Extraction of City Roads Using Luojia 1-01 Nighttime Light Data

Round 1

Reviewer 1 Report

Its already a nicely written paper. However, the authors should do a thorough revision to improve the consistence of reading the manuscript. I have included an annotated PDF where you could find more comments. 

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

The study includes many good ideas and is done technically well.

The paper however is written in a hurry and needs careful spell and grammar checking to improve readability.

line 39: researches -> studies

line 44: heve -> have

line 53: "Many literatures" reads strange -> "Many studies"

line 56/57 please fix the sentences

line 77: its uncommen to first bring acronym and then in brackets explain it

line 94-96: fix the citations - for example CO2 diffusion - you mean fossil fuel combustion

line 98/99 "light source was mainly lamplight illume of human beings," this needs to be fixed

table 1 is wrong: µm is nm (!) - also it is "swath width" not just with 

line 207 ff - I think the Laplacian operator part is superficial - this is well known

Fig 7 & 9 pleas efix "Optimizing"

table 3: I guess you mean "Precision"

Discussion:

It would be interesting to comment on some published papers using nighttime light data and roads. For example:

Jechow, A.; Hölker, F. Evidence That Reduced Air and Road Traffic Decreased Artificial Night-Time Skyglow during COVID-19 Lockdown in Berlin, Germany. Remote Sens. 2020, 12, 3412. https://doi.org/10.3390/rs12203412 

have investigated impact of COVID-19 lockdown on several issues.

They found that major roads were less used (see Fig 7) during pandemic. They used VIIRS DNB. Probably Luojia would have allowed to study this impact within the city.

 

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 3 Report

The paper deals with the extraction of the roads in urban regions from satellite images. The relatively high-resolution images from Chinese Luojia 1-01 are user. The extraction is based on few steps: firstly, the image is enhanced (sharpened) using Laplace-filtering with elimination of the diffusion, then urban areas are detected using threshold value in the brightness. Afterwards the PCNN is used for pattern recognition and the final step, before road extraction itself, includes optimization methods like clean, dilate, erode and thin. The results of the road extraction in representative parts of Wuhan-city are compared with Open Street Map data.

The subject of the manuscript is up to date, the amount of the satellite imagery is increasing and such automatic methods, as described in the manuscript, can be very useful for evaluation of city development. The main method used for pattern recognition (neural network) is a typical and effective choice.

The paper clearly describes the whole procedure and results, I have only few minor comments.

1. The description of the method for diffusion elimination (eq. (6)) should clearly state if it is developed by authors or found in the literature.

2. As can be seen in Fig.9, the optimizing-phase has a big impact on the quality of road patterns. However, this phase is described only briefly in sec. 3.4, in contrast to the detailed description of  previous steps. I recommend to include more detailed description of the optimizing methods used.

3. There are many spelling errors in the manuscript, e.g.: "Optimazing" in fig. 3 and fig.9, symbol "theta" in fig. 4 is not found in related text, ... Please, chceck the manuscript carefully for such errors.

 

 

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 4 Report

The article is original and interesting.
The methodology, although complex and articulated, is well illustrated step by step and it is easy to understand.
The introduction is too long, and it would be good to try to summarise it.
The obtained results confirm the goodness of the proposed technique, even if the citated confounding factors should be better explained when using night lights to extract city streets, because they can influence the application of the methodology in particular in cities with different conformations:
- the role of vegetation cover that may mask the street lighting
- the problem of flux reduction used increasingly in public and private lighting at night
- misunderstandings due not only to residential lighting but also to traffic lighting.
- the problem of city centre with high buildings that 'trap' light and therefore make it not always detectable by satellite.

Finally, some minor editorial revisions are required:
- fig. 1 : enlarge right image
- line 473: reference 48 does not exist, it should be 38

 

Author Response

Please see the attachment

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

the manuscript has improved and is acceptable now