Round 1

Reviewer 1 Report

This work is an endeavour to utilise multiple open-source data such as Advanced Land Observing Satellite World 3D-30m DSM, OpenStreetMap and FABDEM to extract large-scale 3D road networks. It will benefits the research related to smart city reconstruction and 3D urban analysis. 

The comments are as the following.

(1) In Figure 2, the first section is utilizing topological features and road attribute information to simplify the OSM road nodes, ”Thus, on the basis of the potential influencing factors and the principle of vertical road planning, we simplify the topology of the entire network by removing nonintersecting points or dead ends (Figure 2b) ” , but how to simplify the road nodes need a more detailed description. 

(2) In “3.2. Correction”, terrain correction, tunnel correction and grade correction are conducted to minimise the cases in which errors exist in data. But as an important part of road network, the bridges over rivers were unable to be reconstructed in this paper, they are not ignored, although they are more complex and difficult to correction.

(3) Line 375, Figure 6 or Figure 8 ?

(4) In this paper, three cities (Nanjing, Aalborg and Los Angeles) are selected for validation. The authors believe these cities have different levels of population and diverse patterns of road networks. I suggest Chongqing of China as a selected data for validation. Chongqing have typical road network spatial pattern and rough mountain areas.

Author Response

Response to Reviewer 1 Comments

Thank you for taking the time to review our paper and appreciate these valuable comments. Below are our responses to your comments. We hope the changes we made and the responses we provided satisfy the concerns. Thank you again for your time in reviewing our submission.

Point 1: In Figure 2, the first section is utilizing topological features and road attribute information to simplify the OSM road nodes, ”Thus, on the basis of the potential influencing factors and the principle of vertical road planning, we simplify the topology of the entire network by removing nonintersecting points or dead ends (Figure 2b) ” , but how to simplify the road nodes need a more detailed description.

Response 1: Thanks for this valuable comment. We have added details in the second paragraph of section 3.1. In graph theory, the degree indicates the number of edges to which each node is connected. By converting the road networks to undirected graph, we use this index to filter out the intersections in the road network.

‘By converting the road networks to undirected graph, the degrees of nodes are calculated. And the nodes where degree greater than 3 are considered intersections and the others are filtered. To accomplish this graph-related task, we import the NetworkX package into our python program.’, please see Lines 205-208

Point 2: In “3.2. Correction”, terrain correction, tunnel correction and grade correction are conducted to minimise the cases in which errors exist in data. But as an important part of the road network, the bridges over rivers were unable to be reconstructed in this paper, they are not ignored, although they are more complex and difficult to correct.

Response 2: 

We acknowledged a little bias in our method estimating of the height of bridges. However, the elevation information is lacking in the bridges over the river, since AW3D30 DSM was resampled and upscaled by high spatial resolution data and the elevation of bridges is smoothed. If we use high spatial resolution elevation, like LiDAR, we could solve this problem. 

By our method, some small-size bridges have been reconstructed but the bridges with approach spans have bias. In our pervious test, we used the spatial relationship between rivers and bridges to reserve the sample nodes over the bridges, but it cost a great deal of time in Nanjing, a study area with a dense network of rivers and don’t improve the accuracy significantly. Hence, we don’t add this step to our method and are exploring a novel method to deal with this issue that can be applied to most cases. 

Point 3: Line 375, Figure 6 or Figure 8 ?

Response 3: Thanks for your kind reminder, this suggestion has been fixed in the new manuscript.

Point 4: In this paper, three cities (Nanjing, Aalborg and Los Angeles) are selected for validation. The authors believe these cities have different levels of population and diverse patterns of road networks. I suggest Chongqing of China as a selected data for validation. Chongqing has typical road network spatial pattern and rough mountain areas.

Response 4: 

        Since our elevation data are open source and have a spatial resolution of about 30m, the details of the urban interior are smoothed and generalized, so the raw data do not have high reliability in a complex and rugged urban environment like Chongqing. 

        As we mentioned in the discussion, most cities are located in the plains, especially mega-cities, but Chongqing is a special case, which is very unique in the world. Our method has good performance in plain areas, though it has the potential for poor performance in mountainous areas. Chongqing, a mega-city with lots of skyscrapers and multi-layer interchanges, and the original AW3D30 DSM and FABDEM have some biases which affect our results. Specifically, the location where the elevation of roads is greater than surrounding buildings is a challenging issue.

