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The paper proposes a rapid, multi-stage workflow to estimate rural rooftop solar potential in data-scarce regions. The pipeline: (1) extracts roofs from high-resolution satellite imagery using DeepLabV3+ (Section 2.2); (2) rationalizes roof contours via Grasshopper (Bitmap+, minimum bounding rectangle, 3×3 gridding) and fuses CNBH-10m height data to generate 3D village models (Section 2.3); (3) classifies roofs into concrete (CR), clay tile (TR), and metal (MR) by RGB-based binning (27 color ranges; Fig. 3); (4) performs GPU-accelerated solar simulations with Vitality 2.0 using the Perez diffuse sky model (Section 2.4) and computes PV potential Ep and expected payback N with assumed module efficiency (ηpv), performance ratio (PR=90%), and cost model (Table 1); (5) analyzes correlations and builds ridge regressions between morphological indicators (TA, BAR, FAR, BH, OA, CR, TR, MR) and Ep/N (Sections 4.1–4.3). On 31 villages in Tianjin, the authors report large variation in Ep (5–20M kWh/year) and N (17–25+ years) and find: Ep scales strongly with village area/density; TR correlates positively with N while MR correlates negatively; ridge regression predicts Ep well (R^2>0.95; 3-fold CV R^2>0.80) but fails for N (R^2<0), which the authors attribute to nonlinearity.
Cross‑Modal Consistency: 22/50
Textual Logical Soundness: 14/30
Visual Aesthetics & Clarity: 8/20
Overall Score: 44/100
Detailed Evaluation (≤500 words):
Visual ground truth (image‑first):
• Fig.1: Pipeline for rooftop extraction (sliding windows → model → mask stitching).
• Fig.2: Two‑row workflow SI→RI→BL→Roof class; CNBH‑10m→“corrected roof info”.
• Fig.3: Sankey diagram: three roof types → 27 color bins.
• Fig.6: (a) Bar chart “Gross generation” vs village ID; (b) Bar chart “Total Cost” vs village ID.
• Fig.7: Eight small bar charts: floor area, building density, plot ratio, average height, average angle, concrete area, tile area, color‑steel area.
• Fig.8: Correlation heatmap including “Gross generation” and “Expected cost recovery period”.
• Fig.9: VIF for all features.
• Fig.10: (a) Top‑5 PCA weights: OA, MR, BH, BAR, TA; (b) VIF for selected features: TA, BAR, BH, MR, OA.
• Fig.11: Bars of R2/RMSE/MAE/MAPE for Total generation/cost/revenue/recovery period.
• Fig.12: Cross‑validation metrics, similar targets.
1. Cross‑Modal Consistency
• Major 1: “Figure 3” is cited for the minimum‑bounding‑rectangle step, but Fig.3 shows color‑bin classification. Evidence: “generate the smallest rectangular bounding box… (Figure 3)” (Sec 2.3) vs “Fig. 3 The conceptual diagram of the classification of 27 colors.”
• Major 2: Text says Fig. 6 right shows expected payback N, but the graphic is “Total Cost”. Evidence: “Fig. 6 … expected payback period… (right)” vs image title “Total Cost”.
• Major 3: PCA selection text conflicts with Fig. 10. Text selects CR and TR; figure shows MR and OA instead. Evidence: “These included… CR, TR…” (Sec 4.2) vs Fig. 10(a) bars: OA, MR, BH, BAR, TA.
• Major 4: Method inconsistency: pitched roofs are reconstructed, yet radiation modeling assumes “building units are treated as cubes.” Evidence: “Since the building units are treated as cubes…” (Sec 2.4) vs “reconstruct 3D models for these types of roofs.” (Sec 2.3).
• Minor 1: Variable naming drifts (TA vs Site/Floor area; E_total vs “Gross generation”). Evidence: Fig. 8/11 labels vs text “E_total”, “TA”.
• Minor 2: Several unnumbered “Visualization of …” figures appear without textual references.
