Data Paper Zone II Versions EN1 Vol 4 (3) 2019
A High-resolution DOM Dataset of the China-Pakistan Economic Corridor (Kashgar to Islamabad)
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： 2018 - 08 - 01
： 2018 - 10 - 25
： 2019 - 07 - 18
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Abstract & Keywords
Abstract: An important part of the Belt and Road Initiative, the China-Pakistan Economic Corridor plays a significant role of demonstration and promotion in the implementation of the initiative. The dataset is made up of full-color and multi-spectral 2m fusion images accessed by GF-1 and GF-2 domestic satellites with the format of TIFF; the space range is 23°54′N–39°12′N,71°24′E–76°48′E, about 60 km on both sides of the China-Pakistan Highway from Kashgar to Islamabad.The time range is 2013–2017. The data set orthorectification accuracy error is 0.35 pixel in the X direction and 0.4 pixel in the Y direction; the root mean square error is 0.42 pixel in the X direction and 0.38 pixel in the Y direction. In terms of image fusion, the PanSharpening method is better for high-resolution images. This dataset could enrich the spatial basic data resources of the region and could be applied to the infrastructure construction planning, natural disaster warning, ecological security evaluation and other aspects of the China-Pakistan Economic Corridor.
Keywords: the China-Pakistan Economic Corridor, orthophoto dataset, data preprocessing, orthorectification, data fusion
Dataset Profile
 Dataset name Data on Stone-sliding Survey and Susceptibility Distribution in Gaizi Valley Data authors Tian Deyu, Zhang Yaonan, Han Liqing, Luo Lihui, Ai Minghao * Author contact: Zhao Yaonan: (aonan@lzb. ac. cn) Data time range 2017 Geographic scope 38°30′N–39°00′N，75°00′E–75°30′E Spatial resolution 30 m Data volume 2694 KB Data format TIF, SHP Data service system URL http: //www. crensed. ac. cn/portal/metadata/9cf11d7a-3b96-4ca7-a96a-57a5c03d6c86 Sources of funding Data Sharing Fundamental Program for Construction of the National Science and Technology Infrastructure Platform (Y719H71006) Dataset components 1. Vector data on the stone-sliding survey spot in Gaizi Valley; 2. Raster data on stone-sliding susceptibility distribution in Gaizi Valley
1.   Introduction
As an important part of the Belt and Road Initiative, the China-Pakistan Economic Corridor starts from Kashgar in the north and stretches to Gwadar Port in Pakistan in the south. It mainly extends along China-Pakistan Highway as an economic artery including highways, railways, oil and gas and cable channels[1]. The Corridor passes through the Himalayas, the Karakorum Mountains, and the Hindu Kush Mountains. The terrain is high in the north and low in the south (460-4750 meters above sea level), with deep canyons, widespread glaciers and towering snow peaks[2]. The vertical zoning of the climate here is obvious. Affected by climate, altitude and topography, the growth of the vegetation is remarkably distinct. The special natural geographical conditions give rise to the frequent occurrence of various geological disasters, such as ice avalanche, avalanche, freeze-thaw, landslide, mudslide, rock fall, flood, glacial lake break, etc., which would bring about great challenges to infrastructure planning and construction[3][4][5] .
With the introduction of a series of satellite remote sensing data like SPOT, high-resolution images have been rapidly and widely used in areas such as regional mapping[6], disaster monitoring[7]and resource survey[8][9] in recent years. Carrying out thematic mapping and disaster monitoring of geological disasters in the China-Pakistan Economic Corridor by taking advantages of macroscopic, rapid, dynamic, high-space, high-time and high-spectral high-resolution earth observation system, and integrating data of meteorology, hydrology, geology, glaciers, frozen soils, soil types, etc., the establishment of a mechanism model for hazard-inducing cognition and evolution analysis will provide strong data support for infrastructure construction in the region[10][11] .
The dataset all adopt domestic high-resolution special data resources. It is also an attempt to use the domestic high-scoring data to collect the basic image data of the China-Pakistan Economic Corridor. It realizes the 2 m resolution of the China-Pakistan Economic Corridor (Kashgar to Islamabad). The image data cover about 60 km of digital orthophoto maps (DOM) on both sides of the China-Pakistan Highway, including 415 km (G314 National Road) in China and 809 km (N35 National Road) in Pakistan. The dataset enriches the spatial data resources of this region and expands the applicable areas of high-resolution data.
