# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
## 1 Introduction
### Related Work
created a novel wildfire assessment model (WAM) based on a residual-style convolutional network architecture [17]. It performs regression over atmospheric variables and the greenness index, and is able to predict necessary resources, the control and extinction time, and the expected burnt surface area. This model is useful for anticipating the economic and ecological impact of a wildfire, assisting managers in resource allocation and decision-making. The automatic identification of anthropogenic objects from aerial images and the computation of buffer zones has been investigated by several studies. [PERSON] et al. [18] demonstrated that it was possible to detect individual buildings within the wildland-urban interface using semi-automated Object-Based Image Analysis (OBIA) approach, which utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery. However, this kind of imagery has low resolution (50 cm to 100 cm per pixel), and their approach struggles to detect smaller buildings. Moreover, their study calculates buffer zones around buildings but does not evaluate the presence of trees or vegetation within these buffer zones. In their conclusion, they point out that using artificial neural networks could be a better approach for detecting buildings from aerial imagery. The vulnerability of wildland-urban interfaces has also recently gained attention from researchers, as they pose significant risks to lives and property. [PERSON] et al. studied seven fire case studies in central Chile, using satellite and drone imagery and GIS-based analysis of the collected data [19]. They concluded that spatial arrangement factors like distance to vegetation have a greater impact on damage prediction than the structural conditions and fire preparedness of individual buildings. [PERSON] et al. studied the general effectiveness of buffer zones in the southern Spain wildfires (20). They concluded that buffer zones were indeed effective in containing the fire, especially when supported by ground and aerial firefighting. This confirms the importance of buffer zones around buildings and roads. [PERSON] et al. proposed a methodology to detect the continuity of vegetation near roads, based on aerial LiDAR point clouds, in combination with point cloud processing techniques (21). Their study emphasises the importance of forest management around roads in preventing forest fires and mitigating their effects. The main gap in this body of knowledge is that these studies focus on partial methodologies of the topic of this manuscript, but no study integrates all these methodologies into a complete system (from planning the drone flight to the final report) to verify compliance with mandatory fuel management in buffer zones. There is also a specific gap mentioned by [PERSON] et al. (2018): The automatic detection of building contours using CNN methodologies. Finally, none of these studies explored methodologies for automatically delineating the boundaries of multi-layer buffer zones, as defined by the Portuguese legislation for the protection of buildings.