Abstract. Information describing the elements of urban landscapes is required as input data to study numerous physical processes (e.g., climate, noise, air pollution). However, the accessibility and quality of urban data is heterogeneous across the world. As an example, a major open-source geographical data project (OpenStreetMap) demonstrates incomplete data regarding key urban properties such as building height. The present study implements and evaluates a statistical approach that models the missing values of building height in OpenStreetMap. A random forest method is applied to estimate building height based on a building’s closest environment. A total of 62 geographical indicators are calculated with the GeoClimate tool and used as independent variables. A training dataset of 14 French communes is selected, and the reference building height is provided by the BDTopo IGN. An optimized random forest algorithm is proposed, and outputs are compared with an evaluation dataset. At building scale for all cities, at least 50 % of the buildings have their height estimated with an error of less than 4 m (the cities' median building heights range from 4.5 to 18 m). Two communes (Paris and Meudon) demonstrate building height results that deviate from the main trend due to their specific urban fabrics. Putting aside these two communes, when building height is averaged at a regular grid scale (100 m×100 m), the median absolute error is 1.6 m, and at least 75 % of the cells of any city have an error lower than 3.2 m. This level of magnitude is quite reasonable when compared to the accuracy of the reference data (at least 50 % of the buildings have a height uncertainty equal to 5 m). This work offers insights about the estimation of missing urban data using statistical methods and contributes to the use of open-source datasets based on open-source software. The software used to produce the data is freely available at https://doi.org/10.5281/zenodo.6372337 (Bocher et al., 2021b), and the dataset can be freely accessed at https://doi.org/10.5281/zenodo.6855063 (Bernard et al., 2021).

Human activities induce changes on land use and land cover. These changes are most significant in urban areas where topographic features (e.g., building, road) affect the density of impervious surface areas and introduce a range of urban morphological patterns. Those characteristics impact the energy balance and modify the climate locally (e.g., inducing the so-called Urban Heat Island phenomenon).

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.

Abstract. Information describing the elements of urban landscape is a required input data to study numerous physical processes (e.g climate, noise, air pollution). However, the accessibility and quality of urban data is heterogeneous across the world. As an example, a major open-source geographical data project (OpenStreetMap) demonstrates incomplete data regarding key urban properties such as building height. The present study implements and evaluates a statistical approach which models the missing values of building height in OpenStreetMap. A Random Forest method is applied to estimate building height based on building’s closest environment. 62 geographical indicators are calculated with the GeoClimate tool and used as independent variables. A training data set of 14 French communes is selected, and the reference building height is provided by the BDTopo IGN. An optimized Random Forest algorithm is proposed, and outputs are compared with an evaluation dataset. At building scale for all cities, at least 50 % of the buildings have their height estimated with an error being less than 4 m (the city median building height ranges from 4.5 m to 18 m). Two communes (Paris and Meudon) demonstrate building height results out of the main trend due to their specific urban fabric. Putting aside these two communes and when building height is averaged at regular grid scale (100 m × 100 m), the median absolute error is 1.6 m and at least 75 % of the cells of any city have an error lower than 3.2 m. This level of magnitude is quite reasonable when compared to the accuracy of the reference data (at least 50 % of the buildings have an height uncertainty equal to 5 m). This work offers insights about the estimation of missing urban data using statistical method and contributes to the use of open-source data set based on open-source software. The software used to produce the data is freely available at https://zenodo.org/record/6372337 and the data set can be freely accessed at https://zenodo.org/record/6396361.