2.   Data Acquisition and Processing Methods
2. 1 Data Source and Preprocessing
The data sources mainly include satellite images and digital elevation models, as shown in Table 1. The orthophoto data of the China-Pakistan Economic Corridor (Kashgar to Islamabad) are all derived from China’s high-resolution earth observation system, GF 1 and GF 2; the former provides full color 2 meter and multi-spectral 8 m data formed from 2013 to 2017; the latter provides full-color one meter and multi-spectral 4 m data formed from 2015 to 2017. In the Chinese segment of the China-Pakistan Economic Corridor with good imaging conditions and the northern part of Pakistan, the cloud cover of images could be controlled to below 5%, and the cloud cover in central Pakistan could be controlled to below 10% with the corresponding cloud removal process to be carried out. Among them, the GF-1 data account for 97.2%, while the GF-2 provides the supplementary data of the image blank area.
Table 1   Data list for research
 Serial number Name Date Source Type 1 GF-1 images of the China-Pakistan Economic Corridor (Kashgar to Islamabad) 2013-2017 Gansu Data and Application Center for High-resolution Earth Observation System Raster 2 GF-2 images of the China-Pakistan Economic Corridor (Kashgar to Islamabad) 2015-2017 Gansu Data and Application Center for High-resolution Earth Observation System Raster 3 ASTER GDEM dataset 2009 American National Aeronautics and Space Administration (NASA) and Japanese Ministry of Economy, Trade and Industry (METI) Raster
This dataset uses the digital elevation model to perform orthorectification, full-color and multi-spectral fusion, uniform color, and mosaic processing of GF-1 and GF-2 image data through ENVI and ArcGIS software tools. Due to the time resolution limitation of image acquisition of GF-1 and GF-2 satellites, the image resources of the study area stretches over 5 years. Considering the seasonal differences of imaging time, all the image data of GF-1 satellite are uniformly colored. For the uncovered area of the GF-1 satellite, the image resource of GF-2 satellite can be used as a supplement, with independently-processed uniform color. Finally, a dataset of digital orthophoto block can be generated according to a certain range for users to use for block search (Fig. 1). The accuracy and effect of orthorectification and data fusion are the key to data production.
The ASTER GDEM[12]data were developed by NASA based on the vertical observation of the thermal radiation and reflectometer (namely ASTER) 3N and 3B bands of the new generation of earth observation satellite Terra satellite. The development lasted nearly 10 years. The data spatial resolution is 1′′×1′′ (about 30 m×30 m), and the vertical accuracy is about 20 m globally when the built-in reliability is 95%. As the research area is mostly in the alpine region, and the ground high-precision control data are almost blank, it is difficult to guarantee the DEM accuracy.

Fig. 1   Orthophoto production process
According to the analysis of Zhao Guosong[13]and other researchers in the 3°×3° research area in Central China, the elevation value of ASTER GDEM products was 5.42 m lower than the average STRM DEM. Liu Yong[14]pointed out that the accuracy of STRM DEM in mountainous areas is much lower than that in plain areas, but the overall accuracy is acceptable. As shown in Fig. 2, in the statistical analysis of the ASTER GDEM, STRM DEM and ASTER L1T single-view DEM in Muztag Ata and Kongur Tagh, the ASTER L1T single-view DEM presented obvious abnormalities, and the ASTER GDEM and STRM DEM had no obvious abnormalities. However, the maximum STRM DEM was more than 200 m above the main peak of the Muztag Peak, while the ASTER GDEM was basically consistent with the measured values in the field. Therefore, the dataset adopts the ASTER GDEM as the source of the orthorectified elevation data.

Fig. 2   Statistical analysis results of elevation data
2. 2 Data processing methods
2. 2. 1. RPC Orthorectification Model
The orthorectification model of satellite images is divided into the strict geometric correction model and the approximate correction model. This dataset cannot accurately acquire the sensor parameters and orbital parameters of GF-1 and GF-2 satellites, and cannot establish a collinear equation; the polynomial approximate correction model is applicable to the flat ground area. Therefore, when it comes to the more complex areas along the China-Pakistan Economic Corridor, the RPC rational function model method is chosen for image orthorectification.
The rational function model[15]is to associate the coordinates (r,c) of the image point in the image coordinate system and its corresponding ground point with the space coordinate (X,Y,Z) in the object coordinate system after the spatial transformation projection with a ratio polynomial. In order to enhance the stability of the parameter solution, the ground coordinates and image coordinates are normalized into the range from −1.0 to 1.0.
