# Automated Tree Detection Using Image Processing and Multisource Data
## 6 Conclusions  
In this study, we developed an automatic method for detecting and evaluating trees in urban environments using image-processing and data integration techniques. By combining various data sources, including maps, vegetation indices, and numerical land data, we successfully identified and mapped trees within the city of Katowice, Poland. Our approach not only improves the efficiency of urban tree management processes but also supports sustainable urban development by providing accurate and up-to-date information on vegetation coverage. The results demonstrated that our method could effectively identify areas that are assumed to have a high vegetation cover but are actually lacking sufficient tree density. This information is crucial for urban planners and decision makers who want to expand green spaces and improve ecological balance within urban settings. Additionally, our \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Overlapping Metrics** & \multicolumn{3}{c}{**Statistical Metrics**} \\  & **Jaccard** & **CC** & **Accuracy** & **Precision** \\ \hline
6.127.29.03.1 & 0.4219 & \(-19.352\) & 67.20\% & 45.80\% \\
6.130.29.24.4 & 0.0795 & \(-987.421\) & 74.20\% & 8.40\% \\
6.130.29.24.2 & 0.1199 & \(-601.697\) & 58.60\% & 12.50\% \\
6.130.29.20.2 & 0.1099 & \(-627.342\) & 56.60\% & 12.10\% \\
6.130.29.14.4 & 0.4181 & \(-36.385\) & 74.90\% & 42.30\% \\
6.130.30.11.2 & 0.7055 & \(-1085.412\) & 88.38\% & 7.78\% \\
6.129.30.17.1 & 0.5432 & 30.962 & 75.29\% & 59.16\% \\
6.129.29.25.3 & 0.2333 & \(-219.625\) & 63.08\% & 23.83\% \\
6.129.29.14.3 & 0.0000 & \(\infty\) & 28.80\% & 0.00\% \\
6.128.29.22.2 & 0.0000 & \(\infty\) & 32.19\% & 0.00\% \\
6.128.29.20.3 & 0.2895 & \(-142.521\) & 45.55\% & 29.20\% \\
6.128.29.05.4 & 0.6245 & 44.007 & 72.05\% & 64.11\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example comparison of color-based and color/height-based algorithms for selected datasets, focusing on overlapping and statistical metrics. methodology can be extended to label other objects on maps, such as buildings or roads, by integrating additional data sources. Automatic detection and assessment of trees and wooded areas are essential steps toward intelligent urban environment management. Using modern technologies such as image analysis, machine learning, and geospatial data processing can significantly accelerate and improve the monitoring, assessment, and protection of urban vegetation. This contributes to creating greener, more resident-friendly cities, enhancing the quality of life, protecting biodiversity, and mitigating the adverse effects of climate change. Future work could involve applying this method to other urban areas, integrating additional data sources such as hyperspectral imagery, or refining algorithms to further improve detection accuracy. By continuing to develop and implement these innovative technologies, we can contribute to the construction of more sustainable and environmentally friendly urban spaces, ultimately preserving valuable ecosystems for future generations. Conceptualization, G.D., B.P., P.J., P.S., T.J., S.G. and J.K.; Methodology, G.D., B.P., P.J., P.S., T.J. and J.K.; Software, G.D., P.J., P.S., T.J. and S.G.; Validation, G.D., P.J., P.S. and T.J.; Formal analysis, B.P. and P.J. ; Investigation, G.D., B.P. and P.J. ; Resources, G.D., B.P., P.J., P.S., T.J. and S.G.; Data curation, G.D., B.P., P.J., P.S. and S.G.; Writing--original draft, G.D., B.P., P.J., P.S., T.J., S.G. and J.K.; Writing--review & editing, G.D., B.P., P.J. and J.K.; Visualization, G.D., P.J., P.S., T.J. and S.G.; Supervision, B.P. and J.K.; Project administration, B.P. and J.K.; Funding acquisition, B.P. and J.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.