the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Mar 2023
A generic algorithm to automatically classify urban fabric according to the Local Climate Zone system: implementation in GeoClimate 0.0.1 and application to French cities
Abstract. Geographical features may have a considerable effect on local climate. The Local Climate Zone (LCZ) system proposed by Stewart and Oke (2012) is nowadays seen as a standard referential to classify any zone according to a set of urban canopy parameters. While many methods already exist to map the LCZ, only few tools are openly and freely available. This manuscript presents the algorithm implemented in the GeoClimate software to identify the LCZ of any place in the world based on vector data. Seven types of information are needed as input: building footprint, road and rail networks, water, vegetation and impervious surfaces. First the territory is partitioned into Reference Spatial Units (RSU) using the road and rail network as well as the boundaries of large vegetation and water patches. Then 14 urban canopy parameters are calculated for each RSU. Their values are used to classify each unit to a given LCZ type according to a set of rules. GeoClimate can automatically prepare the inputs and calculate the LCZ for two datasets: OpenStreetMap (OSM - available worldwide) and the BD Topo v2.2 (BDT - a French dataset produced by the national mapping agency). The LCZ are calculated for 22 French communes using these two datasets in order to evaluate the effect of the dataset on the results. About 55 % of all areas has obtained the same LCZ type with large differences when differentiating this result by city (from 30 % to 82 %). The agreement is good for large patches of forest and water as well as for compact mid-rise and open low-rise LCZ types. It is lower for open mid-rise, open high-rise mainly due to height underestimation for OSM buildings located in open areas. By its simplicity of use, Geoclimate has a great potential for new collaboration in the LCZ field. The software (and its source code) used to produce the LCZ data is freely available at https://zenodo.org/record/6372337, the scripts and data used for the purpose of this manuscript can be freely accessed at https://zenodo.org/record/7687911 and are based on the R package available at https://zenodo.org/record/7646866.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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RC1: 'Comment on egusphere-2023-371', Anonymous Referee #1, 02 May 2023
The manuscript is written quite well. The model is well decribed, the concept of the manuscript is well organized and it is easy for reading and understanding. It is noticable that authors have experience in modeling as well as in writing the scientific papers. Also GeoClimate software is useful in climate research, and it is open-source software, open of new collaborators and useful for datasets analysis. Also, here is presented that is good tool for the LCZ clasification. Therefore, this manuscript can be considered for publication in this journal.
Also, there are some disadvantages that should be discuss in the future. After the reading this manuscript, the impression remains that it is still sufficient to use the LCZ Generator (WUDAPT), and that GeoClimate does not provide any novelty in defining the LCZ. Maybe for authors it was not the main goal, but maybe readers will expect to see new tool that should be better than old ones. As it was highlighted in the Conclusions...the integration of GeoClimate and WUDAPT tools could make significant improvements in furhter LCZ classification and this should be a next step of the authors.
Citation: https://doi.org/10.5194/egusphere-2023-371-RC1 -
AC1: 'Reply on RC1', Jérémy Bernard, 04 May 2023
Thank you to anonymous referee #1 for the comments.
Concerning the discussion about WUDAPT and GeoClimate comparison, as the referee assumed, it is not the purpose of this manuscript. The GeoClimate LCZ algorithm has been available for the community since several years so it was needed to:
• describe clearly what was the methodology used to determine the LCZ of a given area
• evaluate what would be the difference in using the worldwide available OSM dataset instead of the French BDTopo one.
Some preliminary works (such as in Blond et al. (2023)) have been achieved to compare GeoClimate to WUDAPT on some French cities but they have not been published yet. The main rough observation is that GeoClimate seems more appropriate than WUDAPT for urban areas but less appropriate for rural areas.
Blond, N., Breton, F., Micolier, A., and Mendez, M.: A modeling approach to address building energy consumption and thermal comfort under urban climate change , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14169, https://doi.org/10.5194/egusphere-egu23-14169, 2023.Citation: https://doi.org/10.5194/egusphere-2023-371-AC1 -
RC3: 'Reply on AC1', Anonymous Referee #1, 15 May 2023
Dear authors,
OK. For me, your replies are acceptable. I will not have any comments.
