Drone-Based 3D Thermal Mapping of Urban Buildings for Climate-Responsive Planning

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study presents a pioneering approach combining drone-based thermal infrared imaging with 3D reconstruction using binocular vision, achieving centimeter-level accuracy in building surface temperature measurement. This method addresses the limitations of conventional techniques (e.g., handheld cameras and satellite imagery) by offering high spatial-temporal resolution and flexibility. This paper meticulously documents diurnal variations in building temperatures across three time periods (morning, noon, evening), providing valuable insights into the impact of solar radiation and surface materials on thermal distribution. The inclusion of meteorological data (e.g., air temperature, solar radiation) enhances the robustness of the analysis. The findings obtained in this study are highly relevant for urban planning, energy efficiency optimization, and climate-resilient design. The ability to detect thermal anomalies and quantify temperature differences on building surfaces. For possible publication, the paper should be revised under consideration of the following comments. 

1. Generally, there are various types of building materials on building surfaces. The emissivity of building materials is fixed at 0.90, which may not account for variations in material composition or aging effects. Incorporating dynamic emissivity measurements or material-specific calibrations could improve accuracy.
2.  The temperature color scale used is not standardized. Typically, the color gradient representing temperature from high to low follows the sequence “red → yellow → green → blue”. If possible, it is recommended to revise it accordingly.
3.  The limitations of this study and improvement strategies in future work should be described. 

Comments on the Quality of English Language

The English language in this paper needs slight modification

Author Response

Reply to Reviewer 1 #

Comment 1:

Generally, there are various types of building materials on building surfaces. The emissivity of building materials is fixed at 0.90, which may not account for variations in material composition or aging effects. Incorporating dynamic emissivity measurements or material-specific calibrations could improve accuracy.

Reply and action:

We appreciate the important comments from the reviewer and apologize for the inaccurate expression. We acknowledge that fixing emissivity at 0.90 may not fully account for differences across various surface materials. In our revised manuscript, we have added a discussion on the potential error introduced by this assumption and highlighted the possibility of incorporating material-specific emissivity calibration in future studies to improve measurement accuracy. We have now added the expression to section 5 as follows

“In this study, the emissivity of building surfaces was uniformly set at 0.90 for simplification. However, we acknowledge that the actual emissivity varies depending on surface material, color, and aging. This may introduce minor uncertainties in absolute temperature estimation. In future work, we plan to integrate deep learning-based material classification to automatically identify different surface types and assign material-specific emissivity values accordingly. This approach is expected to improve the accuracy of temperature retrieval in complex urban environments.”

Comment 2:

The temperature color scale used is not standardized. Typically, the color gradient representing temperature from high to low follows the sequence “red → yellow → green → blue”. If possible, it is recommended to revise it accordingly.

Reply and action:

We appreciate the reviewer’s suggestion regarding the standardization of the temperature color scale. After careful consideration, we decided to retain the current color mapping for the following reasons.

First, the existing scale was designed to enhance the contrast and detail in both high- and low-temperature regions, particularly for facade features in 3D thermal models. Second, the chosen colormap performs better in rendering subtle surface texture differences and thermal gradients, which is critical for our temperature analysis and material discrimination. In our preliminary tests, we applied the recommended color gradient. However, we found that using this scale introduced difficulties in the 3D reconstruction process, particularly in the identification of homologous points between thermal images. The lack of consistent contrast in certain temperature bands reduced the robustness of texture matching, which is critical for accurate surface reconstruction. As a result, we adopted the current color scheme to ensure better performance in feature matching and texture continuity. Nevertheless, we agree with the importance of standardization, and in future studies, we will consider testing commonly accepted color scales (e.g., red-yellow-green-blue) for broader comparability.

Comment 3:

The limitations of this study and improvement strategies in future work should be described.

Reply and action:

Thank you for this important suggestion. We add a special paragraph to the conclusion to discuss the limitations of this study and outline the potential improvement of future research. We have now added the expression to section 5 as follows

“Although the proposed approach shows promising accuracy and flexibility, several limitations remain. The study only captured winter-season data from a single site, limiting its generalizability. And the effects of wind and atmospheric stability were not deeply analyzed, though they can influence thermal readings. In future work, we plan to integrate adaptive emissivity correction, expand monitoring to different seasons and building types (e.g., glass facades, metallic surfaces), and investigate thermal dynamics under variable meteorological conditions.”

