Thanks for this work, try to develop a robust algorithm to generate global offshore oil and gas datasets from satellite observations. The methodology and results look reliable and reasonable. I have several comments for your reference.

1. Fig. 3, why do you only use Sentinel-2 satellite imagers during Jan-Mar for OOGP generation? 

2. L155-160, the FIRMS with 1-km spatial resolution, Near-real time frequency was used to identify platform activity status. This is an interesting point and skillful solution. The key challenge is how to address the discrepancy in spatiotemporal resolutions between this fire data and Sentinel-1/2 data (with 10-60 m resolution). Please add more details on this point. For example, how is proximity defined in the proposed approach?

3. L160, as mentioned above, FIRMS has a different spatiotemporal resolution from the primary satellite data (Sentinel-1/2). Please clarify this point.

4. Eq.(1), although two local examples are given to demonstrate the rationale regarding the BCmax threshold of -20db, an adaptive threshold like Eq. (2) is more reliable. 

5. Fig. 10, how can we control the effect of the uncertainty in satellite data and algorithm (like threshold selection) on these temporal variations? 

This manuscript presents a satellite-based framework for detecting offshore oil and gas platforms (OOGPs) using multi-source remote sensing data, primarily Sentinel-1 SAR imagery, and constructs a global dataset covering six major offshore basins from 2017 to 2023. The study addresses an important gap in existing infrastructure inventories by providing a more temporally consistent and spatially comprehensive dataset, with potential applications in environmental monitoring and methane emission attribution. However, in its current form, the manuscript still suffers from methodological ambiguity, insufficient validation rigor, and limited discussion of uncertainty and generalizability. Recommendation: Major RevisionMajor Comments:1. The manuscript claims to develop a “robust” and “automated” framework, yet the methodological components (e.g., thresholding, morphological filtering, occurrence frequency filtering) largely rely on established techniques. What is the clear methodological advancement beyond existing SAR-based OOGP detection studies? How does this approach quantitatively outperform prior methods (e.g., deep learning-based or multi-sensor fusion approaches)?2. The thresholding strategy, including the use of a fixed backscatter cutoff and a percentile-based adaptive threshold, lacks sufficient theoretical justification. It also remains unclear how sensitive the results are to these parameter choices.3. The regional variability of Occurrence Frequency (OF) thresholds (e.g., OF=12 for the English Channel vs. OF ≥ 2 for the GoG) is a pragmatic solution to maritime traffic noise. However, the manuscript lacks a quantitative justification for these specific values. Providing sensitivity analysis results for these thresholds would strengthen the methodological rigor.4. The use of the Mann–Kendall test to infer installation dates from SAR backscatter time series is an interesting approach, but its reliability is uncertain given the limited temporal coverage of Sentinel-1 data. Abrupt changes in backscatter may not uniquely correspond to construction events. The manuscript would benefit from additional validation or uncertainty analysis for these inferred temporal attributes.Minor Comments:

1. In the Abstract, "existing OOGP databases are often incomplete or outdated data" should be corrected to "incomplete or contain outdated data".2. The abstract and conclusions report slightly different accuracy figures (“98 %” vs. “0.99”). Aligning all statements with the precise metrics in Table 3 (precision = 0.98, recall = 0.92, F1 = 0.95) would eliminate any confusion.3. P2, lines 25–30: “these aging platforms typically have three potential decommissioning options…” is wordy; tighten.4. Terminology is generally consistent, but “OOGP(s)” and “offshore platforms” are occasionally mixed within the same paragraph. Using the acronym uniformly after its first definition would enhance precision.5. Figure 9’s color legend (red points) could be slightly deepened for better readability in print or PDF format.6. Table 2 would benefit from a brief note on the exact date format (YYYYMM) and how null values for installation/removal dates are handled.