# Automated Tree Detection Using Image Processing and Multisource Data
The spatial data and accompanying maps for these areas often exemplify the characteristics of Big Data, where the primary concerns extend beyond sheer volume and complexity to include the absence of a standardized format. Considering the factors mentioned above, the implementation of a complex system capable of processing information and generating simple, easy-to-follow recommendations could greatly benefit local administrative units responsible for urban planning and development. Such a system could serve as the initial phase in the development of a comprehensive recommendation framework capable of conducting thorough analyses and providing both numerical and descriptive information. Therefore, the main objective of this study is to develop an automatic method for detecting and assessing trees in urban areas using image-processing and data integration techniques. This method aims to enhance the efficiency of urban tree management processes and support sustainable urban development. The results presented in this article stem from a collaborative project with the City of Katowice, Poland. Throughout our collaboration with the capital of the Upper Silesian Industrial Region, we identified the needs of our partner and the available data required to achieve our objectives. Specifically, the project aimed to assess the developmental status and density of vegetation, particularly trees. Our research focused on automatically analyzing available spatial data to evaluate tree objects within the urban environment. The project involved analysis of various maps and spatial data. The following types of data were used:  
* A numerical terrain model, such as the digital terrain model (DTM), representing the Earth's surface and acquired using remote sensing techniques like Light Detection and Ranging (LiDAR);
* A numerical land-cover model, including information related to specific land-cover classes such as trees, meadows, and buildings;
* Color Infrared data (CIR remote sensing data), used mostly for analyzing the quality of vegetation;
* Geographic Tagged Image File Format (GeoTIFF), a combination of TIFF data and geospatial metadata;
* Transformation World File data (TFW data), including georeferencing information. The research presented in this article was carried out in cooperation with the Department of Urban Planning of the City of Katowice in Poland. During the realization of the research, internal data of the City Hall were made available, which are collected systematically and have served as the basis for manual analysis by the Department's employees for many years. In accordance with the European Union's directives, the city systematically updates the data used in the work of the Office. Our goal is to automate some of the processes. Surveys are carried out at a frequency of twice a year to once every two years, depending on the type of data. All data used in the survey serve the work of the Department, so they are already pre-processed and systematized. The NDVI index is a basic criterion for analyzing plant vegetation. However, it is a physical measure susceptible to interference and noise. The data with which this study was carried out were collected by a company contracted by the Katowice City Hall in a systematic and methodical manner. The primary goal was to collect the data within a short window to minimize vegetation differences across seasons. The contracted company carried out data processing, including the division of the collected data into regions. However, the main limitation related to this study's NDVI index is that the results could differ based on the year and season in which the data were collected. Additionally, the timing of data collection was strictly regulated between the company and the administrative unit. The data were combined to develop a classification system that assigns classes to specific, small-area fragments. In collaboration with the Katowice administration unit, particular emphasis was placed on classifying high vegetation.