Four people were killed in a Russian drone attack on Friday in the Ukrainian city of Dnipro. The regional governor said 19 people were injured and a large fire broke out in a hotel and restaurant complex that consumed a high-rise apartment building and 10 homes. Ukraine's military said its air force had struck a border post in Russia's Bryansk region, destroying infrastructure it said was used for drone launches. The General Staff of the Ukrainian Armed Forces said in a statement that the attack was in response to "dozens of daily strikes by attack drones". Ukrainian troops have staged an incursion into Russia's Belgorod region, according to Russian military bloggers.

Overnight, air sirens were heard sounding in several other Ukrainian regions, including the capital Kyiv. It was not immediately clear whether there were any casualties. The Russian military has not commented on the issue. In his video address late on Friday, Ukrainian President Volodymyr Zelensky again accused Russia of targeting Ukrainian energy infrastructure - in violation of a temporary moratorium agreed earlier this month in talks involving the US. Moscow has also repeatedly blamed Ukraine for attacking Russia's energy sector. Russian President Vladimir Putin earlier this week suggested that Ukraine should temporarily be placed under UN control to elect what he called a more "competent" government.

In recent years, autonomous unmanned aerial vehicle (UAV) technology has seen rapid advancements, significantly improving operational efficiency and mitigating risks associated with manual tasks in domains such as industrial inspection, agricultural monitoring, and search-and-rescue missions. Despite these developments, existing UAV inspection systems encounter two critical challenges: limited reliability in complex, unstructured, and GNSS-denied environments, and a pronounced dependency on skilled operators. To overcome these limitations, this study presents a LiDAR-based UAV inspection system employing a dual-phase workflow: human-in-the-loop inspection and autonomous inspection. During the human-in-the-loop phase, untrained pilots are supported by autonomous obstacle avoidance, enabling them to generate 3D maps, specify inspection points, and schedule tasks. Inspection points are then optimized using the Traveling Salesman Problem (TSP) to create efficient task sequences. In the autonomous phase, the quadrotor autonomously executes the planned tasks, ensuring safe and efficient data acquisition. Comprehensive field experiments conducted in various environments, including slopes, landslides, agricultural fields, factories, and forests, confirm the system's reliability and flexibility. Results reveal significant enhancements in inspection efficiency, with autonomous operations reducing trajectory length by up to 40\% and flight time by 57\% compared to human-in-the-loop operations. These findings underscore the potential of the proposed system to enhance UAV-based inspections in safety-critical and resource-constrained scenarios.

Lebanon's Ministry of Public Health has said that at least four people have been killed in two separate Israeli strikes in south Lebanon, as Israel claimed it struck Hezbollah operatives. Thursday's strikes were the latest in a series of deadly attacks in south Lebanon, despite a November ceasefire between Israel and Hezbollah after more than a year of hostilities, including two months of open war. An "Israeli enemy strike on a car in Yohmor al-Shaqeef led to the death of three people", said a Health Ministry statement reported by the National News Agency (NNA) on Thursday. The NNA said an "enemy drone" targeted a vehicle near the town, in a strike that came at the same time as artillery shelling. Elsewhere, the Israeli military said in a statement that "several Hezbollah terrorists were identified transferring weapons in the area of Yohmor in southern Lebanon", adding that the army "struck the terrorists".

North Korean leader Kim Jong Un has personally supervised his country's testing of new AI-equipped suicide and reconnaissance drones and called for unmanned aircraft and artificial intelligence to be prioritised in military modernisation plans. State-run Korean Central News Agency (KCNA) said on Thursday that Kim oversaw the testing of "various kinds of reconnaissance and suicide drones" produced by North Korea's Unmanned Aerial Technology Complex. The new North Korean drones are capable of "tracking and monitoring different strategic targets and enemy troop activities on the ground and the sea", while the attack drones will "be used for various tactical attack missions", KCNA said, noting that both drone systems have been equipped with "new artificial intelligence". Kim agreed to expand the production capacity of "unmanned equipment and artificial intelligence" and emphasised the importance of creating a long-term plan for North Korea to promote "the rapid development" of "intelligent drones", which is "the trend of modern warfare". Pictures from the tests, which took place on Tuesday and Wednesday, were said to show attack drones successfully striking ground targets, including military vehicles.

