Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management. Although some of the existing survey papers have explored various learning-based approaches, a comprehensive review emphasizing the application of AI-enabled UAV systems and their subsequent impact on multi-stage wildfire management is notably lacking. This survey aims to bridge these gaps by offering a systematic review of the recent state-of-the-art technologies, highlighting the advancements of UAV systems and AI models from pre-fire, through the active-fire stage, to post-fire management. To this aim, we provide an extensive analysis of the existing remote sensing systems with a particular focus on the UAV advancements, device specifications, and sensor technologies relevant to wildfire management. We also examine the pre-fire and post-fire management approaches, including fuel monitoring, prevention strategies, as well as evacuation planning, damage assessment, and operation strategies. Additionally, we review and summarize a wide range of computer vision techniques in active-fire management, with an emphasis on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms for wildfire classification, segmentation, detection, and monitoring tasks. Ultimately, we underscore the substantial advancement in wildfire modeling through the integration of cutting-edge AI techniques and UAV-based data, providing novel insights and enhanced predictive capabilities to understand dynamic wildfire behavior.

Unmanned Aerial Vehicles (UAVs) have become pivotal in domains spanning military, agriculture, surveillance, and logistics, revolutionizing data collection and environmental interaction. With the advancement in drone technology, there is a compelling need to develop a holistic methodology for designing UAVs. This research focuses on establishing a procedure encompassing conceptual design, use of composite materials, weight optimization, stability analysis, avionics integration, advanced manufacturing, and incorporation of autonomous payload delivery through object detection models tailored to satisfy specific applications while maintaining cost efficiency. The study conducts a comparative assessment of potential composite materials and various quadcopter frame configurations. The novel features include a payload-dropping mechanism, a unibody arm fixture, and the utilization of carbon-fibre-balsa composites. A quadcopter is designed and analyzed using the proposed methodology, followed by its fabrication using additive manufacturing and vacuum bagging techniques. A computer vision-based deep learning model enables precise delivery of payloads by autonomously detecting targets.

Large-scale infrastructures are prone to deterioration due to age, environmental influences, and heavy usage. Ensuring their safety through regular inspections and maintenance is crucial to prevent incidents that can significantly affect public safety and the environment. This is especially pertinent in the context of electrical power networks, which, while essential for energy provision, can also be sources of forest fires. Intelligent drones have the potential to revolutionize inspection and maintenance, eliminating the risks for human operators, increasing productivity, reducing inspection time, and improving data collection quality. However, most of the current methods and technologies in aerial robotics have been trialed primarily in indoor testbeds or outdoor settings under strictly controlled conditions, always within the line of sight of human operators. Additionally, these methods and technologies have typically been evaluated in isolation, lacking comprehensive integration. This paper introduces the first autonomous system that combines various innovative aerial robots. This system is designed for extended-range inspections beyond the visual line of sight, features aerial manipulators for maintenance tasks, and includes support mechanisms for human operators working at elevated heights. The paper further discusses the successful validation of this system on numerous electrical power lines, with aerial robots executing flights over 10 kilometers away from their ground control stations.

Other significant Hamas officials died in Tuesday's drone strike that killed senior leader Saleh al-Arouri, harming the armed group's military capabilities in Lebanon during Israel's war on Gaza. According to Lebanese state media, the strike on a Hamas office in the Hezbollah stronghold of Dahiyeb, a southern suburb of Beirut, killed seven people. Hamas described the killing of al-Arouri on its official TV channel as "a "cowardly assassination" by Israel. Israeli Prime Minister Benjamin Netanyahu's adviser Mark Regev told the United States-based TV news channel MSNBC that Israel had not taken responsibility for the attack and added: "Whoever did it, it must be clear that this was not an attack on the Lebanese state." Al-Arouri, 57, was the deputy chief of the Hamas political bureau.

A general strike has been called across the cities of the occupied West Bank in protest against the assassination of seven members of Hamas, including the deputy head of its political bureau, Saleh al-Arouri. The strike was called by Palestinian armed groups that asked people to stay home on Wednesday and only leave to march in demonstrations against the drone attack on the outskirts of Beirut. The slain men are Saleh al-Arouri, who was also the commander of the Qassam Brigades in the occupied West Bank; Samir Fendi, who commanded the Qassam Brigades in Lebanon; Azzam al-Aqraa, who commanded the Qassam Brigades in southern Lebanon; and members Mahmoud Shaheen, Mohammed al-Rayes, Mohammed Bashasha and Ahmed Hamoud. All seven will be buried in Lebanon. Funerals will be held for Hamoud and Shaheen on Wednesday in the Burj al-Barajneh camp for Palestinian refugees and Taalbaya, respectively.

