# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
## 1 Introduction
### Related Work  
The use of remote sensing technologies plays an important role in fire ecology, including risk mapping, fuel mapping, and active fire detection [5]. [PERSON] et al. [7] have shown that drones present several advantages when compared with traditional remote sensing platforms, like satellites and manned aircraft, namely, better spatial and temporal resolutions and lower costs. They concluded that drones with LiDAR and RGB sensors are going mainstream and their importance for decision support is becoming increasingly relevant for researchers and foresters, as well as related business professionals. They also confirmed that LiDAR and photogrammetry techniques are both feasible solutions for measuring and monitoring aspects of complex forest structures. [PERSON] et al. [8] confirm that drones equipped with RGB sensors are a cost-effective option for collecting high-resolution 3D point clouds of forests. They also state that forest fire monitoring and management, as one aspect of integrated forest protection, was one of the first fields that confirmed the importance of drones in forestry applications. While using photogrammetry techniques based on RGB sensors proved to be a cost-effective way of collecting information, LiDAR techniques provide better results, as [PERSON] et al. have demonstrated [9]. They used LiDAR point clouds with an ultra-high resolution in the characterization of forest fuels for further wildfire prevention  
Figure 3: Mandatory buffer zones around roads. and management, allowing for the detection, measurement, and characterization of individual trees and even shrubs. They applied the LiDAR-based methodology to characterize the forest fuels in a wildland-urban interface and along infrastructures, for wildfire protection purposes. Their study also describes the buffer zones that are mandatory in Spain, which are different from those defined in Portuguese law. [PERSON] et al. corroborate that using high-density point clouds has the potential to estimate vegetation metrics with high accuracy [10]. Their study demonstrates the relative ability of using remotely piloted aerial systems (RPAS) LiDAR, digital aerial photogrammetry (DAP) and mobile laser scanning (MLS) point clouds to estimate key canopy and surface fuel metrics used to model fire behaviour. AI is also being widely used in this field: [PERSON] et al. have pointed out how the use of AI, UAV, and deep learning models has created an unprecedented momentum to implement and develop more effective wildfire management [11]. Identifying the type of biomass to estimate the risk of fire propagation in wildland-urban interfaces is one of the topics that could benefit from AI methodologies. [PERSON] et al. were able to use a drone with a custom sensor payload with LiDAR, stereo cameras, inertial measurement unit (IMU), and a multispectral camera to identify several types of biomass (fuel), using a semantic segmentation model [12]. They concluded that their system provides a comprehensive solution for forest monitoring with drones, enabling accurate data collection and mapping for effective forest management and conservation. [PERSON] et al. [13] have demonstrated how a deep-learning image segmentation method, using a mask recursive convolutional neural network (R-CNN), can be used to detect and identify trees in a dense forest. [PERSON] et al. were also able to use Convolutional Neural Networks (CNN) with a segmentation network architecture (U-Net) to detect shrubs in heterogeneous landscapes [14]. [PERSON] et al. went even further: they have successfully used a consumer drone with a multispectral camera to classify shrub and tree species in Mediterranean forests, using point clouds obtained from UAV-based digital aerial photogrammetry [15]. [PERSON] et al. have successfully employed an artificial bee colony-adaptive neuro fuzzy inference system (ABC-ANFIS) to evaluate the risk of wildfires in Dak Nong, a province located in the highland region of Vietnam [16]. [PERSON] et al.