        Our study aims to provide an approach to extracting large-scale 3D road networks for most areas of the world. But if we have a high-resolution DSM by UAVs or LiDAR, we believe that we could obtain 3D road networks for Chongqing.

        In the future, we planned to integrate other terrain derivatives, such as slope, aspect and slope length, into the constraints to cope with this problem. We hope to provide a global 3D road network dataset including Chongqing.

Author Response File: Author Response.docx

Reviewer 2 Report

1. General comments

In the article I miss a comparative analysis of the height data taken from the FABDEM system about road design standards (different in the USA, Denmark, and China), so I cannot assess the correctness or effectiveness of the proposed method.

In general, however, it is worth reading the article that discusses the research problem regarding the automatic approach to extracting large-scale three-dimensional road networks with open-source data. It is a pity that the public source code proposed by the authors for the proposed method is not yet available, which would be valuable for future readers of this article.

2. Listed suggestions

In the reviewer's opinion, some drawings are difficult to read and should be corrected.

Generally, all figures lack information about geographical areas and no references to programs visualizing research results.

E.g.

Fig. 1 - should be given a reference source and information about the geographic area it concerns.

 Fig. 3 - no explanatory legend

Fig. 4 What is this city (country)?

Fig. 5 b What program was used to visualize the images?

3. Evaluation of paper

The overall structure, presentation, and methodology are fine but see the comments mentioned above for suggested fixes. The title and subject are within the scope of the journal. Sometimes the method is not well explained, so a reader who is not a remote sensing specialist would have difficulty repeating this study.

Author Response

Response to Reviewer 2 Comments

Thank you for taking the time to review our paper and appreciate these valuable comments. Below are our responses to your comments. We hope the changes we made and the responses we provided satisfy the concerns. Thank you again for your time in reviewing our submission. The fixed figures are in Word attachment.

Point 1: It is a pity that the public source code proposed by the authors for the proposed method is not yet available, which would be valuable for future readers of this article.

Response 1: We strongly agree with your suggestion and provide the GitHub link to readers in the conclusion of our manuscript now. In GitHub, we upload a Jupyter Notebook as an interactive python program and have commented in English for users. Our source code relies on famous Python packages, such as NetworkX, OSMNx and GeoPandas which can be installed easily via anaconda or pip for programming beginners. In the source code, users can modify the tunnel height and grade limitation for local cases. Our GitHub link is https://github.com/CubicsYang/Road_Elevation_DSM.

Point 2: Fig. 1 - should be given a reference source and information about the geographic area it concerns

Response 2: Fig.1 was fixed by adding longitude and latitude information. Meanwhile, we indicated the place name in the figure title.

[This fixed figure are in Word attachment.]

Figure 1. Comparison of the hillshade effect of SRTM(b) and FABDEM(c) with reference to (a) Remote sensing images in the central area of Nanjing

Point 3: Fig. 3 - no explanatory legend

[This fixed figure are in Word attachment.]

Response 3: Thanks for your suggestion. We added an explanatory legend in Fig.3.

Point 4: Fig. 4 What is this city (country)?

Response 4: Thanks for your comment. This is the central area of Nanjing, a study area. We add a place name in the title of Fig. 4 which is ‘Comparison of simplified nodes and original nodes in the central area of Nanjing’ in Line 222.

Point 5: Fig. 5 b What program was used to visualize the images?

Response 5: Fig. 5 b was visualized by ArcGIS Pro, a professional geospatial software by Esri. We used the Raster Calculator to obtain the result of AW3D30 DSM minus FABDEM which was reclassified into four categories.

Point 6: Sometimes the method is not well explained, so a reader who is not a remote sensing specialist would have difficulty repeating this study.

Response 6:

To make our approach easier to understand, we added the details about our method in sections 3.1(Simplification), 3.2.2 (Tunnel correction)and 4.2(Topology assessment of road network).

• In section 3.1, we added the implementation details about simplification in our software, which is helpful to understand the process of extracting interchanges.

        ’ By converting the road networks to undirected graph, the degrees of nodes are calculated. And the nodes where degree greater than 3 are considered as intersections and the others are filtered. To accomplish this graph-related tasks, we import NetworkX[54] package into our python program.’ Please see Lines 205-208.

        ’After simplification, the lower roads in the interchanges only reserved two nodes which are accurate in the original DSM. Thus, these roads which are floating in the air originally are corrected to the lower location of the interchanges.’ Please see Lines 214-217.