Reviewer 2 Report

Comments and Suggestions for Authors

The article describes the experience of using stereoscopic infrared cameras mounted on a quadcopter to construct 3D images of urban buildings in the thermal range. From a purely practical point of view, the presented material is interesting. However, the article contains no new scientific, technical, mathematical, logistical, or other approaches. The innovative solutions declared by the authors (such as installing thermal imagers on a drone or using three-dimensional reconstruction of 2D images) have long been known. They are widely used, as evidenced even by the literature review made by the authors themselves. Thus, the presented material is unlikely to interest the scientific community. Suppose the authors can deepen the research and supplement the article with new scientific materials. In that case, it will also be helpful for them to take into account several technical comments:

1. What is a "reference source with known temperature"?
2. The authors use a stationary "mobile weather station" to set the air temperature. The air temperature will be different because the drone flies at different altitudes.
3. Page 4, line 163 - no explanation of the abbreviation "DN".
4. To determine the actual temperature emitted by the building, the authors use formula (3) combined with formula (1) but do not provide a mathematical solution.
5. Figure 2 - no explanation of the symbols.
6. The authors indicate, "Using the point cloud data, the surface reconstruction of the object is generated and smoothed by various interpolation and surface fitting techniques." However, they do not explain which smoothing algorithms are used.
7. Judging by the figures, the drones are equipped with conventional and infrared visual cameras, which build 3D visual images of buildings. However, the article says practically nothing about this.

Comments on the Quality of English Language

English language needs to be improved.

Author Response

Comment 1:

The innovative solutions declared by the authors have long been known. Thus, the presented material is unlikely to interest the scientific community.

Reply and action:

We sincerely thank the reviewer for this valuable comment. We agree that the basic concepts of UAV-based thermal imaging and 3D reconstruction are well established. However, our study aims to bridge the methodological gap between these two domains by applying a combined centimeter-level reconstruction and temperature correction framework under real urban field conditions. Our contributions include:

1. A full integration of thermal correction (incorporating local meteorological data and Planck-based inversion) with 3D surface mapping.
2. Quantitative assessment of diurnal thermal variation in building clusters.
3. A detailed error comparison with ground-based thermocouple measurements across multiple time periods and façade orientations.

Comment 2:

What is a "reference source with known temperature"?

Reply and action:

Thank you for this question. The “reference source with known temperature” refers to the calibration baffle (also called blackbody or reference plate), a surface with pre-calibrated temperature and known emissivity. It is used to adjust for background radiation effects and improve thermal image accuracy. We have clarified this in Section 2.1.

"...The calibration baffle, with a known and stable surface temperature and emissivity (typically close to 0.95), serves as the reference source to reduce the impact of environmental noise and to calibrate the thermal infrared camera."

Comment 3:

The authors use a stationary "mobile weather station" to set the air temperature. The air temperature will be different because the drone flies at different altitudes.

Reply and action:

We agree with the reviewer that air temperature may vary with altitude. In our study, the flight height was maintained consistently at 100 meters, and based on typical winter meteorological profiles and low wind conditions, the vertical temperature difference within the 100-meter flight altitude was expected to be minor. Therefore, using the mobile weather station at surface level as the reference was considered acceptable. We have added a clarification in Section 3.3(lines 345-349).

"Since the drone operated at a stable altitude of 100 meters and the vertical air temperature gradient was found negligible under stable atmospheric conditions during winter, the ground-based mobile weather station was used as the reference for atmospheric temperature input."

Comment 4:

Page 4, line 163 - no explanation of the abbreviation "DN".

Reply and action:

Thanks for pointing this out. We have now explained that DN stands for “Digital Number”, i.e., the raw pixel value recorded by the thermal sensor before calibration. The definition is added in Section 2.1(lines 222-223).

Comment 5:

To determine the actual temperature emitted by the building, the authors use formula (3) combined with formula (1) but do not provide a mathematical solution.

Reply and action:

Thank you for this important observation. We agree that the temperature retrieval process should be more explicitly presented. In the revised manuscript, we have added a detailed explanation of how formula (1) (Planck's Law) and formula (3) (thermal radiation model) are used together to calculate the surface temperature from thermal image data. The inversion procedure includes:

1. Converting the pixel DN value into radiance using the camera's internal calibration parameters.
2. Correcting for atmospheric and background radiation effects using formula (3).
3. Applying the inverse of Planck’s Law (formula 1) to retrieve the absolute temperature.

A full description of this process has been added to Section 2.3.

