Wildfires represent a significant natural risk causing economic losses, human death and environmental damage. In recent years, the world has seen an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small-scale environments. However, wildland fires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, unmanned aerial vehicles (UAV) and unmanned aerial systems (UAS) were proposed. UAVs have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper, previous works related to the use of UAV in wildland fires are reviewed. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, some of the recent frameworks proposing the use of both aerial vehicles and unmanned ground vehicles (UGV) for a more efficient wildland firefighting strategy at a larger scale are presented.

In the past few years, the use of Unmanned Aerial Vehicles (UAVs) has expanded and now reached mainstream levels for applications such as infrastructure inspection, agriculture, transport, security, entertainment, real estate, environmental conservation, search and rescue, and even insurance. This surge in adoption can be attributed to the UAV ecosystem’s maturation, which has not only made these devices more accessible and cost effective but has also significantly enhanced their operational capabilities in terms of flight duration and embedded computing power. In conjunction with these developments, the research on Absolute Visual Localization (AVL) has seen a resurgence driven by the introduction of deep learning to the field. These new approaches have significantly improved localization solutions in comparison to the previous generation of approaches based on traditional computer vision feature extractors. This paper conducts an extensive review of the literature on deep learning-based methods for UAV AVL, covering significant advancements since 2019. It retraces key developments that have led to the rise in learning-based approaches and provides an in-depth analysis of related localization sources such as Inertial Measurement Units (IMUs) and Global Navigation Satellite Systems (GNSSs), highlighting their limitations and advantages for more effective integration with AVL. The paper concludes with an analysis of current challenges and proposes future research directions to guide further work in the field.

In the last decade, research was conducted to develop measurement solutions dedicated to forest fires and based on image processing and computer vision. Significant progress was achieved in developing such tools for fire propagation in controlled laboratory environments. However, these developments are not suitable for outdoor unstructured environments. Additionally, wildland fires cover large areas; this limits the use of vision-based ground systems. Unmanned Aerial Vehicles (UAV) with cameras for remote sensing are promising as their performance/price ratio is increasing over time. They can provide a low-cost alternative for the prevention, detection, propagation monitoring and real-time support for fire fighting. In this paper, we give an overview of past work dealing with the use of UAVs in the context of wildland and forest fires, and propose a framework based on cooperative UAVs and UGVs for fires monitoring on a larger scale.

Most of today's UAVs make use of multi-sensor GNSS/INS fusion for localization during navigation. In such a context GNSS systems are used as a compact and cost-effective way to constrain the unbounded error induced by the INS sensors on the localization. Unfortunately, GNSS systems have been proven to be unreliable in multiple contexts. The drawback of such an approach resides in the radio communications necessary to acquire the localization data. Radio communication systems are prone to availability problems in some environments, to signal alteration and to interference. The root cause of the problem resides in the use of global information to solve a local problem. In this work, we propose the use of local visual information to perform relative localization in an unknown outdoor environment. The algorithm uses feature point methods to extract salient points from a set of images pertaining to possible matches during the navigation. The extracted features are matched with available visual data stored during previous navigation or from an aerial view map. Different feature extraction techniques were analyzed, and ORB was the one that gave the best mean absolute error. The estimated distance between the best match and ground-truth localization was within 70 meters on average at an altitude of 150 meters. Experimental tests were conducted on outdoor videos captured using a quadcopter. The obtained results are promising and show the possibility of using relative visual data in GPS/GNSS-denied environments to improve the robustness of UAVs navigation.

Unmanned aerial vehicles have become widespread in today’s world and are used for applications ranging from real estate marketing and bridge inspection to defense and military applications. These applications have in common some form of autonomous navigation that requires a good localization capability at all time. Most UAV are using a combination of global navigation satellite systems (GNSS) and inertial measurement unit (IMU) to perform this task. Unfortunately, GNSS are subject to signal unavailability and all sorts of interference impeding on the ability of the UAV to self-localize. In this paper, we propose a new algorithm to perform localization in GNSS-denied environments by using a relative visual localization technique. We developed a new measure based on the use of local feature points extracted with ORB to estimate the likelihood of a previously captured image to have been taken in a position close to the current UAV location. The measure is embedded in a particle filter in which IMU data is used in order to reduce the number of images we need to analyze to perform localization. The resulting method have shown significant improvement in both accuracy and execution time in comparison to previous approaches.

The FungiCLEF 2025 competition addresses the challenge of automatic fungal species recognition using realistic, field-collected observational data. Accurate identification tools support both mycologists and citizen scientists, greatly enhancing large-scale biodiversity monitoring. Effective recognition systems in this context must handle highly imbalanced class distributions and provide reliable performance even when very few training samples are available for many species, especially rare and under-documented taxa that are often missing from standard training sets. According to competition organizers, about 20\% of all verified fungi observations, representing nearly 20,000 instances, are associated with these rarely recorded species. To tackle this challenge, we propose a robust deep learning method based on prototypical networks, which enhances prototype representations for few-shot fungal classification. Our prototypical network approach exceeds the competition baseline by more than 30 percentage points in Recall@5 on both the public (PB) and private (PR) leaderboards. This demonstrates strong potential for accurately identifying both common and rare fungal species, supporting the main objectives of FungiCLEF 2025.