• In Section 3.2.2, we added some content that argues the choice of the tunnel height at 4.5 m, completes the detailed process of tunnel corrections and made a new figure for the detailed tunnel correction process.

 ‘Considering the regional variability of this criterion and the global suitability of our approach, we combine the two standards and calculate the mean tunnel height. To facilitate the calculation, the tunnel height was set to 4.5 m generally. In our software, users could modify this variant for local urban cases and standards.’ Please see Lines 293-296

‘Figure 6 shows the detailed process for tunnel correction process.’ Please see Line 299

[This fixed figure are in Word attachment.]

Figure 6. The detailed process for the tunnel correction process

• In Section 4.2, we added more description about the topology assessment of the road network in our visual interpretation.

‘We also sample three interchanges of different scales and types in the three cities to check the correctness of their 3D topology (Figure 8). The types include cloverleaf, mixed and parclo. And the radii of the three 3D traffic projects are about 500, 200 and 100 m. We use remote sensing images and 3D visualization of the road network for comparison and validation. The three sample areas are located in a suburban area, a sub-urban junction and an inner-city area, which makes our assessment representative.

The topology assessment is divided into two parts: the up-down relationship between the major roads and the up-down interconnection relationship of the approach roads. By visual interpretation, we find that the results have impressive performance on multiple radius scales. Specifically, the lower roads of interchanges are all in the correct spatial location that they are below the upper roads, rather than intersecting originally. Because the nodes between the main road and ramps were also preserved in the previous simplification steps, these nodes are corrected by multiple steps and the ramps link the upper and lower roads in the interchanges, with the changes of end-nodes elevation.

On the whole, the topological relationships between the upper and lower levels of the main roads and the upper and lower interconnections of the approach roads are correct, though some roads’ nodes still contain noise and unnecessary undulations.’  Please see Line 378-394

In addition, we provided the GitHub link for users to reduce the study cost, and they could repeat our study in a Jupyter Notebook easily.

Author Response File: Author Response.docx

Reviewer 3 Report

Below are some suggestions and questions regarding the presented article

1. In Figure 2 a, in the method section, you can indicate what data source was used in each of the steps. 

2. In section 3a the authors write about the removal of points, please indicate the method of removing these points. Was it done manually or maybe some software was used for it? (what?)

3. In section 3b the authors write in the use of interpolation to determine the ordinates on the sections between intersections, please indicate how long these sections are, is there any length limit for which such assumptions should not be used?

4. In section 3b ii, please argue the choice of the tunnel height at 4.5 m

5. In section 3b iii, please expand the information on expert knowledge. Does it come from literature, authors' research, own experience, interviews or others?

6. In section 4a there is a repetition from the information in section 3c. Recommends that the information on the description of selected research points be transferred from sections 3c to 4a.

7. Do the authors plan to continue working on this topic? Please indicate possible further research directions.

Author Response

Response to Reviewer 3 Comments

Thank you for taking the time to review our paper and appreciate these valuable comments. Below are our responses to your comments. We hope the changes we made and the responses we provided satisfy the concerns. Thank you again for your time in reviewing our submission. The fixed figures are in the Word attachment.

Point 1: In Figure 2 a, in the method section, you can indicate what data source was used in each of the steps. 

Response 1: Figure 2 has been fixed in the edited manuscipt.

[This fixed figures are in the Word attachment.]

Figure 2. (a) Workflow diagram for extracting 3D road networks, (b) The detailed process of simplification, (c) The detailed process for terrain correction

Point 2: In section 3a the authors write about the removal of points, please indicate the method of removing these points. Was it done manually or maybe some software was used for it? (what?)

Response 2: Thanks for this valuable comment. We have added some details of simplification in the second paragraph of section 3.1. ‘By converting the road networks to an undirected graph, the degrees of nodes are calculated. And the nodes where a degree greater than 3 are considered intersections and the others are filtered. To accomplish this graph-related task, we import the NetworkX package into our python program.’, please see Line 203-206

Point 3: In section 3b the authors write about the use of interpolation to determine the ordinates on the sections between intersections, please indicate how long these sections are, is there any length limit for which such assumptions should not be used?

