## 3 Approach
### Selection, Application, and Adaptation of AI Methods for Position and Species Determination
#### 3.2.2 Positioning Challenges  
Two quality measures of AI applications are accuracy and coverage. In this context, accuracy refers to capturing as many trees as possible while minimizing errors. For example, no trees should be detected where none exist (false positives, FP), which may result from misclassification of another object or noise in the data. Conversely, no trees should be overlooked. False negatives (FN) may occur when image quality is poor or environmental conditions are unfavorable. As many trees are already missing from the municipal tree cadastre, prioritizing high coverage is desirable. Erroneously detected trees can be promptly removed from the dataset by volunteers in the field using a web application for editing the tree attributes. In contrast, adding unrecognized trees is more complex, requiring accurate GPS coordinates. For pragmatic reasons, urban tree cadastres normally only include trees on public land for which the city is responsible for maintenance and safety compliance. AI, on the other hand, detects all trees in the given images, including those on private land. This is particularly relevant for use cases dealing with shading and air quality [1], to show the ratio of 'high emitter' to 'low emitter' plants. A misalignment in ratios could indicate that it might be contraindicated to have numerous tree species with high isoprene emission potential. Likewise, when comparing new planting with climate-adapted construction throughout the urban area, the predominant species need to be considered at the planning stage to prevent such an imbalance. Consequently, a complete tree cadastre could help identify the locations of potential biogenic isoprene sources at an early stage and develop strategies to optimize the air quality situation. The employed deep learning model encounters challenges in detecting individual trees when they are densely populated, as is often the case in forests. Generating sufficiently high-quality training data by manually annotating these densely packed tree patches also proves to be challenging. For this reason, these areas, primarily located on the outskirts of the city, were initially masked. This prevented the model from being trained on incorrect information and enhanced the usability of the results. It is anticipated that accuracy and recall will improve if smaller groups of trees in the urban area are also initially masked and  
Figure 6: Air imagery of Kaiserslautern. Forest-like areas are masked in the data preprocessing. the model is applied exclusively to individual trees. Those masked tree groups are being examined using other, more appropriate methods. There are some approaches that can provide added value and additional information on tree clusters without having to manually record every single tree and its species:  
* Aerial imagery from different seasons can be used to estimate the proportion of deciduous and coniferous trees within a group of trees [20]. * There are approaches to calculating the total number of trees in a forested area, employing techniques known as crown segmentation. Although these methods do not provide precise locations, they infer that the location falls within the crown, indicating a specific area. However, the accuracy of the results is greatly influenced by the condition of the forest, with dense areas being particularly prone to distortion [20]. * The German forestry offices record the main tree species for each forested area. Existing approaches calculate these species classifications from satellite images [20]. The applicability of those models to groups of trees on the urban fringe and in the city needs further investigation. * [PERSON] et al. [21] studied forest areas in Croatia with the three main tree species: oak, hornbeam, and black alder. [PERSON] et al. [20], on the other hand, differentiated between multiple main tree species: beech, oak, larch, and other broadleaves among deciduous trees, and spruce, pine, and Douglas fir among evergreen trees.