Wildfires are expected to increase in number, extent, and severity due to climate change. Hence, it is ever more important to integrate technological developments and scientific knowledge into fire management aiming at protecting lives, infrastructure, and the environment. In this paper, a decision support system (DSS) adapted to the Portuguese context and based on multi-sensor technologies and geographic information system (GIS) functionalities is proposed to leverage operational data, enabling faster and more informed decisions to reduce the impact of wildfires. Here we present a flexible and reconfigurable DSS composed of three components: an ArcGIS online feature service that provides operational data and enables a collaborative environment of users that share operational data in near real-time; a mobile client application to interact with the system, enabling the use of GIS technology and visualization dashboards; and a multi-sensor device that collects field data providing value to external services. The design and validation of this system benefitted from the feedback of wildfire management specialists and a partnership with an end-user in the municipality of Mação that also helped establish the system requirements. The validation results demonstrated that a robust system was achieved with fully interoperable components that fulfill the defined system requirements.

To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang–Suen and Guo–Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications.

Wildfires pose significant threats to ecosystems worldwide, emphasizing the urgent need for rapid firefighting responses. It is well recognized that firefighters encounter substantial challenges when combating advanced-stage fires, underscoring the critical importance of early detection and suppression. This study proposes a novel approach employing deep learning algorithms to automate the detection of rural roads from aerial imagery. A manually annotated dataset was constructed from orthorectified aerial images of wildfire-prone areas, aiming to realistically capture the dynamic characteristics of rural road networks. The proposed system comprises a two-step process: a segmentation model to identify rural roads, followed by post-processing techniques designed to refine the segmentation results. Four deep learning architectures—VGG16 U-Net, VGG19 U-Net, ResNet50 U-Net, and MobileNet U-Net—were evaluated with extensive hyperparameter tuning, both before and after data augmentation. In the post-processing phase, two methods were implemented: one to remove small connected components, and another to reconnect segmented roads disrupted by occlusions caused by trees and shadows. Data augmentation yielded significant performance improvements across the models, which were further enhanced by post-processing. The highest performance achieved included a precision of 85.1%, a recall of 81.9%, and a Jaccard index of 71.7%, with comparable results observed for the other architectures. While post-processing generally contributed to improved outcomes, the percentage gains in evaluation metrics were modest. Notably, MobileNet U-Net exhibited the most substantial improvements after post-processing, with increases of 1.5% in Precision, 0.4% in Recall, and 1.2% in the Jaccard index. Overall, the proposed system demonstrates considerable potential for the efficient creation of rural road networks to support wildfire management.