Response 3: In this step, we used linear interpolation to get the elevation of other locations between two intersections but not limit the length of sections. After simplification, we statistically calculated the average road length which is about 450m in Nanjing of China. According to the principle of vertical road planning, we believe that the changes in elevation of road sections are not significant and the trend is consistent unless the terrain is rugged and hilly. Hence, our interpolation would keep good performance but no length limit. We added the description of the length limit in the Line. ‘Hence, we use interpolation to obtain the elevation of connecting road segments from two adjacent intersections of major roads but not road length limit.’ Please see inLine 229-231.

Point 4: In section 3b ii, please argue the choice of the tunnel height at 4.5 m

Response 4:

Thanks for the suggestion. Due to our study areas distribute across three continents, we calculate the mean tunnel height of their local standards. To facilitate the calculation, we round the tunnel height at 4.5 m. Users could modify this variant for local urban cases and standards in our software.

We added some explanation about the choice of the tunnel height at 4.5 m and made a new figure to explain this correction. ‘Considering the regional variability of this criterion and the global suitability of our approach, we combine the two standards and calculate the mean tunnel height. To facilitate the calculation, the tunnel height was set to 4.5 m generally. In our software, users could modify this variant for local urban cases and standards.’ Please see Lines 293-296

 [This fixed figures are in the Word attachment.]

Figure 6. The detailed process for the tunnel correction process

Point 5: In section 3b iii, please expand the information on expert knowledge. Does it come from literature, authors' research, own experience, interviews or others?

Response 5: Thanks for these valuable comments. In section 3.2.3, we added a citation to explaining that tertiary or higher-level roads are generally distributed in plain or rolling areas. In addition, we expanded the road design standards of China and the USA as expert knowledge to take a maximum grade by the designed speed.

‘According to the requirements of road grades mentioned in the highway design manual from CA (Table 1)[57] and JTG 2112-2021 (Table 2), the grade standards relate to the road design speed and terrain situations. Our method mainly focuses on tertiary and high-er-level roads. Most cities with tertiary roads are generally distributed in plain or rolling areas[48], which means the roads are not very steep. In addition, according to standards of CA and JTG 2112-2021, the design speed of tertiary roads is between 30-60 km/h and high-level roads have greater designed speed, so we take 0.07 as the maximum grade. ‘(See Line 311-318)

Point 6: In section 4a there is a repetition of the information in section 3c. Recommends that the information on the description of selected research points be transferred from sections 3c to 4a

Response 6: We deleted the repeated information in section 3.3 and transferred the description of selected research points from sections 3.3 to 4.1. Please see Lines 333-376

• Section 3.3 Validation with reference data

'Three cities on three continents, namely, Nanjing of China, Aalborg of Denmark and Los Angeles of the USA, are selected for validation. In the validation process, we use random sampling some nodes to calculate the bias. Root-mean-square error (RMSE) and mean absolute error (MAE) are widely applied to measure deviations between predicted and reference values, which are also used to evaluate the model performance by following equations.

As a supplement, we further combine remote sensing images to sample three classical interchanges to check the 3D topological correctness. In the topology assessment of road network, we plan to verify the relative location of ramps and main roads in the interchanges by visual interpretation.'

• Section 4.1 Accuracy assessment of road network elevation

'Three sample areas distributed across three continents with different data sources are used to evaluate the accuracy of the 3D road network elevations. These three cities have different levels of population and diverse patterns of road networks. Based on previous classifications, their road network patterns are mixed, sphere and grid respectively[58], which make our validation more representative. A high-resolution DSM has been processed in our previous work, and the spatial resolution is about 0.03 m in Nanjing from UAVs. Considering that these high-resolution data still contain much noise, including trees and street lamps, the DSM data was repaired by comparing them with remote sensing images. For Aalborg, we refer to the LiDAR data published by Aarhus University, which have a nominal accuracy of plus or minus 20 cm[20]. Google Earth is known to provide accurate 3D models and elevation data in most parts of the world, so our study samples 200 points in Los Angeles to compare and verify with it. These areas comprise tunnels, interchanges and other 3D traffic projects, which are very representative in complex urban environment.'

Point 7: Do the authors plan to continue working on this topic? Please indicate possible further research directions.

Response 7:

We are very interested in this topic of 3D road network. In the future, we plan to provide a dataset about 3D road networks worldwide. Our 3D road network could be applied in various relevant study, e.g., urban planning, transportation and landscape. 