The definition is as follows:
(1)
In the formula (1), (Xn,Yn,Zn ) is a regularized ground point coordinate, and (rn,cn ) is a regularized image point coordinate. Polynomials P1,P2,P3,P4 are as in equation (2):
(2)
Polynomial coefficient 00≤n1 ≤3; 0≤n2 ≤3; 0≤n3 ≤3, and nn1 +n2 +n3 =3, polynomial P1,P2,P3,P4 are cubic polynomials containing 20 coefficients, as in formula (3):
P1=a0+a1X+a2Y+a3Z+a4XY+a5XZ+a6YZ+a7X2+a8Y2+a9Z2+a10XYZ+a11X3+a12X2Y+
a13X2Z+a14XY2+a15XZ2+a16Y3+a17Y2Z+a18YZ2+a19Z3;
P2=b0+b1X+b2Y+b3Z+b4XY+b5XZ+b6YZ+b7X2+b8Y2+b9Z2+b10XYZ+b11X3+b12X2Y+
b13X2Z+b14XY2+b15XZ2+b16Y3+b17Y2Z+b18YZ2+b19Z3; (3)
P3=c0+c1X+c2Y+c3Z+c4XY+c5XZ+c6YZ+c7X2+c8Y2+c9Z2+c10XYZ+c11X3+c12X2Y+
c13X2Z+c14XY2+c15XZ2+c16Y3+c17Y2Z+c18YZ2+c19Z3;
P4=d0+d1X+d2Y+d3Z+d4XY+d5XZ+d6YZ+d7X2+d8Y2+d9Z2+d10XYZ+d11X3+d12X2Y+
d13X2Z+d14XY2+d15XZ2+d16Y3+d17Y2Z+d18YZ2+d19Z3;
And:
(4)
X0,Y0,Z0,Xs,Ys,Zs in the formula (4) are scale factors for displacement of ground coordinates; r0,c0,rs,cs are scale factors for displacement of image coordinates. a,b,c,d in the formula (3) are polynomial coefficients, and a0,b0,c0,d0 usually take the value of 1.
2. 2. 2. Image Fusion Methods
Remote sensing image fusion[16][17] is a multi-level and multi-layer processing process for multiple sensor images to improve image resolution, enhance target features, and improve classification accuracy. It is an important means to improve the applicability of remote sensing images. This dataset uses the Nearest Neighbor Diffusion pan-sharpening method to complete the fusion of full-color and multi-spectral data of GF-1 and GF-2 satellite images. Compared with Brovey Transform, Brovey, Principal Component Analysis, Gram-Schmidt and other methods, the PanSharpening method can better preserve color, texture and spectral characteristics of multi-spectral images and by reflecting the mean, standard deviation, average gradient, and carry out quantitative evaluation of image quality by reflecting the mean value, standard deviation, average gradient, spectral quality deviation index, correlation coefficient and cross entropy of image information.
(1) Mean value (μ)
The arithmetic mean value of all the image pixel gradation reflects the average reflectivity of the features in the image. Where F(i,j) is the gray-scale value of the fused image F at the pixel point (i,j), and M and N are the sizes of the image F. The higher the average, the higher the overall brightness of the image.
μ =$$\frac{1}{M×N}\sum _{i=1}^{M}\sum _{j=1}^{N}F\left(i,j\right)$$ (5)
(2) Standard deviation (std)
The standard deviation is obtained indirectly from the mean value, which indicates the dispersion degree the image gray-scale pixel value and the average value. Where F(i,j) is the gray-scale value of the fused image F at the pixel point (i,j), and M and N are the sizes of the image F; $$\mu \mathrm{i}\mathrm{s}\mathrm{ }\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{ }\mathrm{g}\mathrm{r}\mathrm{a}\mathrm{y}-\mathrm{s}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{ }\mathrm{m}\mathrm{e}\mathrm{a}\mathrm{n}\mathrm{ }\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}$$. The larger the standard deviation , the farther the gradation dispersion, the larger the contrast of the image, and the better the visual effect.
std =$$\sqrt{\frac{1}{M×N}\sum _{i=1}^{M}\sum _{j=1}^{N}\left(F\left(i,j\right)-\mu }\right)$$ (6)
The average gradient reflects the average grayscale change rate of the image, i.e. the sharpness. F(i,j) is the gray-scale value of the fused image F at the pixel (i,j), and M and N are the sizes of the image F; their values represent the small detail contrast and the texture change feature in the fused image. In the fused image, the larger the average gradient, the higher the image sharpness.
