth
platform and remote sensing images with high accuracy resolution, which
requires high quality of remote sensing images in data processing and has time
limitation in platform computing.Buildings, as one of the most important
substrates of disaster-bearing bodies, are also important basic data for urban
refinement management [3]. An accurate understanding of the spatial
distribution of buildings is important for later exploring the social
development, earthquake emergency response, disaster prevention and mitigation,
and disaster assessment of the region.
2 Metadata of the Dataset
Metadata of the Spatial distribution dataset of buildings in Haibei,
Qinghai of China (2020) [4] is showed in Table 1.
3 Methods
3.1 Study Area and Data Sources
The study area of this paper is the Tibetan
Autonomous Prefecture of Haibei Prefecture, Qinghai Province, which includes
four counties, including Menyuan, Qilian, Haiyan and Gangcha counties.It is
located in the northeastern part of the Qinghai-Tibet Plateau and the western
edge of the Loess Plateau, between 36°44′00″ and 39°05′18″ north latitude and 98°5′00″
and 102°41′03″ east longitude, and belongs to the continental climate of the
Table
1 Metadata Summary of the Spatial distribution
dataset of buildings in Haibei,
Qinghai of China (2020)
Items
|
Description
|
Dataset full name
|
Spatial distribution dataset of buildings in Haibei, Qinghai of China
(2020)
|
Dataset short name
|
BuildingHaibei2020
|
Authors
|
Ma, M. F., Qinghai Normal University,
841880419@qq.com
Liu, F. G., Qinghai Normal University
,lfg_918@163.com
Zhou, Q.,Qinghai Normal
University, 598416603@qq.com
Che, Q.,Qinghai Normal University,
872636400@qq.com
Gao, Y.,Qinghai Normal University,2480216410@qq.com
MA ,X. Y.,Hualong
Qunke New Area Middle School,935558381@qq.com
Chen,Y. M., Qinghai Normal University
,15643198582@qq.com
Gao, H. X., Qinghai Normal University
,3506422405@qq.com
Ding, Y. H., Qinghai Normal
University ,905087165@qq.com
|
Geographical region
|
Haibei Prefecture, Qinghai Province
|
Year
|
2020
|
Data format
|
.shp
|
|
|
Data size
|
5.35MB
|
|
|
Data files
|
.ship
|
Foundation(s)
|
(Data volume is 37 MB (compressed into1 files, 5.35 MB in total)
|
Data publisher
|
Global Change Research Data Publishing &
Repository, http://www.geodoi.ac.cn
|
Address
|
No. 11A, Datun Road, Chaoyang District, Beijing 100101, China
|
Data sharing policy
|
Data from the Global Change
Research Data Publishing & Repository includes metadata, datasets (in the Digital Journal of Global Change Data Repository), and
publications (in the Journal of Global Change Data & Discovery). Data sharing policy includes: (1) Data are openly
available and can be free downloaded via the Internet; (2) End users are
encouraged to use Data subject to citation; (3) Users, who are by definition
also value-added service providers, are welcome to redistribute Data
subject to written permission from the GCdataPR Editorial Office and the
issuance of a Data redistribution license; and (4) If Data are used to
compile new datasets, the ‘ten per cent principal’ should be followed such
that Data records utilized should not surpass 10% of the new
dataset contents, while sources should be clearly noted in suitable places in
the new dataset[7]
|
Communication and searchable system
|
DOI, DCI, CSCD, WDS/ISC, GEOSS, China GEOSS, Crossref
|
plateau.Among them, Mengyuan County has experienced more frequent large
earthquakes in recent years.
For the data, the 10 m resolution land use
data was obtained from ESA, and the 0.6 m high resolution remote sensing image
in July 2020 was obtained from Shuijingjing software platform, based on the
data obtained from AI Earth platform and Arcgis 10.7 software of AliCloud.
3.2 Technical Route
The technical route
of this study consists of four parts: downloading remote sensing image data,
data processing, importing into the platform and spatialization of buildings
(Figure 1), firstly, downloading the 0.6m high precision remote sensing impact
data of 2020 (selected to minimize clouds and fog, etc.) and the 10m land use
data of ESA 2020, secondly, processing the downloaded data and obtaining the
regional construction Secondly, the downloaded data are processed and the
regional construction land use data are obtained through the land use data,
again the processed data are typed into the platform for extraction, and
finally, the spatial distribution data set of buildings in Haibei Prefecture is
obtained by processing with Arcgis 10.7 software.

Figure
1 Technicalogy
roadmap
4 Data Results and Validation
4.1 Data Composition
The data set of
Haibei Prefecture includes four counties of Haibei Prefecture, including Menyuan
County, Qilian County, Gangcha County and Haiyan County, etc. This data is
dated July 2020, and the distribution area of the building distribution data is
urban and rural in the study area, and the final result shipment file format
(Figure 2).

