Spelling and Grammar Issues

ZZ 45–46

Original: “The codes and dataset … is available.”

Correct: “The code and dataset … are available.”

ZZ 83

Original: “there is a geospatial wind turbine dataset for 2020 is introduced.”

Correct: “A geospatial wind turbine dataset for 2020 was introduced.”

Unclear Description: OSM Query

The query string in line 129 (["generator: source"="wind"]) appears syntactically incorrect.

OpenStreetMap commonly uses:

• generator:source=wind
• or power=generator combined with generator:source=wind

The manuscript should clearly state the exact Overpass query used.

Major Comment: Missing Methodological Detail on OSM Extraction

• The manuscript does not specify whether nodes, ways, or relations were extracted.
• It is unclear whether both power=generator and generator:source=wind were used.
• The handling of offshore turbines is not described.
• No link to the exact Overpass script is provided.
• There is no information on whether wind farms, meteorological masts, or power-line infrastructure were filtered out.
• These omissions significantly limit reproducibility and must be clarified.

Major Comment: Unexplained OSM Error Rate

The manuscript states a “10% error rate in OSM’s global wind turbine dataset” but provides no methodological explanation.

Missing information:

• How was this error rate calculated? What was the validation procedure?
• Was the error rate spatially or regionally variable?
• Were commission and omission errors distinguished?
• Was the result compared to existing studies with likewise approaches, e.g. https://www.mdpi.com/2220-9964/14/6/232

Major Comment: Insufficient Documentation of Random Forest Sampling

Missing details include:

• From which land cover classes were negative samples drawn?
• Was spatial autocorrelation considered?
• How was it ensured that negative samples were not within 30 m of existing turbines?
• Was the global distribution of positive and negative samples balanced?

The Random Forest sampling workflow requires a clear methodological description.

Minor Comment: Sentinel-1/2 Features and Missing GEE Scripts

The manuscript lists processing steps, but details are missing, included:

• Exact satellite collections used.
• Time span and temporal compositing strategy.
• Cloud masking method (e.g., QA60 for Sentinel-2).
• Preprocessing steps such as resampling, mosaicking, and normalisation.

Referencing Zenodo alone is insufficient; the core processing steps must appear in the manuscript.

Overall Assessment

This manuscript presents a 2024 global inventory of 379,595 onshore wind turbines derived through a hybrid workflow integrating OpenStreetMap (OSM), high-resolution Google Earth imagery, Sentinel-1/2 data, deep learning (ResNet-18), and Random Forest classification. The topic is timely and relevant, and the resulting dataset could potentially support renewable energy planning, biodiversity assessments, and land-use analysis at multiple scales.

Despite its potential value, the manuscript raises significant concerns regarding benchmarking against existing global datasets, spatial completeness, false positives, and validation rigor. In its current form, the study does not convincingly demonstrate that the proposed dataset represents a substantial advancement over the most up-to-date global wind turbine inventories. These issues should be carefully addressed before the manuscript can be considered for publication.

Major Comments

The authors state that their dataset represents a tenfold expansion over the 2020 global wind turbine inventory containing 33,514 turbines. While this numerical comparison is technically correct, it does not constitute an appropriate benchmark. The manuscript does not adequately compare the proposed dataset with the Global Renewables Watch dataset (https://github.com/microsoft/global-renewables-watch?tab=readme-ov-file#dataset-download), which already contains 375,197 wind turbines and is publicly available. Given that this resource is both recent and widely recognized, it should serve as the primary point of reference rather than a 2020 dataset.

Furthermore, the Global Renewables Watch dataset includes additional attributes such as construction year, which are absent from the authors’ dataset. This omission weakens the claim of producing a more comprehensive inventory. A direct and systematic comparison with Global Renewables Watch is therefore essential. The authors should quantify the spatial overlap between the two datasets, explain methodological differences, and clarify in what specific ways their dataset improves upon existing products in terms of coverage, accuracy, update frequency, or attribute richness. Without this comparison, the assertion of substantial advancement remains insufficiently supported.

I downloaded both datasets. A visual comparison between the two datasets reveals substantial spatial inconsistencies. A considerable number of turbines present in Global Renewables Watch are missing from the authors’ dataset. Conversely, the authors’ dataset includes many turbines that do not appear in Global Renewables Watch. Among these newly detected turbines, a noticeable proportion appear to be false positives. These discrepancies raise concerns about both omission and commission errors.

The manuscript reports very high classification metrics, but these results appear to be derived primarily from internal validation procedures. Independent cross-dataset validation is necessary for a global infrastructure product of this scale. The authors should quantify omission and commission rates relative to Global Renewables Watch and perform targeted manual verification of turbines that are unique to their dataset. Regional accuracy assessments would also be more informative than reporting only global averages, particularly given the strong geographic heterogeneity in wind turbine siting environments.

An additional concern relates to the acquisition and use of Google Earth imagery for training deep learning models. Google’s terms of service and licensing agreements generally restrict bulk downloading, scraping, and redistribution of imagery, particularly for automated analysis or machine learning training, unless explicit authorization has been obtained. Even if enforcement actions are unlikely, the legal and ethical responsibility remains with the data producers. The fact that similar practices may have appeared in other published studies does not necessarily imply compliance with platform policies. 

More broadly, a truly state-of-the-art global wind turbine dataset should ideally integrate existing authoritative sources rather than operate independently of them. A stronger contribution would involve building upon Global Renewables Watch as a baseline and incorporating newly detected turbines identified by the proposed workflow. Preserving useful attributes such as construction year and land cover would substantially increase scientific value. Including a data source field (e.g., OSM, Global Renewables Watch, or the authors) to indicate provenance and a confidence score or quality flag would further improve transparency and usability. Such integration would significantly enhance the comprehensiveness and reliability of the dataset.

Finally, the manuscript should focus more explicitly on validating newly detected turbines that are absent from existing inventories. These turbines represent the main added value of the proposed method, yet they are also the most likely source of false positives. The authors should isolate these unique detections, conduct stratified manual validation across multiple regions, and report false positive rates specifically for this subset. Clarifying whether classification thresholds were optimized using independent data would also strengthen methodological credibility. Reducing false positives to a minimum is particularly important for infrastructure datasets that may inform ecological impact assessments and policy decisions.

Minor Comments

The manuscript contains several grammatical and stylistic issues that require correction. For example, the phrase “current methods remain inadequate monitoring” is awkwardly constructed, “which balancing representativeness” contains a grammatical error, and “Compared current datasets” is an incomplete sentence.