As an indicator, the height difference of 3D road networks can be integrated with 2D topological factors to identify the urban functional zones[1], reveal the urban expansion phase[2] and indicate the spatial intercity and intracity heterogeneity[3];

As databases, 3D road networks can be applied to estimate the solar and wind energy potential[4,5], simulate the process of the urban flood[6] and calculate the impact range of urban noise field[7] from a vertical perspective;

As a kind of unique urban facility, 3D road networks enrich the urban landscape[8], promote the quantitative study of urban morphology[9] and provide auxiliary information for urban landscape design and urban planning.

Our group is using this data to quantify urban 3D morphology, calculate urban open space, and other related research. If you have some relevant ideas about these, we are very welcome to cooperate and communicate with your group.

1. Liu, H.; Xu, Y.; Tang, J.; Deng, M.; Huang, J.; Yang, W.; Wu, F. Recognizing Urban Functional Zones by a Hierarchical Fusion Method Considering Landscape Features and Human Activities. Trans. GIS 2020, 24, 1359–1381.
2. Burghardt, K.; Uhl, J.H.; Lerman, K.; Leyk, S. Road Network Evolution in the Urban and Rural United States since 1900. Comput. Environ. Urban Syst. 2022, 95, 101803, doi:10.1016/j.compenvurbsys.2022.101803.
3. Xue, J.; Jiang, N.; Liang, S.; Pang, Q.; Yabe, T.; Ukkusuri, S.V.; Ma, J. Quantifying the Spatial Homogeneity of Urban Road Networks via Graph Neural Networks. Nat. Mach. Intell. 2022, 4, 246–257, doi:10.1038/s42256-022-00462-y.
4. Zhong, T.; Zhang, K.; Chen, M.; Wang, Y.; Zhu, R.; Zhang, Z.; Zhou, Z.; Qian, Z.; Lv, G.; Yan, J. Assessment of Solar Photovoltaic Potentials on Urban Noise Barriers Using Street-View Imagery. Renew. Energy 2021, 168, 181–194, doi:10.1016/j.renene.2020.12.044.
5. Zahedi, R.; Ghorbani, M.; Daneshgar, S.; Gitifar, S.; Qezelbigloo, S. Potential Measurement of Iran’s Western Regional Wind Energy Using GIS. J. Clean. Prod. 2022, 330, 129883, doi:10.1016/j.jclepro.2021.129883.
6. Singh, P.; Sinha, V.S.P.; Vijhani, A.; Pahuja, N. Vulnerability Assessment of Urban Road Network from Urban Flood. Int. J. Disaster Risk Reduct. 2018, 28, 237–250, doi:10.1016/j.ijdrr.2018.03.017.
7. Rey Gozalo, G.; Suárez, E.; Montenegro, A.L.; Arenas, J.P.; Barrigón Morillas, J.M.; Montes González, D. Noise Estimation Using Road and Urban Features. Sustainability 2020, 12, 9217, doi:10.3390/su12219217.
8. Zeng, L.; Lu, J.; Li, W.; Li, Y. A Fast Approach for Large-Scale Sky View Factor Estimation Using Street View Images. Build. Environ. 2018, 135, 74–84, doi:10.1016/j.buildenv.2018.03.009.
9. Huang, X.; Wang, Y. Investigating the Effects of 3D Urban Morphology on the Surface Urban Heat Island Effect in Urban Functional Zones by Using High-Resolution Remote Sensing Data: A Case Study of Wuhan, Central China. ISPRS J. Photogramm. Remote Sens. 2019, 152, 119–131, doi:10.1016/j.isprsjprs.2019.04.010.

Author Response File: Author Response.docx

Reviewer 4 Report

This paper proposed a novel approach to extract large-scale 3D road networks, integrating terrain correction and road engineering rule constraint, by using Advanced Land Observing Satellite World 3D DSM, OpenStreetMap and FABDEM.  The paper is well written , however, the problem analysis in several parts of the paper should be enhanced (following the question order why, what and how).

1. How to extract the height of interchanges, and tunnels is not cleaning in the paper, and it must be detail described in the paragraph 3.2.2 and paragraph 4.2.

2. Google Earth elevation data which is used to compare and verify is not necessarily appropriate in Los Angeles，more accurately reference data should be used in other place.

Author Response

Response to Reviewer 4 Comments

Thank you for taking the time to review our paper and appreciate these valuable comments. Below are our responses to your comments. We hope the changes we made and the responses we provided satisfy the concerns. Thank you again for your time in reviewing our submission. The fixed figures are in the word attachment.