2. Text Logic
• Major 1: “Precise 3D village models” is asserted despite using CNBH‑10m with ≈6.2 m RMSE and no local validation. Evidence: “produce precise 3D village models” (Abstract) and CNBH accuracy (Sec 2.1.3).
• Major 2: No quantitative validation of roof segmentation or BL correction (no IoU/accuracy vs ground truth). Evidence: “The study extracted roof information… generated… models.” (Sec 3) with no metrics.
• Major 3: N‑modeling acknowledged as poor (R²<0 and <0.3) yet used to propose strategies. Evidence: “R² value below zero… ridge regression failed…” (Sec 4.3).
• Minor 1: Causal claim “MR higher efficiency due to better heat dissipation” is asserted without experiment on these data. Evidence: “Due to better heat dissipation on MR, ηpv… higher.” (Sec 2.4).
3. Figure Quality
• Major 1: Many critical plots are illegible at print size (Fig.7, Fig.11–12; axis numbers/text unreadable). Evidence: tiny 212–339 px panels for key metrics.
• Minor 1: Missing sub‑figure labels in multi‑panel figures (Figs.6,7,10–12) hinder “figure‑alone” understanding.
Key strengths:
• Practical end‑to‑end workflow combining segmentation, DSM‑based heights, parametric modeling, and PV simulation.
• Sensible economic framing (N) and attempt to relate morphology to PV potential.
Key weaknesses:
• Multiple figure–text mismatches and inconsistent variable naming.
• Core methodological assumptions conflict (pitched roofs vs cube radiation model).
• Lack of segmentation/3D accuracy validation; “precise” claim unsupported.
• Critical figures are too small to read; several unreferenced visuals.
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This paper presents a methodology for estimating the solar energy potential of rural rooftops, focusing on a case study in the Tianjin region of China. The authors employ a multi-source data approach, integrating satellite imagery, a national building height dataset, and solar radiation models. The core of their method involves using a pre-trained deep learning model, DeepLabV3+, to segment building roofs from satellite images. These segmented roofs are then combined with building height data to create 3D models of villages. The solar potential is subsequently estimated using a GPU-based solar simulation tool, Vitality 2.0, which calculates potential power generation based on the modeled 3D structures. The study also incorporates a roof material classification scheme based on RGB color analysis of satellite imagery, which is used to estimate installation costs. The authors further analyze the correlation between village morphological characteristics and solar potential using statistical methods, including ridge regression. The findings suggest that building density and site area are significant factors influencing solar power generation potential. While the paper introduces a potentially useful workflow for rural solar potential assessment, it is important to note that the methodology relies heavily on existing techniques and data sources, and the study is limited by its focus on a single geographic region in China. The paper's contribution lies in the specific combination of these methods and their application to the rural Chinese context, but the lack of methodological novelty and the limited scope of the study raise questions about the generalizability of the findings. The paper also suffers from some presentation issues, including unclear figure captions and a lack of detailed validation of the generated 3D models. Despite these limitations, the paper highlights the potential for using remote sensing and simulation techniques to assess solar energy potential in rural areas, which is a relevant and important area of research.
The paper's primary strength lies in its attempt to address a significant practical problem: assessing the solar energy potential of rural rooftops, particularly in the context of China's rural energy needs. The authors have assembled a diverse set of data sources, including high-resolution satellite imagery from Jilin-1 satellites, a national building height dataset (CNBH-10m), and solar radiation models. This multi-source approach is commendable as it leverages available data to create a comprehensive view of the rural built environment. The use of DeepLabV3+ for roof segmentation is a reasonable choice given its established performance in semantic segmentation tasks. Furthermore, the integration of these data sources into a workflow that generates 3D village models and estimates solar potential using the Vitality 2.0 plugin demonstrates a practical application of these tools. The study also attempts to incorporate a degree of realism by considering different roof types and their associated installation costs, although this aspect is based on a simplified RGB color analysis. The authors' effort to analyze the correlation between village morphological characteristics and solar potential using statistical methods, such as ridge regression, is another positive aspect of the paper. This analysis, while not groundbreaking, does provide some insights into the factors that influence solar potential in the studied region. Finally, the paper's focus on a data-scarce region and its attempt to provide a cost-effective method for solar potential assessment is a valuable contribution, even if the methodology is not entirely novel. The paper highlights the potential for using remote sensing and simulation techniques to address real-world energy challenges in rural areas.
