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Peer-Review Record

CA-BASNet: A Building Extraction Network in High Spatial Resolution Remote Sensing Images

Sustainability 2022, 14(18), 11633; https://doi.org/10.3390/su141811633
by Liang Huang 1,2, Juanjuan Zhu 1,*, Mulan Qiu 1, Xiaoxiang Li 1 and Shasha Zhu 1
Reviewer 1:
Reviewer 2: Anonymous
Sustainability 2022, 14(18), 11633; https://doi.org/10.3390/su141811633
Submission received: 19 August 2022 / Revised: 13 September 2022 / Accepted: 14 September 2022 / Published: 16 September 2022
(This article belongs to the Special Issue Intelligent GIS Application for Spatial Data Analysis)

Round 1

Reviewer 1 Report

This is a very technically oriented manuscript, which is technically innovative. However, the following points can be further improved:

1. This paper focuses on evaluating the innovation of the technology, however, there is a lack of economic and social effects of this technology. It is recommended to add relevant content.

2.In the 4. Discussion section, further expansion is required. It is suggested to discuss from two aspects, namely theoretical implications and practical implications.

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

In this manuscript, the authors introduce a DL-based network for building extraction from high spatial resolution remote sensing data.

In my opinion, this manuscript is, generally, well written and organized. It is also well argued and the presented results are convincing.

I recommend its publication after a minor suggestion: In my opinion, all results discussions and analyzes should be moved to the Discussion section rather than leaving them in the Experimental and analysis one.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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