g=$$\frac{1}{M×N}\sum _{i=1}^{M}\sum _{j=1}^{N}\sqrt{\frac{\left(\left(\frac{\partial F\left(i,j\right)}{\partial i}\right)²+\left(\frac{\partial F\left(i,j\right)}{\partial j}\right)²\right)}{2}}$$ (7)
(4) Deviation index (dc)
The spectral distortion directly reflects the degree of distortion of the fused image to the original spectral image. The value indicates the difference and matching degree of pixel gray value between the fused image and the original multi-spectral image. Where F(i,j) is the gray-scale value of the fused image F at the pixel point (i,j), and M and N are the sizes of the image F; A(i,j) represents the gray-scale value of the original multi-spectral image at the pixel point; the larger the deviation index, the more the image distortion.
dc=$$\frac{1}{M×N}\sum _{i=1}^{M}\sum _{j=1}^{N}\frac{|F\left(i,j\right)-\mathrm{A}\left(i,j\right)|}{\mathrm{A}\left(i,j\right)}$$ (8)
(5) Correlation coefficient (cc)
The correlation coefficient reflects the correlation degree of the spectral features between the fused image and the source image, and the ability to maintain the spectral information of the fused image. Where F(i,j) is the gray-scale value of the fused image F at the pixel point (i,j), and M and N are the sizes of the image F; A(i,j) represents the gray-scale value of the original multi-spectral image at the pixel point as well as the gray-scale values that represent the fused image and the source image respectively. $$\mu F\mu A$$ The larger the correlation coefficient, the more information the fused image gets from the source image, and the better the fusion effect.
cc=$$\frac{\sum _{i=1}^{M}\sum _{j=1}^{N}\left(F\left(i,j\right)-\mu F\right)\left(A\left(i,j\right)-\mu A\right)}{\sum _{i=1}^{M}\sum _{j=1}^{N}\left(F\left(i,j\right)-\mu F\right)²\left(A\left(i,j\right)-\mu A\right)²}$$ (9)
(6) Image information volume ()
The entropy value of the image reflects the richness of the image information. The cross entropy (ce) is used to measure the difference in the gray distribution of the two images A and F. For a single image, the gray-scale value of each pixel is independent of each other, then the image gray distribution P=｛P0,P1,…Pi,…Pn ｝; Pi represents the probability that the image pixel gray value i, i.e. the ratio of the pixel with the gray value i to the total pixel of the image. l is the total gray level of the image, and , indicates the probability that the gray level of the two image pixels is i. The smaller the cross entropy, the smaller the difference between the gray-scale distribution of the fused image and the source image, that is, the more source image information the fused image contains, the better the fusion effect.
(10)
3.   Data Sample Description
This dataset is divided into orthophoto framing data, orthophoto framing index data and ASTER DEM data from the China-Pakistan Economic Corridor (Kashi to Islamabad). The orthophoto resolution is 2 m (partly 1 m), TIFF format, and the framing index data are saved in SHP format. The coordinate system of all data is WGS1984. The local data results are shown in Fig. 3.

Fig. 3   Partial orthophoto dataset of the China-Pakistan Economic Corridor (Kashgar to Islamabad)
4.   Data Quality Control and Evaluation
4. 1 Orthorectification Accuracy Evaluation
This dataset constructs a rational function model based on the RPC parameters. In the orthorectification process, the optimized ASTER GDEM is used for the first correction; it delineates and masks the boundary of the abnormal deformation area. The mask area is visually edited by two-dimensional DEM one by one, with elevation smoothing. Finally, the corrected DEM is further imported into the orthorectification module to ensure the orthorectification accuracy.
Taking the sample image of the Gaihe Valley in the study area (imaging time 2016) as an example, the orthorectification accuracy analysis is performed from the image pixel error. 25 checkpoints are evenly selected on the target image and the reference image, wherein the reference image is derived from level-16 base image of the Map World with the spatial resolution of 1.88 m. Based on the reference image, the maximum error in the X direction of the target image checkpoint is 0.35 pixels, and the maximum error in the Y direction is 0.4 pixels; the root mean square error in the X direction is 0.42 pixels, and that in the Y direction is 0.38 pixels.