Point 1: How to extract the height of interchanges, and tunnels is not cleaning in the paper, and it must be detail described in the paragraph 3.2.2 and paragraph 4.2.

Response 1: Thanks for these valuable comments. In order to understand the detailed process easily, we added more description in section 3.1(Simplification), 3.2.2 (Tunnel correction)and 4.2(Topology assessment of road network).

• In section 3.1, we added the implement details about simplification in our software, which is helpful to understand the process of extracting interchanges.

        ’ By converting the road networks to undirected graph, the degrees of nodes are calculated. And the nodes where degree greater than 3 are considered as intersections and the others are filtered. To accomplish this graph-related task, we import NetworkX[54] package into our python program.’ Please see Lines 205-208.

        ’After simplification, the lower roads in the interchanges only reserved two nodes which are accurate in the original DSM. Thus, these roads which are floating in the air originally are corrected to the lower location of the interchanges.’ Please see Lines 214-217.

• In Section 3.2.2, we added some content that argues the choice of the tunnel height at 4.5 m, completes the detailed process of tunnel corrections and made a new figure for the detailed tunnel correction process.

 ‘Considering the regional variability of this criterion and the global suitability of our approach, we combine the two standards and calculate the mean tunnel height. To facilitate the calculation, the tunnel height was set to 4.5 m generally. In our software, users could modify this variant for local urban cases and standards.’ Please see Lines 293-296

‘Figure 6 shows the detailed process for tunnel correction process.’ Please see Line 299

[This fixed figure is in the word attachment.]

Figure 6. The detailed process for the tunnel correction process

• In Section 4.2, we added more description about the topology assessment of the road network in our visual interpretation.

'We also sample three interchanges of different scales and types in the three cities to check the correctness of their 3D topology (Figure 8). The types include cloverleaf, mixed and parclo. And the radii of the three 3D traffic projects are about 500, 200 and 100 m. We use remote sensing images and 3D visualization of the road network for comparison and validation. The three sample areas are located in a suburban area, a sub-urban junction and an inner-city area, which makes our assessment representative.

The topology assessment is divided into two parts: the up-down relationship between the major roads and the up-down interconnection relationship of the approach roads. By visual interpretation, we find that the results have impressive performance on multiple radius scales. Specifically, the lower roads of interchanges are all in the correct spatial location that they are below the upper roads, rather than intersecting originally. Because the nodes between the main road and ramps were also preserved in the previous simplification steps, these nodes are corrected by multiple steps and the ramps link the upper and lower roads in the interchanges, with the changes of end-nodes elevation.

On the whole, the topological relationships between the upper and lower levels of the main roads and the upper and lower interconnections of the approach roads are correct, though some roads’ nodes still contain noise and unnecessary undulations.'

Please see Line 378-394

Point 2: Google Earth elevation data which is used to compare and verify is not necessarily appropriate in Los Angeles， more accurately reference data should be used in other place.

Response 2:  In the other study area, we selected more accurate reference data to assess our accuracy and obtained a good performance, so we chose the most popular 3D view map software, Google Earth, as our reference data source in Los Angeles. Though the reference accuracy of Google Earth is lower than point clouds from LiDAR and high-resolution DSM by UAVs, it has wide coverage than other reference data sources and has accurate 3D models in Los Angeles according to our visual interpretation. In addition, we found the structures of tunnels and interchanges are represented well in Google Earth and some papers have assessed the accuracy of elevation in Google Earth and have good reviews [1–3].

1. Wang, Y.; Zou, Y.; Henrickson, K.; Wang, Y.; Tang, J.; Park, B.-J. Google Earth Elevation Data Extraction and Accuracy Assessment for Transportation Applications. PLOS ONE 2017, 12, e0175756, doi:10.1371/journal.pone.0175756.
2. Schpok, J. Geometric Overpass Extraction from Vector Road Data and DSMs. In Proceedings of the Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems; Association for Computing Machinery: New York, NY, USA, November 1 2011; pp. 3–8.
3. Mohammed, N.Z.; Ghazi, A.; Mustafa, H.E. Positional Accuracy Testing of Google Earth. Int. J. Multidiscip. Sci. Eng. 2013, 4, 6–9.

Author Response File: Author Response.docx

Round 2

Reviewer 4 Report

I have no more comments, thanks for the author's revision

Author Response

Thanks for your thoughtful suggestions to improve the quality of this paper