Citation: https://doi.org/10.5194/egusphere-2023-371-RC3
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RC3: 'Reply on AC1', Anonymous Referee #1, 15 May 2023
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AC1: 'Reply on RC1', Jérémy Bernard, 04 May 2023
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RC2: 'Comment on egusphere-2023-371', Jan Geletic, 10 May 2023
In general, I completely agree with the first reviewer. Manuscript is well written and the introduction to the concept of LCZs is fine. Honesty, I expected more innovations and discussion of problems you found. I can imagine problems related to development, becuase I also developed a LCZ generator based on national dataset(s).
Related to that I have 3 major questions:
- You mentioned the widely known LCZ generator, WUDAPT. There is no comparison and I fully accept it (as you stated in reply). But which benefits can we could expect if your algorithm will be used? Is it better, faster, integrated... This study is focused on French cities only, so I am not sure.
- There were presented and discussed differences between BDT and OSM. But which layer is more precise? Did you somehow validate results with reality (e.g., using manually defined samples etc.)? How accurate algorithm is?
- Geodatabase in OSMs is based on community - quality is definitely not the same worldwide. Moreover, there is much more data available for OSM editors. I am quite skeptical of the trustworthiness of OSM in some parts of the world. Despite that fact, in Europe exists freely-available sources with a strong potential for delineation methods - Copernicus Land Monitoring Services. Do you know about these datasets with a spatial resolution in tens of meters (e.g., Urban Atlas, European Settlement Map, Imperviousness etc.)? It would be interesting novelty...
Citation: https://doi.org/10.5194/egusphere-2023-371-RC2 -
AC2: 'Reply on RC2', Jérémy Bernard, 17 May 2023
Thank you to Jan Geletic for his review and comments. As an answer to your general comment, we would like to highlight that our main contribution to the field is having a generic vector-based method to calculate the LCZ and that this algorithm is available by anyone. Then any dataset can be used to calculate the LCZ using this method. The main goal of the manuscript is then to describe the detailled methodology used by the GeoClimate LCZ algorithm. Then the application of the algorithm is illustrated using BDT and OSM datasets. Our manuscript thus falls into the GMD manuscript type called "Model description papers" (https://www.geoscientific-model-development.net/about/manuscript_types.html).
Below are answers to Jan Geletic three major comments.
- For many European and North America cities, we expect a better identification of urban LCZ types using GeoClimate and OSM than using LCZ generator since the degree of completeness of OSM is rather good for building geometry and building types in these regions and also OK concerning building height (see Bernard et al., 2022). However, LCZ generator is probably more appropriate for rural areas since many small tree vegetation patches or types might be missing in OSM. Moreover, there is still a big lack of OSM data such as in many African, Asian or South American countries. As described in Bocher et al. (2021), GeoClimate algorithms can roughly be distinguished in two steps: first data are imported into a well defined and generic data model; second all indicators and classifications are performed using the data previously included in the generic model. Thus GeoClimate might still be used if local vector dataset exist. However, the first step will still be needed to be performed.
-
As discussed in the manuscript section 2.5, the main expectations we may have about the differences between OSM and BDT data are that:
- BDT building height is more accurate than the OSM one since for most of the OSM buildings, this information is simply estimated using a random forest model using BDT as real value for the training (cf. Bernard and al., 2022).
- OSM data has a generally higher land coverage which is mainly due to a better representation of impervious area and vegetation within cities and also a higher building coverage. This information has been verified in our study and is discussed section 3.3. However, the statistics given have been inverted and will be corrected in a next version (37% in BDT against 55% in OSM).
We have not verified the data to manually defined samples in this study since the comparison of the two datasets was not the objective of this manuscript (thus the datasets are compared relatively to each other).