It performs the absolute positioning accuracy error analysis from the ground measured point and the ground object target of the target image target. The checking points of the actual measurement are derived from the control points that were actually measured via differential GPS during the scientific survey along the China-Pakistan Economic Corridor in 2017. The control point distribution is shown in Fig. 4. As the research area is located in the depopulated zone where the Pamirs and Karakorum Mountains meet, the measured control points are laid on the marked roadbed on both sides of the China-Pakistan Highway. There are 7 sample sections with the resolution based on the absolute positioning error and the corresponding marker points in the image. The average mean square error of the image marker points is statistically analyzed. The results show that the mean square error of the single-point positioning is 3.36 m at the maximum and the average mean square error is 1.18 m.

Fig. 4   Ground checkpoint distribution
4. 2 Evaluation of Image Fusion
In terms of the qualitative evaluation of fusion effects, the Brovey method entails more color distortion; the GS method has a poor definition; the PanSharpening method and the PCA method have a better overall effect, but the PanSharpening method has a fine texture, and the color and spectrum information are better. The high-resolution image uses the PanSharpening method to achieve a better fusion effect, as shown in Fig. 5.

(a) PanSharpening fusion

(b) Brovey fusion

(c) GS fusion

(d) PCA fusion

Figure 5   Effect of four fusion methods
The quantitative evaluation index of fusion effect is mainly directed to the four bands of blue, green, red and near-infrared, and the four fusion methods are quantitatively counted respectively. The results are shown in Table 2.
Table 2   Evaluation of fusion effect
 Fusion methods Wave band μ std g dc cc Brovey Blue 0. 0561 0. 0382 2. 4011 0. 6231 0. 3715 10. 6124 Green 0. 0440 0. 0300 1. 9015 0. 9760 0. 6630 4. 0227 Red 0. 0333 0. 0226 4. 0919 0. 5948 0. 4017 5. 4216 NIR 0. 0747 0. 0589 2. 2800 0. 3190 0. 7173 5. 9013 GS Blue 0. 07136 0. 0503 1. 8762 0. 6176 0. 4282 2. 1786 Green 0. 0940 0. 0656 1. 803 0. 4761 0. 3132 3. 7913 Red 0. 1203 0. 0848 5. 8418 0. 8074 0. 3128 4. 5201 NIR 0. 1863 0. 1309 4. 0825 0. 7014 0. 5710 5. 9514 PCA Blue 0. 0714 0. 0509 4. 1137 0. 1691 0. 5179 2. 1947 Green 0. 0940 0. 0662 3. 9371 0. 2285 0. 7060 4. 7489 Red 0. 1204 0. 0858 6. 4918 0. 2271 0. 5710 4. 4702 NIR 0. 1864 0. 1287 3. 9272 0. 3384 0. 6257 3. 7513 PanSharpenig Blue 0. 0725 0. 0515 4. 2794 0. 1541 0. 6248 3. 3985 Green 0. 0955 0. 0669 3. 8371 0. 2080 0. 7136 2. 9453 Red 0. 1222 0. 0867 8. 2592 0. 3536 0. 5981 4. 3971 NIR 0. 1892 0. 1297 4. 1293 0. 3227 0. 6929 2. 5296
The result of Brovey changes is that mean value, standard deviation, and average gradient values are low; the image quality is poor, and image details are not clear. Although the content of image information and correlation coefficient are comparable to those of other methods, the deviation index is too high and the spectral distortion is more serious.
The result of GS changes is that the mean value and standard deviation are equivalent to those of PCA and PanSharpening methods, but the average gradient is lower; in addition, there are more mixed pixels, and the ground object resolving ability is worse; the deviation index value is too high, and the spectral distortion is more severe.
The result of PCA changes is that the mean value, standard deviation, and average gradient values are low; the image quality is better. Although it is slightly lower than that of PanSharpening method, it is more advantageous than those of the Brovey and GS methods. In addition, the deviation index is lower, the spectral distortion is smaller, and the information content cannot be kept rich.
The result of PanSharpening changes is that the mean value, standard deviation, and average gradient values are comparable to those of the PCA method, but with better image quality, it is more advantageous than that of the Brovey and GA method. The spectral deviation index value is smaller as a whole, and the spectral information is less distorted. The correlation coefficient value is larger as a whole, and the retention capability of fusion information is strong. The image information entropy is smaller than those of other methods; the fusion effect is fine and smooth, and the information content is rich.
Therefore, from the experimental results, the PanSharpening method is more suitable for high-resolution image fusion processing in this region.