“In this study, the actual surface temperature was calculated through a two-step process. First, the total radiance measured by the thermal sensor was corrected using atmospheric parameters, emissivity, and background radiation based on the thermal radiation balance model (formula 3). Then, the corrected surface radiance was used in the inverse form of Planck’s law (formula 1) to determine the corresponding surface temperature. While the full derivation is omitted for brevity, this method follows standard procedures in thermal remote sensing and has been validated in previous studies.

All necessary parameters (e.g., emissivity, transmittance, background radiance) were either measured or estimated based on on-site meteorological data and sensor specifications.”

Comment 6:

Figure 2 lacks explanation of the symbols.

Reply and action:

We agree that complete picture descriptions have been added to Section 2.2.
“where  and  are the centers of two cameras, A is a point in space, and the projection of A on the corresponding image plane is  and . The intersection points  and  between  and  and the image plane are called poles.”

Comment 7:

The authors indicate, "Using the point cloud data, the surface reconstruction of the object is generated and smoothed by various interpolation and surface fitting techniques." However, they do not explain which smoothing algorithms are used.

Reply and action:

Thank you for pointing this out. In the revised manuscript, we have specified the smoothing techniques used in our 3D reconstruction workflow. Specifically, we applied bilateral filtering to reduce noise while preserving edge structures, and Poisson surface reconstruction to generate a continuous and smooth surface mesh from the point cloud. These methods were selected based on their robustness and proven effectiveness in processing thermal image-derived geometry under noisy outdoor conditions. We have added this clarification in Section 2.2.
  “Using the point cloud data, the surface reconstruction of the object is generated and smoothed by a combination of bilateral filtering and Poisson surface reconstruction. The bilateral filter helps reduce noise while preserving the edges of thermal features, and the Poisson surface reconstruction algorithm is used to produce a continuous and watertight mesh that conforms to the geometric structure captured by the drone. These methods ensure that both thermal gradients and façade geometries are accurately preserved.”

Comment 8:

Judging by the figures, the drones are equipped with conventional and infrared visual cameras, which build 3D visual images of buildings. However, the article says practically nothing about this.

Reply and action:

Thank you for this valuable comment. In our study, the visible-light (RGB) camera was not involved in the 3D reconstruction process. The 3D model was entirely generated based on thermal infrared image pairs using the binocular vision method. However, the RGB images were acquired in parallel and used primarily for comparative analysis and visual inspection of building surface conditions, materials, and structures. These visible images helped interpret the observed thermal patterns by identifying façade colors, materials, and features such as balconies or ventilation points. We have now clarified the role of visible-light images in Section 2.2 and in the captions of related figures.

“It is important to note that in this study, the visible-light (RGB) camera was used solely for comparative analysis and visualization. The 3D reconstruction was performed exclusively using thermal infrared images based on the binocular vision algorithm. The RGB images provided structural and material context, helping to interpret the temperature distribution observed in the thermal model (e.g., identifying wall coating colors, balcony overhangs, or roof equipment), but they were not used in the generation of the 3D geometry.”

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript addresses an interesting topic and is, overall, clearly written and well-structured.

However, there are a few points that need clarification and improvement. In the introduction, it is stated that the mapping was conducted on January 13th, 2023, whereas in other sections of the paper, the date appears to be January 8th, 2023. Additionally, the rationale behind the choice of this specific date is not explained. Given that the simulation was carried out more than two years ago, it would have been valuable to include results from the summer period as well, to provide a more comprehensive perspective.

The title suggests that this approach supports climate-responsive planning, yet the manuscript lacks a dedicated discussion section linking the mapping results explicitly to climate-responsive planning strategies. This connection should be strengthened and clearly articulated.

Furthermore, in the case study description, it would be helpful to include more details about the wall composition or visual appearance of the building envelope.

Author Response

Comment 1:

In the introduction, it is stated that the mapping was conducted on January 13th, 2023, whereas in other sections of the paper, the date appears to be January 8th, 2023.

Reply and action:

Sorry for the mistake. The correct date of the field campaign is January 8th, 2023. We have revised the introduction accordingly to ensure consistency throughout the manuscript.

Comment 2:

The rationale behind the choice of this specific date is not explained.

Reply and action:

We appreciate the reviewer’s comment. January 8th, 2023 was selected due to its clear sky, calm wind conditions, and stable winter temperature, which are ideal for acquiring high-quality thermal data with minimal atmospheric interference. We have added this explanation in Section 3.2.

“January 8th, 2023 was selected for field data acquisition as the meteorological conditions on that day — including clear skies, low wind speed (<3 m/s), and stable ambient temperature — were ideal for thermal imaging and ensured minimal atmospheric distortion.”