What does the reviewer mean by algorithm accuracy ? Accuracy of the LCZ algorithm in general ? This accuracy is not discussed in the article since it always mean that a reference data should be found and that this reference data may also have its shortcomings. As answered to the #Anonymous referee 1, in the near future, we aim at comparing the GeoClimate tool results to manual classifications or remote sensing methods such as LczGenerator. If by accuracy Jan Geletic means the accuracy of the UCP used for LCZ classification, there are limitations which depends on each UCP. For example for the SVF calculation, more information about the accuracy can be found in Bernard et al. (2018) but the algorithm is limited in a way that it only consider flat roofs and no trees. The accuracy of each UCP is then really UCP dependent. - As previously discussed, there is indeed almost no OSM data in some part of the world and thus OSM is not applicable there yet. However, as previously explained also, GeoClimate is a two steps approach where the first step concerns data import into the generic GeoClimate data model. Thus some other datasets can be merged during this first step (even though it might mean a considerable amount of work depending on data completeness) and then be used in the presented GeoClimate algorithm (second step of the GeoClimate processing chain). But the first objective of this article is to share the methodology used by GeoClimate to identify the LCZ (so the second GeoClimate step), not to show combination of data that can be used in GeoClimate (first GeoClimate step). However we have illustrated the behavior of the algorithm depending on two different datasets. In the future, a potential work could indeed be to try to merge different datasets to obtain the best land coverage and thus LCZ classification but this has to be done in a future work, this one being the description of the LCZ methodology used by GeoClimate.
Citation: https://doi.org/10.5194/egusphere-2023-371-AC2 -
RC4: 'Reply on AC2', Jan Geletic, 18 May 2023
Dear authors,
Thank you for your reaction and references. Points 1 and 3 are fine for me.
Further explanation for point 2 is still needed. As you stated, your paper was submitted as ‘Model description paper’. See, please, its detailed definition on GMD website, specifically point below:
Examples of model output should be provided, with evaluation against standard benchmarks, observations, and/or other model output included as appropriate. In this respect, authors are expected to distinguish between verification (checking that the chosen equations are solved correctly) and evaluation (assessing whether the model is a good representation of the real system). Sufficient verification and evaluation must be included to show that the model is fit for purpose and works as expected. Where evaluation is very extensive, a separate paper focussed solely on this aspect may be submitted.
Can you, please, select one of the cities you classified and compare it with an expert-based classification? Or with a WUDAPT method? This information is important for a potential user; without this information you cannot state that model provides relevant or sufficient results. Attached is a manually classified sample for Brno in the Czech Republic, if you have no own sample.
With respect,
Jan Geletic
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AC3: 'Reply on RC4', Jérémy Bernard, 24 May 2023
Dear Jan Geletic,
Thank you for your quick reaction.
You are waiting for further explanation concerning the lack of evaluation of the method. We understand and agree that comparing the GeoClimate results to other state-of-the art methods is necessary and as previously said as answer to your “major comment 1”, we will make this comparison in an early coming future.
You state that as a “Model description paper”, our manuscript must contain an evaluation section. You refer to a paragraph where the word “should” is used. Thus this paragraph is not necessarily applicable (below is the distinction made by GMD between "should", "must" and "may", this paragraph being located at the begining of the “manuscript type” page of GMD).
'In the following, "must" means that the stated actions are required, and the paper cannot be published without them; "should" means that we encourage the action, but papers can still be published if the criteria are not met; "may" means that the action may be carried out by the authors or reviewers, if they so wish.'
We have made our best to fullfill all the “must”, most of the “should” and some of the “may” but we have not fullfilled the evaluation part which is a “should”. Concerning this one, we need to reaffirm our position. There are two objectives within this manuscript: the first (the main one) aims at describing accurately what is performed within the GeoClimate LCZ algorithm; the second is to illustrate the differences obtained using two datasets that are currently automatically usable with GeoClimate. Still, the “evaluation” of GeoClimate using the LCZ map produced by an other method on a single territory could be performed and added to the manuscript as you proposed. However, this involves:
- A third objective to our article (or at least a new section) which might make the manuscript hard to follow and too long
- Having the LCZ map of a French city since the article focus on French cases
- The results of the “evaluation” (comparison) would be valid only for this single territory. Thus it will only be a sort of illustration more than an evaluation or an interesting comparison where general conclusions can be made.
In order to have a proper evaluation, a solution would be to compare the GeoClimate method to an other one which has been applied to many different French locations. However, this raises two issues:
- The manuscript would get really long
- For now and at our knowledge, only the WUDAPT method has been applied at such scale and as previously discussed in our answer to your “major comment 1”, we have planned to do this comparison in a separate article
Our point of view is that this manuscript is a good base for future comparisons involving the GeoClimate algorithm. This first article describes the algorithm and the limitations of using one of the two datasets (BDT or OSM). As a consequence, for French applications, the current optimized dataset would be to use a combination of OSM and BDT. Moreover, we have now evaluated the limitation of using the OSM data (which miss the building height) thanks to a reference dataset (BDT where building height accuracy is known). This preliminary knowledge was at our point of view necessary before to evaluate further the GeoClimate method using OSM on other territories than France.