4.3   Evaluation of Mosaic Edge Joining
The image mosaic adopts the mean value as the raster cell value, and the multi-view image is seamlessly spliced in line with the inlaid line, mainly including calculating the contour line, color correction, image feathering/harmonization, edge-joining and other processing steps. The precision of the mosaic edge is shown in Table 3. The color transition at the edge of the image joining zone is natural, and the ground is reasonably connected; there is no ghost phenomenon, and the texture is clear. The effect is shown in Fig. 4.
Table 3   Evaluation of image mosaic edge joining accuracy
 Serial number Type Content 1 Average error/pixel 1.24 2 Standard deviation/pixel 1.13 3 Whether the edge joining is clear or not Clear 4 Whether the texture is clear or not Clear 5 Difference of chromatic aberration Natural transition
5.   Data Value
The China-Pakistan Economic Corridor mainly passes through permafrost, glaciers and snow-covered areas. With harsh climate, the region is sparsely populated. It is short of basic image data and there is scarce image data of domestic meter-level resolution. High-resolution DOM data are urgently needed for the survey of glaciers, snow, frozen soil and other special environments in the region.
Based on the domestic GF-1 and GF-2 remote sensing data, this paper uses the processing software of remote sensing images to carry out the collection of high-resolution DOM data in the China-Pakistan Economic Corridor. For the complex terrain effects in orthorectification, the RPC rational function model is used for correction. For the fusion of full-color and multi-spectral images, the PanSharpening tool is used, and compared with Brovey, GS, PCA, etc. for further analysis. Half pixel correction accuracy of the orthophoto is finally realized with the data set of clear texture and small spectral loss.
The orthophoto data of the China-Pakistan Economic Corridor are an important basic data for site selection, design and planning along the corridor. It can also be used as the background data for corridor resource surveys in the context of global climate change. Based on the orthophoto data and combined with the comprehensive analysis of climate, hydrology and ecology, it is of great significance for the long-term safe operation of the project and the sustainable development of the ecological environment.
6.   Data Usage and Recommendations
The orthophoto dataset of the China-Pakistan Economic Corridor (Kashgar to Islamabad) is saved in a raster TIF format. Commonly used GIS and remote sensing software such as ArcGIS, QGIS, ENVI and ERDAS can support the reading and manipulation of this data.
Acknowledgments
Thanks to the high-resolution image data supplied by Gansu Data and Application Center for High-resolution Earth Observation System, the ASTER GDEM V2 altitude data supplied by NASA and METI, and the reference data supplied by the US DigitalGlobe Commercial Satellite and IKONOS Commercial Satellite.
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Data citation
1. Han Liqing, Zhang Yaonan, Tian Deyu, et al. A High-resolution DOM Dataset of the China-Pakistan Economic Corridor (Kashgar to Islamabad) [DB/OL]. Service Center, National Special Environment and Function of Observation and Research Stations Shared Service Platform, 2018. (2018-03-21). DOI: 10.12072/casnw.048.2018.db.
Article and author information
Han L, Zhang Y, Tian D, et al. A High-resolution DOM Dataset of the China-Pakistan Economic Corridor (Kashgar to Islamabad) [J/OL]. China Scientific Data, 2019, 4(3). DOI: 10.11922/csdata.2018.0052.zh.
Han Liqin
Contribution: high-reference image processing and terrain correction
male, from Fan County, Henan Province, doctoral candidate, majoring in quantitative remote sensing and remote sensing geological disaster monitoring
Zhao Yaonan
Contribution: Data processing flow design
yaonan@lzb.ac.cn
male, from Tianshui City, Gansu Province, doctoral candidate, researcher, majoring in Geoscience big data.
Tian Deyu
Contribution: Data batch processing
male, from Siziwang Banner, Inner Mongolia, postgraduate, majoring in remote sensing application in cold and arid areas.
Kang Jianfang
Contribution: Data quality control and management
female, from Qin’an County, Gansu Province, master, engineer, majoring in big data application in cold and arid regions.
National Science & Technology Infrastructure Center Platform (Y719H71006), Chinese Academy of Sciences Informationization Special (XXH13506), Gansu Provincial University Science and Technology Transformation Project (2017D-27).
Publication records
Published: July 18, 2019 （ VersionsEN1
Updated: July 18, 2019 （ VersionsEN3
Released: Oct. 25, 2018 （ VersionsZH2
Published: July 18, 2019 （ VersionsZH3
References

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