Comment 3:

Given that the simulation was carried out more than two years ago, it would have been valuable to include results from the summer period as well.

Reply and action:

We agree with the reviewer that incorporating data from multiple seasons would provide a more comprehensive view of building thermal dynamics. We have added this explanation in section 5.

“To better capture seasonal variability in thermal performance, future work will include additional campaigns in summer and transitional seasons.”

Comment 4:

The title suggests that this approach supports climate-responsive planning, yet the manuscript lacks a dedicated discussion section linking the mapping results explicitly to climate-responsive planning strategies.

Reply and action:

Thank you for pointing this out. In section 5, we added a special paragraph to emphasize how the heat map results can provide information for climate-responsive urban planning.

“The detailed 3D thermal maps produced in this study offer valuable inputs for climate-responsive planning. First, they help identify building surfaces with excessive heat retention, which can be targeted for retrofitting (e.g., through improved insulation or reflective coatings). Second, by analyzing how surface temperature correlates with material types and solar exposure, planners can refine building codes to promote heat-resilient façade materials in specific orientations. Third, this approach supports passive design optimization by visualizing diurnal heat load variations, which is essential for reducing cooling demand in summer. Overall, integrating thermal mapping into early-stage planning and retrofitting can enhance thermal comfort, reduce energy use, and increase urban resilience to climate change. This study contributes to UN Sustainable Development Goals 11.”

Reviewer 4 Report

Comments and Suggestions for Authors

The review in the attached file

Comments for author File: Comments.pdf

Author Response

Comment 1:

What do you mean with "background"? the environmental condition of the site? please specify in the text.

Reply and action:

Thank you for pointing out this ambiguity. In the revised manuscript, we have clarified that “background radiation” refers specifically to the reflected longwave infrared radiation from surrounding sources (e.g., sky or ground) that is reflected by the object surface and captured by the thermal camera. This clarification has been incorporated into the explanation of Fig. 1.

Comment 2:

Are TMAX and TMIN the terms MAX and MIN reported in the equation 4?

Thank you for this observation. Yes, TMAX and TMIN refer to the maximum and minimum measured temperature values and correspond directly to MAX and MIN in Equation (4). To improve clarity, we have updated the notation in the text and figure captions to consistently use MAX and MIN throughout the manuscript.

Comment 3:

Does the position take into account the edge effect? Were the measurements taken on the four sides of each building? When reporting the measurements taken, the orientations of the facades should be specified in the text.

Reply and action:

Thank you for this important suggestion.We confirm that the mobile weather station was placed in an open area near the center of the study site, ensuring minimal obstruction from nearby buildings and reducing potential edge effects. Regarding the thermocouple validation, temperature measurements were taken on three representative building facades, including sunny (south-facing) and shaded (north-facing), to capture the variation caused by solar exposure.

Comment 4:

This background temperature is affected by the presence of the buildings; moreover, we don't know what is the trend during the day. The best thing could be to include a daily graph with data collected at municipal level without interference (on a rooftop, or far from buildings), to better understand the "background" of this specific area.

Reply and action:

Thank you for this valuable comment. We agree that the presence of surrounding buildings may influence the background temperature measured by our on-site weather station, especially through radiative exchanges or wind shielding. Unfortunately, municipal-level rooftop data for this specific date were not publicly available.

Comment 5:

Beside the data from the firm information, could be more significant to mention the scientific studies that define the acceptable errors, usually in terms of percentage. 10%? 30%?

Reply and action:

Thank you for this insightful suggestion. In addition to the manufacturer’s specification, we have now referred to published scientific studies that discuss acceptable error margins in UAV-based thermal imaging. According to Yuan & Zhang (2019), an error margin of ±2–5°C or approximately 10–15% is generally considered acceptable in building thermography applications, depending on the surface temperature range and atmospheric conditions. We have included this reference and clarification in Section 3.4.

“The deviations were all within 5°C, which is consistent with both the manufacturer’s specification and prior studies on thermal infrared building diagnostics. For instance, Yuan & Zhang (2019) reported that errors within ±2–5°C or 10–15% are generally acceptable for UAV-based thermography in urban environments, especially when the target surface temperatures are in the 20–60°C range.”

Comment 6:

In the presentation of the study area, it could be interesting to include the construction system of the buildings (brick wall? concrete? wooden?), because it dramatically affects the thermal mass and then the surface temperature.