Citation: https://doi.org/10.5194/egusphere-2023-371-AC3
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AC3: 'Reply on RC4', Jérémy Bernard, 24 May 2023
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-371', Anonymous Referee #1, 02 May 2023
The manuscript is written quite well. The model is well decribed, the concept of the manuscript is well organized and it is easy for reading and understanding. It is noticable that authors have experience in modeling as well as in writing the scientific papers. Also GeoClimate software is useful in climate research, and it is open-source software, open of new collaborators and useful for datasets analysis. Also, here is presented that is good tool for the LCZ clasification. Therefore, this manuscript can be considered for publication in this journal.
Also, there are some disadvantages that should be discuss in the future. After the reading this manuscript, the impression remains that it is still sufficient to use the LCZ Generator (WUDAPT), and that GeoClimate does not provide any novelty in defining the LCZ. Maybe for authors it was not the main goal, but maybe readers will expect to see new tool that should be better than old ones. As it was highlighted in the Conclusions...the integration of GeoClimate and WUDAPT tools could make significant improvements in furhter LCZ classification and this should be a next step of the authors.
Citation: https://doi.org/10.5194/egusphere-2023-371-RC1 -
AC1: 'Reply on RC1', Jérémy Bernard, 04 May 2023
Thank you to anonymous referee #1 for the comments.
Concerning the discussion about WUDAPT and GeoClimate comparison, as the referee assumed, it is not the purpose of this manuscript. The GeoClimate LCZ algorithm has been available for the community since several years so it was needed to:
• describe clearly what was the methodology used to determine the LCZ of a given area
• evaluate what would be the difference in using the worldwide available OSM dataset instead of the French BDTopo one.
Some preliminary works (such as in Blond et al. (2023)) have been achieved to compare GeoClimate to WUDAPT on some French cities but they have not been published yet. The main rough observation is that GeoClimate seems more appropriate than WUDAPT for urban areas but less appropriate for rural areas.
Blond, N., Breton, F., Micolier, A., and Mendez, M.: A modeling approach to address building energy consumption and thermal comfort under urban climate change , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14169, https://doi.org/10.5194/egusphere-egu23-14169, 2023.Citation: https://doi.org/10.5194/egusphere-2023-371-AC1 -
RC3: 'Reply on AC1', Anonymous Referee #1, 15 May 2023
Dear authors,
OK. For me, your replies are acceptable. I will not have any comments.
Citation: https://doi.org/10.5194/egusphere-2023-371-RC3
-
RC3: 'Reply on AC1', Anonymous Referee #1, 15 May 2023
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AC1: 'Reply on RC1', Jérémy Bernard, 04 May 2023
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RC2: 'Comment on egusphere-2023-371', Jan Geletic, 10 May 2023
In general, I completely agree with the first reviewer. Manuscript is well written and the introduction to the concept of LCZs is fine. Honesty, I expected more innovations and discussion of problems you found. I can imagine problems related to development, becuase I also developed a LCZ generator based on national dataset(s).
Related to that I have 3 major questions:
- You mentioned the widely known LCZ generator, WUDAPT. There is no comparison and I fully accept it (as you stated in reply). But which benefits can we could expect if your algorithm will be used? Is it better, faster, integrated... This study is focused on French cities only, so I am not sure.
- There were presented and discussed differences between BDT and OSM. But which layer is more precise? Did you somehow validate results with reality (e.g., using manually defined samples etc.)? How accurate algorithm is?
- Geodatabase in OSMs is based on community - quality is definitely not the same worldwide. Moreover, there is much more data available for OSM editors. I am quite skeptical of the trustworthiness of OSM in some parts of the world. Despite that fact, in Europe exists freely-available sources with a strong potential for delineation methods - Copernicus Land Monitoring Services. Do you know about these datasets with a spatial resolution in tens of meters (e.g., Urban Atlas, European Settlement Map, Imperviousness etc.)? It would be interesting novelty...