Foreign Policy Adviser to Netanyahu Dr. Ophir Falk tells'Cavuto Live' that it could take Israel anywhere from a week to a year to'destroy Hamas.' A pair of explosions near the grave of Iranian general Qassem Soleimani killed at least 103 people and wounded up to 141 more in Kerman, Iran, according to Wednesday reports from Iranian media. Iranian officials say the explosions occurred during a ceremony honoring Soleimani on the fourth anniversary of his death. Soleimani was killed in 2020 at the hands of a U.S. drone strike ordered by then-President Trump. "The blasts were caused by terrorist attacks," Iranian media quoted a local official as saying, without accusing any specific party. "Several gas canisters exploded on the road leading to the cemetery."

Iraqi forces shot down an armed drone on Tuesday over the Erbil airport in northern Iraq, where U.S. and other international forces are stationed, according to a report. The Kurdistan Counter Terrorism, an Iraqi semi-autonomous regional security agency, said an "illegal militia" launched an armed drone against the Erbil airport that was shot down at approximately 09:52 a.m. It is not immediately clear if the foiled attack caused any damage or casualties. U.S. forces in Iraq and Syria have been attacked more than 90 times since the start of the conflict. The Pentagon does not count attacks on U.S. warships at sea in this number.

Vision-based object tracking is an essential precursor to performing autonomous aerial navigation in order to avoid obstacles. Biologically inspired neuromorphic event cameras are emerging as a powerful alternative to frame-based cameras, due to their ability to asynchronously detect varying intensities (even in poor lighting conditions), high dynamic range, and robustness to motion blur. Spiking neural networks (SNNs) have gained traction for processing events asynchronously in an energy-efficient manner. On the other hand, physics-based artificial intelligence (AI) has gained prominence recently, as they enable embedding system knowledge via physical modeling inside traditional analog neural networks (ANNs). In this letter, we present an event-based physics-guided neuromorphic planner (EV-Planner) to perform obstacle avoidance using neuromorphic event cameras and physics-based AI. We consider the task of autonomous drone navigation where the mission is to detect moving gates and fly through them while avoiding a collision. We use event cameras to perform object detection using a shallow spiking neural network in an unsupervised fashion. Utilizing the physical equations of the brushless DC motors present in the drone rotors, we train a lightweight energy-aware physics-guided neural network (PgNN) with depth inputs. This predicts the optimal flight time responsible for generating near-minimum energy paths. We spawn the drone in the Gazebo simulator and implement a sensor-fused vision-to-planning neuro-symbolic framework using Robot Operating System (ROS). Simulation results for safe collision-free flight trajectories are presented with performance analysis, ablation study and potential future research directions

In today's agriculture, there are far too many innovations involved. One of the emerging technologies is pesticide spraying using drones. Manual pesticide spraying has a number of negative consequences for the people who are involved in the spraying operation. The result of exposure symptoms can include minor skin inflammation and birth abnormalities, tumors, genetic modifications, nerve and blood diseases, endocrinal interference, coma or death. However, Drone can be used to automate fertilizer application, pesticide spraying, and field tracking. This paper provides a concise overview of the use of drones for field inspection and pesticide spraying. displays different methodologies and controllers of agriculture drone and explains some essential Drone Hardware, Software elements and applications

One of the most critical applications undertaken by coalitions of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) is reaching predefined targets by following the most time-efficient routes while avoiding collisions. Unfortunately, UAVs are hampered by limited battery life, and UGVs face challenges in reachability due to obstacles and elevation variations. Existing literature primarily focuses on one-to-one coalitions, which constrains the efficiency of reaching targets. In this work, we introduce a novel approach for a UAV-UGV coalition with a variable number of vehicles, employing a modified mean-shift clustering algorithm to segment targets into multiple zones. Each vehicle utilizes Multi-agent Deep Deterministic Policy Gradient (MADDPG) and Multi-agent Proximal Policy Optimization (MAPPO), two advanced reinforcement learning algorithms, to form an effective coalition for navigating obstructed environments without collisions. This approach of assigning targets to various circular zones, based on density and range, significantly reduces the time required to reach these targets. Moreover, introducing variability in the number of UAVs and UGVs in a coalition enhances task efficiency by enabling simultaneous multi-target engagement. The results of our experimental evaluation demonstrate that our proposed method substantially surpasses current state-of-the-art techniques, nearly doubling efficiency in terms of target navigation time and task completion rate.