# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
## 4 Discussion  
The main objective of this study was to check whether inexpensive off-the-shelf drones equipped with standard RGB cameras could be used to detect the excess of trees and vegetation in buffer zones around buildings and roads, as defined by Portuguese law. To accomplish this wider goal, several smaller challenges had to be tackled. The first one was related to the input of the CHAMELON bundles, which require a high-resolution orthophoto map and a high-quality 3D point-cloud file. Therefore, the system had to be able to create these files from a mosaic of aerial RGB images captured by the drone. This was accomplished by using the ODM software, which easily created high-quality orthophoto maps from almost any type of image mosaic, as long as the images were sufficiently overlapped. Creating high-quality 3D point clouds of trees and vegetation with ODM proved more complicated, requiring several adjustments until the results were satisfactory. The best results were obtained with east-west sweeps, strong image overlap, and a gimbal pitch of several degrees off-nadir. Even the best results were not perfect: Some trees were not correctly represented, especially in areas with a high density of similar trees. Using a methodology that fuses photogrammetry and LiDAR data [26] would probably lead to better results and is something worth exploring to increase the accuracy of tree detection. In addition, LiDAR data could be used to create a digital terrain model (DTM) [27], which would serve as a ground reference for detecting trees from crown images. However, LiDAR sensors for drones are still very expensive: the entry-level LiDAR sensor from a popular drone brand costs EUR 14,520, while a high-quality RGB drone from the same brand can cost just over EUR 1100. This substantial cost difference highlights the importance of continuing to explore and improve the best methods of detecting trees and vegetation using only RGB sensors. Another challenge that the system had to overcome was the automatic recognition of buildings using AI and the automatic definition of buffer zones around buildings and roads. The automatic segmentation of buildings was facilitated by using the YOLO segmentation model, which is easy to train and use. However, the AI model is not infallible, and various types of errors are occasionally generated: false positives (when a structure is identified as a building, but it is not a building), false negatives (when a building is not identified as such) and contour errors (when the polygon generated by the model does not exactly match the contours of the building). Although the software allows the user to manually correct the gross errors produced by AI segmentation, if small errors in the building's contour are ignored, this will lead to error propagation in the calculation of buffer zones. This error propagation is linear, because the boundaries of the buffer zones are defined as a linear distance from the closest point on the building's contour. After the segmentation of buildings (using AI) and roads (manually), the automatic definition of buffer zones was implemented in a separate module, using standard geospatial libraries. This modular approach will allow the incorporation of different modules to calculate the boundaries of buffer zones in other countries, according to local legislation and requirements. The representation of the buffer zones over the high-resolution orthophoto map, without any additional data, already provides valuable insights to the user. In fact, using drones to capture photos from the ground allows for high-resolution orthophoto maps (1 cm per pixel is easily achievable), where vegetation and trees are then easily spotted by a human user. However, the system is able to automatically identify the offending vegetation and trees within the buffer zones, thanks to the information extracted by the CHAMELON bundles from the 3D point cloud files.