Reply and action:

Thank you for this important suggestion. The main structure of the buildings in the Yuquanting area is composed of reinforced concrete with solid infill walls and painted cement renderings. This construction system has a relatively high thermal mass, which contributes to the surface temperature lag and gradual cooling behavior observed during the evening period. We have added this information to the description of the study area and to the interpretation of thermal patterns in Section 4.1.

“The buildings in the Yuquanting residential area are primarily constructed using reinforced concrete frames with solid brick or block infill walls, finished with cement plaster and exterior paint coatings. This construction system is typical for urban residential buildings in northern China and exhibits moderate to high thermal mass.”

Comment 7:

 and thermal mass  (J/°K) and specific heat capacity (J⋅kg⁻¹⋅K⁻¹)

Reply and action:

Thank you for this valuable comment. We agree that in addition to thermal conductivity, the thermal mass and specific heat capacity of building materials also play important roles in determining surface temperature responses in shaded areas. We have now revised the sentence to reflect this more comprehensively.

“This temperature difference can be attributed to the heat conduction (1.4 W/(m · K)), thermal mass (J/°K), and specific heat capacity (J·kg⁻¹·K⁻¹) of the building materials, which together influence how quickly and to what extent the surface temperature adjusts in response to solar exposure and air temperature increase.”

Comment 8:

It is supposed to be analysed the south facade, isn't it? Please specify in the text.

Reply and action:

Thank you for your observation. Yes, the analysis presented in Fig. 10 focuses on the south-facing facade of Building E, which receives the most direct solar radiation during the day. We have clarified this in the text to avoid ambiguity.

“Fig. 10 shows the visible and infrared 3D models of the south-facing facade of Building E during three time periods. This facade was selected for analysis due to its direct exposure to solar radiation throughout the day, which provides representative insights into diurnal thermal behavior.”

 

Comment 9:

The reason could be clear if a shadow cast analysis in the same day and hours of the survey with the drone was done.

Reply and action:

Thank you for this excellent suggestion. We agree that a shadow cast analysis based on sun position and building geometry could provide clearer evidence for differences in solar exposure between facades. Although a full simulation was not included in this study, we confirmed from the 3D model and drone-captured images that only the south facade of Building G was fully exposed to direct sunlight during the survey time. We have now added this clarification in Section 4.2, and a shadow simulation is planned in future work to further support the thermal analysis.

“It could be seen that only the south-facing facade of Building G received full direct solar radiation during the survey period. This was verified by inspecting the solar position and 3D model geometry, which revealed that adjacent buildings partially shaded other facades. Although a full shadow cast simulation was not conducted in this study, the observed solar exposure pattern aligns with the captured infrared data.”

Comment 10:

As suggestion for the following step, it could be interesting to include the sky view factor, that add some information about the exchange with the sky vault and the exposure to the sun, and could justify some data from the field survey.

Reply and action:

We sincerely appreciate this insightful recommendation. The Sky View Factor (SVF) is indeed a key geometric parameter influencing both solar exposure and longwave radiative cooling. Incorporating SVF analysis in future work will allow us to more quantitatively interpret building surface temperature variations, especially in areas with dense urban morphology or partial shading. We have acknowledged this in the discussion on future research directions.

“This study is our first step in measuring the urban thermal environment and retrieving building surface temperature using UAV-based thermal imaging. In future research, we will extend the investigation to different building types such as wooden and glass curtain-wall buildings. In addition, seasonal comparisons will be performed, especially during summer periods with higher thermal loads.

Furthermore, geometric indicators such as the Sky View Factor (SVF) will be integrated into our analysis framework to assess the influence of solar access and radiative heat loss to the sky dome. This is expected to enhance our ability to explain spatial temperature patterns in complex urban settings.”

Reviewer 5 Report

Comments and Suggestions for Authors

This study presents a technically sound and timely contribution to urban thermal environment monitoring using UAV-based 3D thermal infrared imaging. 

Following some suggestions for improving the paper with more clarity:

• The manuscript should mention any reference to thermal comfort, which is crucial when discussing urban climate and building thermal behavior. Since surface temperatures influence microclimate and human thermal experience, include a brief analysis in the discussion section and future recommendations for assessing outdoor thermal comfort indices such as the UTCI or the PET. Discuss how these indicators could help assess the surface temperature profiles and their influence on thermal comfort. 
• Mention in the methodology how the calibration parameters regarding the emissivity, reflectance, and background temperature were obtained or assumed. 
• Present an error analysis or sensitivity study to evaluate the temperature extraction process under varying environmental conditions. 
• Regarding the 3D models, please include a quantitative use of the 3D model, for instance, the surface area calculation, orientation impact, and surface heat flux estimates, to strengthen the methodology and to justify the relevance of using 3D over 2D models. 
• In the discussion section, please mention what you expect during the winter and what seasonal contrast you expect in the surface temperatures, solar gain, and comfort impacts. 