After a thorough review of the paper and the reviewer comments, I have identified several key weaknesses that significantly impact the paper's overall contribution and validity. First and foremost, the paper suffers from a lack of methodological novelty. As noted by multiple reviewers, the individual components of the methodology, such as the use of DeepLabV3+ for semantic segmentation, the use of satellite imagery and LiDAR-like data for 3D modeling, and the application of solar simulation tools, are all well-established techniques. The paper does not introduce any significant advancements or modifications to these existing methods. Instead, it combines them in a specific workflow for the context of rural Chinese villages. While this application is potentially useful, it does not constitute a significant contribution to the field of machine learning or remote sensing. This lack of novelty is a major limitation, as it reduces the paper's potential impact and relevance for a broader audience. The paper's reliance on existing methods is evident in its description of the methodology, where it explicitly cites and adopts techniques from previous studies without introducing substantial innovations. For example, the paper states, "This study employs the building roof information extraction method developed by Zhang et al. (Zhang et al.,2022)" and "The study adopts the CNBH-10m dataset proposed by Wu. as the building height information data for rural villages(Wu et al., 2023)." This reliance on existing methods is a recurring theme throughout the paper, confirming the lack of methodological novelty. Secondly, the paper's scope is limited by its focus on a single geographic region in China. The study explicitly states that it focuses on "the rural areas of the Tianjin Grand Canal region within the Beijing-Tianjin-Hebei area." This narrow focus raises concerns about the generalizability of the findings to other regions with different geographic, climatic, and architectural characteristics. The paper does not provide any evidence or discussion to support the applicability of its methodology or findings to other contexts. This limitation is significant because it restricts the broader relevance of the study and its potential impact on global solar energy assessment. The paper's introduction and methodology sections consistently refer to the specific region, highlighting the limited scope of the study. Thirdly, the paper lacks sufficient validation of its 3D model generation process. While the paper mentions the accuracy of the building height data (CNBH-10m) and uses a pre-trained model for roof segmentation, it does not provide any quantitative assessment of the accuracy of the final 3D models. This is a critical omission, as the accuracy of the 3D models directly impacts the reliability of the solar potential estimates. The paper should have included a comparison of the generated 3D models with ground truth data or other high-accuracy models to assess the quality of the reconstruction. The absence of such validation raises concerns about the accuracy of the results and the reliability of the proposed methodology. The paper's methodology section describes the process of generating 3D models but does not mention any validation steps. Fourthly, the paper's presentation is problematic, with several instances of unclear figure captions, missing figure references, and undefined variables in equations. For example, the paper includes a placeholder text "[Figure 3 missing]" and refers to "[first figure]" and "[second figure]" in figure captions. Additionally, the equations in Section 2.4 contain variables that are not explicitly defined within the equation context, such as αs in the equation for Gbi. These presentation issues make it difficult to follow the paper's arguments and understand the results. The numerous errors in figure references and captions, as well as the undefined variables in equations, demonstrate a lack of attention to detail in the paper's presentation. Finally, the paper's roof type classification method based on RGB color analysis is overly simplistic and lacks a strong justification. The paper divides the RGB color space into 27 equal ranges and assigns roof types based on these ranges, without providing any evidence that these ranges correspond to actual roof materials. This method is not robust and is likely to be inaccurate, especially given the variability in roofing materials and their appearance in satellite imagery. The paper does not cite any prior work that supports this specific method of roof classification based on equal RGB ranges. This oversimplification of roof classification is a significant weakness that could affect the accuracy of the cost estimates and the overall solar potential assessment. The paper's methodology section describes the RGB-based classification method without providing any justification or validation.
