# Automated Tree Detection Using Image Processing and Multisource Data
## 2 Related Works  
As a key element of public administration at the local level, urban planning demands extensive analysis related to an entity's current and future actions. These actions are undertaken within the current cycles of each unit, but the scientific community also seeks to contribute to this field. In the literature, many studies focus on analyzing green spaces in cities. A significant portion of these publications discusses the impact of trees and vegetation on urban temperatures [24; 25; 26]. There are also studies dedicated to distinguishing and classifying individual trees or plants, as seen in [25]. Additionally, other studies include computer simulations for planning the placement of trees in parking lots by considering climatic and spatial parameters, as well as the need to optimize this process [25]. This topic has been relevant for many years, as demonstrated in publications such as [27], among others. It is also a global issue that has been addressed with examples from many cities and regions, including Los Angeles [24], Manchester [26], Tokyo [27], and the United States [28]. The authors of these studies emphasize the importance of green spaces in cities due to their positive impact on the environment and their utilitarian or economic benefits to society. The automatic detection and estimation of individual tree attributes using remote sensing data have become crucial for the efficient monitoring of urban green spaces. In [29], the authors utilized machine learning techniques, specifically the Random Forest algorithm, to predict individual tree attributes from airborne laser scanning data. Their study demonstrated that machine learning could effectively estimate tree height and diameter at breast height (DBH), which are essential parameters for forest inventory and urban planning. Moreover, [PERSON] et al. [7] proposed a method for individual tree detection and classification using UAV-based photogrammetric point clouds and hyperspectral imaging. Their approach allows for precise mapping and classification of trees, leveraging the high-resolution data obtained from UAVs and the spectral information from hyperspectral sensors. This method is particularly useful for urban environments, where traditional ground-based surveys are challenging. The growing popularity and effectiveness of artificial intelligence have enabled the application of machine learning models in urban planning. Activities that were previously manual and often field-based can now be automated or virtualized. This approach increases efficiency and can cover much larger areas. The research presented in [30] serves as an example of the use of machine learning in urban planning. By employing Gaussian Process Regression, the authors developed a model to aid urban planners and decision makers in managing urban spaces. The ability to simulate a complex urban area facilitates relatively straightforward and rapid comparisons of development scenarios and their impact on urban space sustainability. Another issue that has been addressed is the recognition of different tree types in urban areas. Researchers have employed deep learning and computer vision techniques with data from Street View to classify and measure tree clusters [22]. Classification models in urban planning have found applications beyond urban areas. For instance, the authors of [31] presented results based on various individual and ensemble classification models to map wetland vegetation types. The best performance was achieved with the Random Forest, eXtreme Gradient Boosting, and Support Vector Machine algorithms. In [28], the classification of image data was based on block image analysis, where areas were analyzed pixel by pixel, each measuring 10 km \(\times\) 10 km. The proposed approach accounts for a pixel's position within the entire frame, distinguishing between edge and interior pixels. The algorithms tested included Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Bagging, Artificial Neural Networks, and deep neural networks.