## 5 Conclusions  
The experiments performed in this work showed an underlying relationship between the land cover composition and the air temperature for the chosen study area. The analysis of the average temperature patterns provided also a metric to quantify this relationship in terms of observed temperature offsets between different LCZs. Data availability was commensurate to the expectation of the analysis. The combined use of open data with a worldwide coverage, such as Landsat 8 and Sentinel-2 imagery, with traditionally available datasets such as air temperature observations, makes it possible to replicate the analysis in almost all major cities on the Earth. Nevertheless, it must be said that an enormous amount of high-accuracy, high-frequency and high-resolution environmental data which is potentially key for studying, understanding and solving climate-related issues is only managed by private companies, with no or limited access to both the research and public community. At least at the European level, some efforts are however underway towards the preparation of policies aiming to promote and facilitate the open release of private data [60]. The use of FOSS technology was an asset for both data processing and analysis. This proves the maturity, completeness, and reliability of such technology for geo-data management [61], since flexibility and customization (e.g., in data modeling) are combined with the sustainability in terms of technology costs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Satellite** & **Period** & \begin{tabular}{c} **Majority** \\ **Class ID** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Buffers** \\ \end{tabular} &
\begin{tabular}{c} **Median \({}^{\circ}\)C** \\ **Deviation \({}^{\circ}\)C** \\ \end{tabular} \\ \hline \multirow{3}{*}{Landsat 8} & \multirow{3}{*}{Non-Vegetative} & 1 & 13 & 10.59 & 1.2 \\  & & 2,3,4,5 & 23 & 9.60 & 1.5 \\  & & 6 & 0 & / & / \\  & & 7 & 12 & 8.98 & 0.8 \\ \hline \multirow{3}{*}{Landsat 8} & \multirow{3}{*}{Vegetative} & 1 & 11 & 21.97 & 0.9 \\  & & 2,3,4,5 & 23 & 21.46 & 1.1 \\  & & 6 & 0 & / & / \\  & & 7 & 14 & 20.84 & 0.7 \\ \hline \multirow{3}{*}{Sentinel-2} & \multirow{3}{*}{Non-Vegetative} & 1 & 18 & 5.46 & 1.2 \\  & & 2,3,4,5 & 14 & 4.68 & 1.5 \\  & & 6 & 0 & / & / \\  & & 7 & 16 & 3.93 & 0.9 \\ \hline \multirow{3}{*}{Sentinel-2} & \multirow{3}{*}{Vegetative} & 1 & 12 & 19.15 & 1 \\  & & 2,3,4,5 & 21 & 18.58 & 1.1 \\  & & 6 & 2 & 16.69 & / \\  & & 7 & 13 & 18.08 & 0.6 \\ \hline \multirow{3}{*}{RapidEye} & \multirow{3}{*}{Non-Vegetative} & 1 & 12 & 11.02 & 1.7 \\  & & 2,3,4,5 & 18 & 9.97 & 0.9 \\  & & 6 & 0 & / & / \\  & & 7 & 18 & 9.02 & 1.1 \\ \hline \multirow{3}{*}{RapidEye} & \multirow{3}{*}{Vegetative} & 1 & 13 & 21.97 & 0.9 \\  & & 2,3,4,5 & 17 & 21.30 & 1.2 \\ \cline{1-1}  & & 6 & 1 & 19.53 & / \\ \cline{1-1}  & & 7 & 17 & 20.90 & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of LCZ classes effects on air temperatures. Departing from the urban environment, the establishment of good practices in environmental policy-making by leveraging the use of available geo-data represents the ultimate goal of the presented work. The proposed analysis proved to be promising for all those applications requiring insights into the human impact on relevant climate-related variables such as the air temperature. These include urban planning and design, land policies impact assessment, and urban climate monitoring, among others. Conceptualization, D.O. and G.R. ; Data curation, D.O., G.R., M.E.M. and M.L. ; Methodology, D.O., G.R., M.E.M. and M.L. ; Project administration, M.A.B. ; Software, D.O. and G.R. ; Supervision, G.S. and M.A.B. ; Validation, D.O., G.R., M.M. and G.S. ; Writing--original draft, D.O., G.R. and M.M. ; Writing--reviewed manuscript, D.O., G.R. and M.M. This work was partially supported by URBAN-GEO BIG DATA, a Project of National Interest (PRIN) funded by the Italian Ministry of Education, University and Research (MIUR) -- ID. 20159 CNLW8.