Based on the identified weaknesses, I recommend several concrete improvements to strengthen the paper. First, the authors should significantly enhance the methodological novelty of their approach. This could involve developing new algorithms for roof segmentation or 3D model generation, or adapting existing methods to address the specific challenges of rural environments. For example, the authors could explore the use of more advanced deep learning architectures for semantic segmentation or investigate methods for integrating LiDAR data with satellite imagery to improve the accuracy of 3D models. The authors should also consider incorporating more sophisticated methods for roof type classification, such as using spectral information or texture analysis, rather than relying on a simple RGB color analysis. Second, the authors should expand the scope of their study to include a more diverse set of geographic regions. This would involve testing the methodology in areas with different climates, building styles, and levels of data availability. This would help to assess the generalizability of the findings and identify any limitations of the approach. The authors could also consider comparing their results with existing studies in other regions to provide a broader context for their work. Third, the authors should conduct a thorough validation of their 3D model generation process. This would involve comparing the generated 3D models with ground truth data, such as LiDAR point clouds or high-accuracy manual measurements. The authors should also quantify the accuracy of the roof segmentation and the building height estimation. This validation is crucial for establishing the reliability of the results and the credibility of the proposed methodology. The authors should report the accuracy metrics, such as precision, recall, and F1-score, for the roof segmentation and the root mean square error (RMSE) for the building height estimation. Fourth, the authors should significantly improve the presentation of their paper. This would involve carefully checking all figure references and captions, ensuring that all variables in equations are clearly defined, and providing more detailed explanations of the methodology and results. The authors should also consider reorganizing the paper to improve its clarity and flow. The authors should also ensure that all figures are properly referenced in the text and that the captions are descriptive and informative. Finally, the authors should provide a more detailed discussion of the limitations of their study and the potential impact of these limitations on the results. This would involve acknowledging the uncertainties associated with the data and the methodology, and discussing the implications of these uncertainties for the conclusions of the study. The authors should also consider the potential impact of factors such as roof orientation, shading, and local weather conditions on the solar potential estimates. By addressing these limitations, the authors can provide a more balanced and nuanced assessment of their work. In summary, the authors should focus on enhancing the methodological novelty, expanding the scope of the study, validating the results, improving the presentation, and acknowledging the limitations to significantly strengthen their paper.
Based on my analysis, I have several questions that I believe are crucial for understanding the paper's methodology and results. First, regarding the roof segmentation, why was DeepLabV3+ chosen over more recent semantic segmentation architectures? What specific advantages did this model offer for the task of rural roof extraction, and were any other models considered? I am curious about the performance of DeepLabV3+ in this specific context and whether the authors explored any model-specific optimizations. Second, concerning the 3D model generation, how accurate are the building heights derived from the CNBH-10m dataset, and what is the impact of height inaccuracies on the solar potential estimates? The paper mentions the accuracy of the CNBH-10m dataset but does not explicitly link these accuracy metrics to the impact on solar potential calculations. I would like to understand the sensitivity of the solar simulation to errors in building height. Third, regarding the roof type classification, what is the accuracy of the RGB-based method, and how does it compare to other roof classification techniques? The paper does not provide any validation of this method, and I am concerned about its reliability. I would like to know if the authors considered alternative methods and why they chose this particular approach. Fourth, concerning the solar simulation, what are the limitations of using the module area method for estimating PV power generation, and how do these limitations affect the accuracy of the results? The paper does not discuss the potential inaccuracies of this method, and I would like to understand the potential biases introduced by this approach. I am also curious about the choice of the Perez diffuse sky model and whether other models were considered. Finally, regarding the statistical analysis, why was ridge regression chosen over other regression techniques, and what is the justification for the specific variables included in the model? The paper does not provide a detailed rationale for the choice of ridge regression, and I would like to understand the assumptions underlying this model and whether these assumptions are met by the data. I am also curious about the potential impact of multicollinearity on the regression results, even after applying PCA. These questions are aimed at clarifying key methodological choices and assumptions, and I believe that addressing them would significantly improve the paper's transparency and rigor.