Accurate visual localization from aerial views is a fundamental problem with applications in mapping, large-area inspection, and search-and-rescue operations. In many scenarios, these systems require high-precision localization while operating with limited resources (e.g., no internet connection or GNSS/GPS support), making large image databases or heavy 3D models impractical. Surprisingly, little attention has been given to leveraging orthographic geodata as an alternative paradigm, which is lightweight and increasingly available through free releases by governmental authorities (e.g., the European Union). To fill this gap, we propose OrthoLoC, the first large-scale dataset comprising 16,425 UAV images from Germany and the United States with multiple modalities.

Visual geo-localization for drones faces critical degradation under weather perturbations, e.g., rain and fog, where existing methods struggle with two inherent limitations: 1) Heavy reliance on limited weather categories that constrain generalization, and 2) Suboptimal disentanglement of entangled scene-weather features through pseudo weather categories. We present WeatherPrompt, a multi-modality learning paradigm that establishes weather-invariant representations through fusing the image embedding with the text context. Our framework introduces two key contributions: First, a Training-free Weather Reasoning mechanism that employs off-the-shelf large multi-modality models to synthesize multi-weather textual descriptions through human-like reasoning. It improves the scalability to unseen or complex weather, and could reflect different weather strength. Second, to better disentangle the scene and weather features, we propose a multi-modality framework with the dynamic gating mechanism driven by the text embedding to adaptively reweight and fuse visual features across modalities. The framework is further optimized by the cross-modal objectives, including image-text contrastive learning and image-text matching, which maps the same scene with different weather conditions closer in the representation space. Extensive experiments validate that, under diverse weather conditions, our method achieves competitive recall rates compared to state-of-the-art drone geo-localization methods. Notably, it improves Recall@1 by 13.37% under night conditions and by 18.69% under fog and snow conditions.

More than 1,000 civilians in Sudan have been killed in drone strikes in the first five months of 2026, according to the United Nations. The death toll is due to a "sharp" increase in the use of drone warfare in the country's vicious civil war, UN High Commissioner for Human Rights (UNHCHR) Volker Turk said in a speech on Monday. On top of documenting more than 1,000 civilians being killed in the first five months of this year, the UN office also reported "rampant" levels of sexual violence, including rape. The war in the African nation started in April 2023 when a rivalry between Sudan's army chief, Abdel Fattah al-Burhan, and the commander of the paramilitary Rapid Support Forces, Mohamed Hamdan "Hemedti" Dagalo, exploded into war. The conflict, which had first started in the capital Khartoum, soon spread to several areas of the country.

Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static scenes, and a lack of annotations about exploration and the world. In this paper, we introduce Sekai (meaning "world" in Japanese), a high-quality first-person view worldwide video dataset with rich annotations for world exploration. It consists of over 5,000 hours of walking or drone view (FPV and UVA) videos from over 100 countries and regions across 750 cities. We develop an efficient and effective toolbox to collect, pre-process and annotate videos with location, scene, weather, crowd density, captions, and camera trajectories. Comprehensive analyses and experiments demonstrate the dataset's scale, diversity, annotation quality, and effectiveness for training video generation models. We believe Sekai will benefit the area of video generation and world exploration, and motivate valuable applications.

Nine people have been killed and several others injured in a wave of Russian strikes on Ukraine during which a major religious landmark in Kyiv caught fire, reports say. Four people were killed in attacks on Kyiv, while five rescue workers died trying to put out a fire caused by a Russian strike on the north-eastern city of Kharkiv, Ukrainian officials said. The 11th Century Dormition Cathedral was significantly damaged in what Ukrainian Prime Minister Yulia Svyrydenko called a brutal assault on our people and our heritage. Meanwhile, a Ukrainian drone attack in the Russian city of Tula, south of Moscow, killed three people and wounded three others, including a one-year-old, officials said. Drone and missile strikes set fire to buildings and cars and left more than 140,000 people in Ukraine's capital without electricity, Kyiv Mayor Vitali Klitschko said.

Protesters clashed with police forces during a demonstration against the upcoming G7 summit in Geneva. Tear gas and a water cannon were deployed to disperse the large crowd after protesters smashed windows and set a car on fire. What needs to be understood is the message, the basic message regarding all these countries that oppress us through money and power, said one protester who was disappointed to see the protest turn violent. The G7 summit starts on 15 June in Évian-les-Bains and will bring together the leaders of Britain, France, Canada, Germany, Italy, Japan, the United States and the European Union. Pope Leo XIV says Barcelona's iconic Sagrada Família is a masterpiece of stones, colours and light during his visit to Spain.

While unmanned aerial vehicles (UAVs) offer wide-area, high-altitude coverage for anomaly detection, they face challenges such as dynamic viewpoints, scale variations, and complex scenes. Existing datasets and methods, mainly designed for fixed ground-level views, struggle to adapt to these conditions, leading to significant performance drops in drone-view scenarios.To bridge this gap, we introduce A2Seek (Aerial Anomaly Seek), a large-scale, reasoning-centric benchmark dataset for aerial anomaly understanding. This dataset covers various scenarios and environmental conditions, providing high-resolution real-world aerial videos with detailed annotations, including anomaly categories, frame-level timestamps, region-level bounding boxes, and natural language explanations for causal reasoning. Building on this dataset, we propose A2Seek-R1, a novel reasoning framework that generalizes R1-style strategies to aerial anomaly understanding, enabling a deeper understanding of "Where" anomalies occur and "Why" they happen in aerial frames.To this end, A2Seek-R1 first employs a graph-of-thought (GoT)-guided supervised fine-tuning approach to activate the model's latent reasoning capabilities on A2Seek. Then, we introduce Aerial Group Relative Policy Optimization (A-GRPO) to design rule-based reward functions tailored to aerial scenarios. Furthermore, we propose a novel "seeking" mechanism that simulates UAV flight behavior by directing the model's attention to informative regions.Extensive experiments demonstrate that A2Seek-R1 achieves up to a 22.04\% improvement in AP for prediction accuracy and a 13.9\% gain in mIoU for anomaly localization, exhibiting strong generalization across complex environments and out-of-distribution scenarios. Our dataset and code are released at https://2-mo.github.io/A2Seek/.

Things to Do in L.A. Tap to enable a layout that focuses on the article. The FBI's Counter Drone Enforcement Team cited drone pilots and had their drones seized for violating the FAA's temporary flight restrictions around World Cup events. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

Unmanned Aerial Vehicles (UAVs) or drones, are increasingly used in search and rescue missions to detect human presence. Existing systems primarily leverage vision-based methods which are prone to fail under low-visibility or occlusion. Drone-based audio perception offers promise but suffers from extreme ego-noise that masks sounds indicating human presence. Existing datasets are either limited in diversity or synthetic, lacking real acoustic interactions, and there are no standardized setups for drone audition.

Pokemon Go became a worldwide hit after its launch - but players may not know that their game data trained AI that will potentially help military drones during war. Pokemon Go became a worldwide hit after its launch - but players may not know that their game data trained AI that will potentially help military drones during war. Fri 12 Jun 2026 03.06 EDTLast modified on Fri 12 Jun 2026 03.38 EDT An AI model trained on data collected from users of Pokémon Go will potentially help military drones find their location in war zones. Pokémon Go, a 2016 augmented reality mobile game, allowed players to find and catch Pokémon in the real world using the cameras on their mobile phones, and exploded in popularity. In 2018, the company reported having more than 800m downloads worldwide.