Recent advancements in unmanned aerial vehicle (UAV) technology have opened new avenues for dynamic data collection in challenging environments, such as sports fields during fast-paced sports action. For the purposes of monitoring sport events for dangerous injuries, we envision a coordinated UAV fleet designed to capture high-quality, multi-view video footage of collision events in real-time. The extracted video data is crucial for analyzing athletes' motions and investigating the probability of sports-related traumatic brain injuries (TBI) during impacts. This research implemented a UAV fleet system on the NetLogo platform, utilizing custom collision detection algorithms to compare against traditional TV-coverage strategies. Our system supports decentralized data capture and autonomous processing, providing resilience in the rapidly evolving dynamics of sports collisions. The collaboration algorithm integrates both shared and local data to generate multi-step analyses aimed at determining the efficacy of custom methods in enhancing the accuracy of TBI prediction models. Missions are simulated in real-time within a two-dimensional model, focusing on the strategic capture of collision events that could lead to TBI, while considering operational constraints such as rapid UAV maneuvering and optimal positioning. Preliminary results from the NetLogo simulations suggest that custom collision detection methods offer superior performance over standard TV-coverage strategies by enabling more precise and timely data capture. This comparative analysis highlights the advantages of tailored algorithmic approaches in critical sports safety applications.

Object detection models often struggle with class imbalance, where rare categories appear significantly less frequently than common ones. Existing sampling-based rebalancing strategies, such as Repeat Factor Sampling (RFS) and Instance-Aware Repeat Factor Sampling (IRFS), mitigate this issue by adjusting sample frequencies based on image and instance counts. However, these methods are based on linear adjustments, which limit their effectiveness in long-tailed distributions. This work introduces Exponentially Weighted Instance-Aware Repeat Factor Sampling (E-IRFS), an extension of IRFS that applies exponential scaling to better differentiate between rare and frequent classes. E-IRFS adjusts sampling probabilities using an exponential function applied to the geometric mean of image and instance frequencies, ensuring a more adaptive rebalancing strategy. We evaluate E-IRFS on a dataset derived from the Fireman-UAV-RGBT Dataset and four additional public datasets, using YOLOv11 object detection models to identify fire, smoke, people and lakes in emergency scenarios. The results show that E-IRFS improves detection performance by 22\% over the baseline and outperforms RFS and IRFS, particularly for rare categories. The analysis also highlights that E-IRFS has a stronger effect on lightweight models with limited capacity, as these models rely more on data sampling strategies to address class imbalance. The findings demonstrate that E-IRFS improves rare object detection in resource-constrained environments, making it a suitable solution for real-time applications such as UAV-based emergency monitoring.

Federal officials face a looming threat of foreign nationals utilizing drones to surveil United States military bases after two recent arrests and a string of mysterious incursions suggest the country's airspace is ill-equipped to handle the rapidly evolving technology. In late 2024, the Department of Justice announced charges against Yinpiao Zhou, 39, for allegedly flying a drone over Vandenberg Space Force Base in California and taking photos of the facility. The Chinese-American citizen was detained as he attempted to board a China-bound flight and was charged with violation of national defense airspace and failure to register an aircraft. "Anyone operating a drone over a restricted space, like a military base, would be subject to prosecution," Ken Gray, a former FBI agent and military analyst, told Fox News Digital. "A foreign national operating [a drone] raises a concern about that person being involved in some type of espionage or intelligence gathering."

Testing autonomous robotic systems, such as self-driving cars and unmanned aerial vehicles, is challenging due to their interaction with highly unpredictable environments. A common practice is to first conduct simulation-based testing, which, despite reducing real-world risks, remains time-consuming and resource-intensive due to the vast space of possible test scenarios. A number of search-based approaches were proposed to generate test scenarios more efficiently. A key aspect of any search-based test generation approach is the choice of representation used during the search process. However, existing methods for improving test scenario representation remain limited. We propose RILaST (Representation Improvement in Latent Space for Search-Based Testing) approach, which enhances test representation by mapping it to the latent space of a variational autoencoder. We evaluate RILaST on two use cases, including autonomous drone and autonomous lane-keeping assist system. The obtained results show that RILaST allows finding between 3 to 4.6 times more failures than baseline approaches, achieving a high level of test diversity.

The potential of tree planting as a natural climate solution is often undermined by inadequate monitoring of tree planting projects. Current monitoring methods involve measuring trees by hand for each species, requiring extensive cost, time, and labour. Advances in drone remote sensing and computer vision offer great potential for mapping and characterizing trees from aerial imagery, and large pre-trained vision models, such as the Segment Anything Model (SAM), may be a particularly compelling choice given limited labeled data. In this work, we compare SAM methods for the task of automatic tree crown instance segmentation in high resolution drone imagery of young tree plantations. We explore the potential of SAM for this task, and find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts, but that there is potential for methods which tune SAM further. We also show that predictions can be improved by adding Digital Surface Model (DSM) information as an input.