Citation: https://doi.org/10.5194/egusphere-2023-371-RC2 -
AC2: 'Reply on RC2', Jérémy Bernard, 17 May 2023
Thank you to Jan Geletic for his review and comments. As an answer to your general comment, we would like to highlight that our main contribution to the field is having a generic vector-based method to calculate the LCZ and that this algorithm is available by anyone. Then any dataset can be used to calculate the LCZ using this method. The main goal of the manuscript is then to describe the detailled methodology used by the GeoClimate LCZ algorithm. Then the application of the algorithm is illustrated using BDT and OSM datasets. Our manuscript thus falls into the GMD manuscript type called "Model description papers" (https://www.geoscientific-model-development.net/about/manuscript_types.html).
Below are answers to Jan Geletic three major comments.
- For many European and North America cities, we expect a better identification of urban LCZ types using GeoClimate and OSM than using LCZ generator since the degree of completeness of OSM is rather good for building geometry and building types in these regions and also OK concerning building height (see Bernard et al., 2022). However, LCZ generator is probably more appropriate for rural areas since many small tree vegetation patches or types might be missing in OSM. Moreover, there is still a big lack of OSM data such as in many African, Asian or South American countries. As described in Bocher et al. (2021), GeoClimate algorithms can roughly be distinguished in two steps: first data are imported into a well defined and generic data model; second all indicators and classifications are performed using the data previously included in the generic model. Thus GeoClimate might still be used if local vector dataset exist. However, the first step will still be needed to be performed.
-
As discussed in the manuscript section 2.5, the main expectations we may have about the differences between OSM and BDT data are that:
- BDT building height is more accurate than the OSM one since for most of the OSM buildings, this information is simply estimated using a random forest model using BDT as real value for the training (cf. Bernard and al., 2022).
- OSM data has a generally higher land coverage which is mainly due to a better representation of impervious area and vegetation within cities and also a higher building coverage. This information has been verified in our study and is discussed section 3.3. However, the statistics given have been inverted and will be corrected in a next version (37% in BDT against 55% in OSM).
We have not verified the data to manually defined samples in this study since the comparison of the two datasets was not the objective of this manuscript (thus the datasets are compared relatively to each other).
What does the reviewer mean by algorithm accuracy ? Accuracy of the LCZ algorithm in general ? This accuracy is not discussed in the article since it always mean that a reference data should be found and that this reference data may also have its shortcomings. As answered to the #Anonymous referee 1, in the near future, we aim at comparing the GeoClimate tool results to manual classifications or remote sensing methods such as LczGenerator. If by accuracy Jan Geletic means the accuracy of the UCP used for LCZ classification, there are limitations which depends on each UCP. For example for the SVF calculation, more information about the accuracy can be found in Bernard et al. (2018) but the algorithm is limited in a way that it only consider flat roofs and no trees. The accuracy of each UCP is then really UCP dependent. - As previously discussed, there is indeed almost no OSM data in some part of the world and thus OSM is not applicable there yet. However, as previously explained also, GeoClimate is a two steps approach where the first step concerns data import into the generic GeoClimate data model. Thus some other datasets can be merged during this first step (even though it might mean a considerable amount of work depending on data completeness) and then be used in the presented GeoClimate algorithm (second step of the GeoClimate processing chain). But the first objective of this article is to share the methodology used by GeoClimate to identify the LCZ (so the second GeoClimate step), not to show combination of data that can be used in GeoClimate (first GeoClimate step). However we have illustrated the behavior of the algorithm depending on two different datasets. In the future, a potential work could indeed be to try to merge different datasets to obtain the best land coverage and thus LCZ classification but this has to be done in a future work, this one being the description of the LCZ methodology used by GeoClimate.
Citation: https://doi.org/10.5194/egusphere-2023-371-AC2 -
RC4: 'Reply on AC2', Jan Geletic, 18 May 2023
Dear authors,
Thank you for your reaction and references. Points 1 and 3 are fine for me.