Figure 2 Spatial distribution of buildings in Haibei
Prefecture, Qinghai Province
The
distribution of buildings in Haibei Prefecture generally shows the spatial
characteristics of dense east and sparse west, dense
south and sparse north. Among the four counties in Haibei Prefecture, Mengyuan
County has the largest number of buildings, accounting for 55% of the total
regional buildings, followed by Qilian County with 18.5%, Haiyan County with
15%, and Gangcha County with 11.5%.Population distribution Mengyuan County
accounts for 52.3% of the state, followed by Qilian County with 18.3%, Haiyan
County with 15.3% and Gangcha County with 14.1%, the number of buildings is
basically the same compared to the data of the 7th census.The largest area is
Qilian County, accounting for 40.6% of the state’s land and only 18.3% of the
population, due to Qilian County is mainly pastoralism, grass hills cover a
large area and the establishment of the Qilian Mountains National Park, in
order to ecological and biodiversity conservation, to limit human activities.
4.2 Data Products
This data is the
data of building distribution in July 2020, distributed in the cities and
villages of the study area. The final result is the ship file format.
4.3 Data Validation
By comparing some
areas arbitrarily selected with the original downloaded 0.6m high precision
remote sensing images, the accuracy of urban areas can reach more than 90%.5
Discussion and Conclusion.Residential neighborhoods and office buildings in the
city can be well identified, mainly because of the large building spacing and
regular shape (Figure 3), while the identification is generally poor for
irregular or obsolete houses in the city or in the suburbs (Figure 4).The
accuracy of the rural area can reach 85%, for the agricultural area, mainly the
rural areas in Haibei Prefecture of Qinghai Province are mainly inhabited by
villages, which are relatively concentrated, which results in a small and
irregular shape of household-to-household spacing and poor recognition (Figure
5).For pastoral areas, on the other hand, the house buildings are more
scattered and each household is relatively independent, and the buildings are
regular in shape and widely spaced, which are generally better identified but
less numerous (Figure 6).

|

|
Figure 3 Comparison of urban building profiles
|
Figure 4 Comparison of building profiles in the suburbs
|

|

|
Figure 5 Comparison of rural building contours
|
Figure 6 Comparison of building outlines in
pastoral areas
|
4.3 Exploration
of Preliminary Classification of Building-Related Attributes
After completing
the preparation of spatial distribution data of buildings in Haibei Prefecture,
attributes such as spatial location and area (one floor) of buildings were
obtained, while for other attributes such as height, structure type, total
building area and construction year could not be obtained.Therefore, through my
participation in the Qinghai-Tibet second scientific research project, I found
that the difference between urban and rural buildings in northeastern Qinghai
is obvious. In terms of height, rural areas are generally dominated by one
story with a height of 3.5 meters, while urban areas are generally dominated by
two to five stories, with residential houses mainly dominated by five stories
and stores mainly dominated by three stories with a height of 3 meters.In terms
of structure types, buildings before 2010 were mainly brick and mortar, while
buildings after 2010 were mainly frame and rural houses were mainly brick and
mortar.As far as the total building area is concerned, the area of the ground
floor in rural areas is the total building area, while in urban areas the
number of storeys is obtained through the height of the storey, and the total
building area can be obtained through the number of storeys. The year of
construction is obtained by means of field survey. The relevant attributes can
be obtained by the above methods, but they are more time-consuming and
laborious.In the next step, by means of remote sensing, combined with GDP,
population and other data, we can try to get the height, structure type, area,
construction age and other attributes of buildings, which can provide strong
support for regional rescue and disaster relief and disaster damage assessment.
5.1 Discussion
The spatial
distribution of buildings in Haibei Prefecture, Qinghai was obtained through
AliCloud’s AI Earth platform and land use data.However, in the process of data
production, high-precision remote sensing images are required and the platform
import requires data less than 5G, so the data production process takes a long
time.Meanwhile, after this application of land use data, the effect is
relatively good and can reduce the size of remote sensing images. However,
after extracting the buildings within the counties of Haibei Prefecture, it is
found that the buildings are more dense in rural
gathering areas or urban suburbs, and there are contiguous buildings, which
makes it necessary to manually review and correct the data subsequently, so as
to improve the accuracy of the data.
5.2 Conclusion
Through this
method, we can get the spatial data of buildings in each county of Qinghai
Haibei Prefecture in 2020, and when natural disasters such as earthquakes,
floods and mudslides occur, the data accuracy is relatively good for buildings
in the city, which can be used for reference, and the buildings in the suburbs
are relatively poor, which can reflect the number of dwellings and other
related information through the density as well.The accuracy of rural areas is
relatively low, where the accuracy of pastoral areas is high, then it can be
used, while agricultural areas can be used due to poor centralized
identification, which can be reflected by density and other methods.This data
provides basic data for the local government’s timely emergency plan, and at
the same time, the building, as one of the most important disaster-bearing
base, can improve help for subsequent disaster damage assessment, prevention
and mitigation in a timely and accurate manner.
Author Contributions
Liu,
F.G., Zhou, Q. designed the algorithms of dataset. Ma,M.F
collected and processed the remote sensing image data; Ma,M.F. Chen. Y.M, Gao,H.X, and Ding,Y.H collected and compared the methods;
Chen,Q. and Gao, Y. did the data validation; Ma,M.F. andMa ,X.Y. wrote the data
paper, etc.
Conflicts of Interest
The
authors declare no conflicts of interest.
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