Author Response

Comment 1:

The manuscript should mention any reference to thermal comfort, which is crucial when discussing urban climate and building thermal behavior. Since surface temperatures influence microclimate and human thermal experience, include a brief analysis in the discussion section and future recommendations for assessing outdoor thermal comfort indices such as the UTCI or the PET. Discuss how these indicators could help assess the surface temperature profiles and their influence on thermal comfort.

Reply and action:

Thank you for this insightful suggestion. We agree that thermal comfort is a crucial aspect of urban climate analysis. Although our current study focuses on building surface temperatures, we have now added a discussion on the potential application of thermal comfort indices such as UTCI (Universal Thermal Climate Index) and PET (Physiological Equivalent Temperature) in future research. These indicators could be integrated with 3D thermal models to assess microclimate variation and outdoor human comfort. This has been added to Section 5.

“In future studies, thermal comfort indices such as the Universal Thermal Climate Index (UTCI) and Physiological Equivalent Temperature (PET) could be calculated using combined data from surface temperature, radiation, air temperature, wind speed, and humidity. These indices would help translate the thermal information into human-centered microclimatic insights, supporting outdoor space design and climate-sensitive planning.”

 

Comment 2:

Mention in the methodology how the calibration parameters regarding the emissivity, reflectance, and background temperature were obtained or assumed.

Reply and action:

Thank you for the comment. We have now clarified in Section 2.3 that emissivity was assumed as 0.90 based on typical building material values, reflectance was derived as (1−ε), and background temperature was approximated using ambient air temperature measured by the weather station, in line with previous studies.

 

Comment 3:

Present an error analysis or sensitivity study to evaluate the temperature extraction process under varying environmental conditions.

Reply and action:

We appreciate this important suggestion. While a full sensitivity study is beyond the current scope, we have now included an error discussion in Section 3.4. The sources of error—such as atmospheric transmittance uncertainty, emissivity variation, and pixel co-registration mismatch—are outlined, along with estimated impact based on literature.

“The accuracy of the retrieved surface temperature is affected by several factors, including uncertainties in emissivity assignment, atmospheric transmittance variation, and pixel-level alignment between stereo pairs. Based on previous thermal calibration studies, the cumulative uncertainty is estimated to be within ±2–5℃, which aligns with our validation using thermocouple data.”

Comment 4:

Regarding the 3D models, please include a quantitative use of the 3D model, for instance, the surface area calculation, orientation impact, and surface heat flux estimates, to strengthen the methodology and to justify the relevance of using 3D over 2D models.

Reply and action:

Thank you for this constructive suggestion. We fully agree that using 3D models for quantitative analysis such as surface area extraction, orientation analysis, and surface heat flux estimation would enhance the methodological strength of this work. However, these calculations were beyond the current scope and technical setup of this study, which focused primarily on validating the feasibility of UAV-based 3D thermal reconstruction in urban contexts. Nevertheless, in our ongoing research, we plan to integrate orientation-based surface radiation models and area-based energy flux calculations to better quantify urban heat distribution and to further demonstrate the advantages of 3D thermal modeling over 2D approaches.

Comment 5:

In the discussion section, please mention what you expect during the winter and what seasonal contrast you expect in the surface temperatures, solar gain, and comfort impacts.

Reply and action:

Thank you. We have added a seasonal outlook to the discussion section. During summer, higher solar angles and ambient temperatures are expected to result in elevated surface temperatures and increased cooling loads, while thermal comfort conditions may worsen in exposed pedestrian areas.

“In contrast to the winter findings, we anticipate significantly higher surface temperatures and thermal discomfort in summer due to increased solar intensity, reduced longwave heat loss, and higher air temperatures. Future campaigns will analyze these seasonal effects and their implications on facade design, energy loads, and urban comfort.”

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The scientific value of this paper remains relatively low. However, the authors have demonstrated persistence and diligence, clarifying numerous technical issues related to the method they employed. The paper is of practical interest to practicing engineers and is suitable for publication in the journal Sustainability.

Reviewer 4 Report

Comments and Suggestions for Authors

reviews took into consideration