Further explanation for point 2 is still needed. As you stated, your paper was submitted as ‘Model description paper’. See, please, its detailed definition on GMD website, specifically point below:
Examples of model output should be provided, with evaluation against standard benchmarks, observations, and/or other model output included as appropriate. In this respect, authors are expected to distinguish between verification (checking that the chosen equations are solved correctly) and evaluation (assessing whether the model is a good representation of the real system). Sufficient verification and evaluation must be included to show that the model is fit for purpose and works as expected. Where evaluation is very extensive, a separate paper focussed solely on this aspect may be submitted.
Can you, please, select one of the cities you classified and compare it with an expert-based classification? Or with a WUDAPT method? This information is important for a potential user; without this information you cannot state that model provides relevant or sufficient results. Attached is a manually classified sample for Brno in the Czech Republic, if you have no own sample.
With respect,
Jan Geletic
-
AC3: 'Reply on RC4', Jérémy Bernard, 24 May 2023
Dear Jan Geletic,
Thank you for your quick reaction.
You are waiting for further explanation concerning the lack of evaluation of the method. We understand and agree that comparing the GeoClimate results to other state-of-the art methods is necessary and as previously said as answer to your “major comment 1”, we will make this comparison in an early coming future.
You state that as a “Model description paper”, our manuscript must contain an evaluation section. You refer to a paragraph where the word “should” is used. Thus this paragraph is not necessarily applicable (below is the distinction made by GMD between "should", "must" and "may", this paragraph being located at the begining of the “manuscript type” page of GMD).
'In the following, "must" means that the stated actions are required, and the paper cannot be published without them; "should" means that we encourage the action, but papers can still be published if the criteria are not met; "may" means that the action may be carried out by the authors or reviewers, if they so wish.'
We have made our best to fullfill all the “must”, most of the “should” and some of the “may” but we have not fullfilled the evaluation part which is a “should”. Concerning this one, we need to reaffirm our position. There are two objectives within this manuscript: the first (the main one) aims at describing accurately what is performed within the GeoClimate LCZ algorithm; the second is to illustrate the differences obtained using two datasets that are currently automatically usable with GeoClimate. Still, the “evaluation” of GeoClimate using the LCZ map produced by an other method on a single territory could be performed and added to the manuscript as you proposed. However, this involves:
- A third objective to our article (or at least a new section) which might make the manuscript hard to follow and too long
- Having the LCZ map of a French city since the article focus on French cases
- The results of the “evaluation” (comparison) would be valid only for this single territory. Thus it will only be a sort of illustration more than an evaluation or an interesting comparison where general conclusions can be made.
In order to have a proper evaluation, a solution would be to compare the GeoClimate method to an other one which has been applied to many different French locations. However, this raises two issues:
- The manuscript would get really long
- For now and at our knowledge, only the WUDAPT method has been applied at such scale and as previously discussed in our answer to your “major comment 1”, we have planned to do this comparison in a separate article
Our point of view is that this manuscript is a good base for future comparisons involving the GeoClimate algorithm. This first article describes the algorithm and the limitations of using one of the two datasets (BDT or OSM). As a consequence, for French applications, the current optimized dataset would be to use a combination of OSM and BDT. Moreover, we have now evaluated the limitation of using the OSM data (which miss the building height) thanks to a reference dataset (BDT where building height accuracy is known). This preliminary knowledge was at our point of view necessary before to evaluate further the GeoClimate method using OSM on other territories than France.
Citation: https://doi.org/10.5194/egusphere-2023-371-AC3
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AC3: 'Reply on RC4', Jérémy Bernard, 24 May 2023
Peer review completion
Journal article(s) based on this preprint
Data sets
GeoClimate 0.0.1 LCZ calculation: Code and data Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, Élisabeth Le Saux Wiederhold, and François Leconte https://zenodo.org/record/7687911
Model code and software
GeoClimate: a Geospatial processing toolbox for environmental and climate studies Erwan Bocher, Jérémy Bernard, Élisabeth Le Saux Wiederhold, François Leconte, Gwendall Petit, Sylvain Palominos, and Camille Noûs https://zenodo.org/record/6372337
lczexplore : an R package to compare different local climate zone classifications on same geographical areas Matthieu Gousseff https://zenodo.org/record/7646866
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Cited
Erwan Bocher
Matthieu Gousseff
François Leconte
Elisabeth Le Saux Wiederhold
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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