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Project Genie is rolling out today for all adult Google AI Ultra subscribers across the globe, following its debut in the United States this January. The service is also getting a new Street View capability that can generate interactive landscapes based on real-world locations found on Google Maps, starting with places in the US. Project Genie is Google's AI-powered system for creating explorable snow-globe environments from written prompts, with creations lasting 60 seconds at 720p and 24 fps. Users are able to create contained worlds in whatever style they'd like, complete with a character of their own description, and then move a camera around that space. The fresh Street View functionality allows users to base their AI worlds on location photos pulled from Google Maps, grounding their creations in a snapshot of reality.

"Ask Maps," rolling out today to Google Maps on mobile, lets you ask Gemini questions about locations and even to plan trips on your behalf. There's a new button in Google Maps: "Ask Maps." Google started rolling out this new generative AI feature today, a conversational, in-app tool that combines data from Maps with a user experience similar to the company's Gemini chatbot. It's designed to answer questions about locations and schedule routes in the navigation app. This is part of Google's overall strategy of adding Gemini to all its products.

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ABSTRACT Reliable geospatial information on road accidents is vital for safety analysis and infrastructure planning, yet most low-and middle-income countries continue to face a critical shortage of accurate, location-specific crash data. Existing text-based geocoding tools perform poorly in multilingual and unstructured news environments, where incomplete place descriptions and mixed language (e.g. To address these limitations, this study introduces ALIGN (Accident Location Inference through Geo-Spatial Neural Reasoning) -- a vision-language framework that emulates human spatial reasoning to infer accident location coordinates directly from available textual and map-based cues. ALIGN integrates large language and vision-language model mechanisms within a multi-stage pipeline that performs optical character recognition, linguistic reasoning, and map-level verification through grid-based spatial scanning. The framework systematically evaluates each predicted location against contextual and visual evidence, ensuring interpretable, fine-grained geolocation outcomes without requiring model retraining. Applied to Bangla-language news data source, ALIGN demonstrates consistent improvements over traditional geoparsing methods, accurately identifying district-and sub-district-level crash sites. Beyond its technical contribution, the framework establishes a high accuracy foundation for automated crash mapping in data-scarce regions, supporting evidence-driven road-safety policymaking and the broader integration of multimodal artificial intelligence in transportation analytics. Hossain) 1. Introduction Accurate, fine-grained geospatial data is the bedrock of effective public safety policy, urban planning, and strategic response. For road safety, knowing the precise location of traffic crashes is essential for diagnosing high-risk black spots, deploying emergency services, and evaluating the impact of engineering interventions. While high-income nations increasingly rely on robust, integrated crash databases and vehicle telematics (Guo, Qian, & Shi, 2022; Szpytko & Nasan Agha, 2020), utilizing advanced methods such as deep learning on multi-vehicle trajectories (Yang et al., 2021), ensemble models integrating connected vehicle data (Yang et al., 2026), and 2 probe vehicle speed contour analysis (Wang et al., 2021), a significant'geospatial data desert' persists in most Low-and Middle-Income Countries (LMICs) (Mitra & Bhalla, 2023; Chang et al., 2020). This gap is particularly tragic given that these regions bear the overwhelming brunt of global road traffic fatalities. This research focuses on a low-resource country-Bangladesh, a nation that exemplifies this critical data-sparse challenge. The World Bank has estimated that the costs associated with traffic crashes can amount to as much as 5.1% of the country's Gross Domestic Product (World Bank, 2022).

Google Maps, Waze and Apple Maps comparison reveals key differences in navigation accuracy, privacy policies and features to help users choose the best app.

We explore the geospatial reasoning capabilities of Large Language Models (LLMs), specifically, whether LLMs can read road network maps and perform navigation. Using road network as context, our prompting framework enables LLMs to generate valid paths without accessing any external navigation tools. Experiments show that LLMs outperform off-the-shelf baselines and specialized trajectory recovery models, with strong zero-shot generalization. Fine-grained analysis shows that LLMs have strong comprehension of the road network and coordinate systems, but also pose systematic biases with respect to regions and transportation modes. Finally, we demonstrate how LLMs can enhance navigation experiences by reasoning over maps in flexible ways to incorporate user preferences. Large Language Models (LLMs) are increasingly recognized as general-purpose systems, showing strong performance across domains ranging from mathematics and coding to vision and robotics. An emerging yet underex-plored question is whether these models possess geospa-tial understanding, the ability to reason about maps, paths, and spatial relationships. Such capabilities are fundamental to many real-world applications, e.g., autonomous vehicle navigation, logistics, and urban planning. While prior work has studied LLMs in contexts such as geographic knowledge retrieval (Manvi et al., 2024a;b) and map-based multiple-choice question answering (Dihan et al., 2025), the ability of LLMs to read road networks and plan paths has not been systematically evaluated. We investigate whether LLMs can perform navigation through the trajectory recovery task: reconstructing masked segments of GPS traces from the road network context, to bypass the restriction of relying on shortest path-type of ground truth which may not reflect human navigation pattern in practice (Golledge, 1995; Duckham & Kulik, 2003). Our dataset is framed in away that is harder than the traditional point-wise trajectory recovery task (Newson & Krumm, 2009; Song et al., 2017; Si et al., 2024), and closer to the higher-level navigation problem.

A ground-breaking study shows the most detailed map of a mammal's brain to date. The 3D blueprints display more than two miles of neural wiring, close to 100,000 nerve cells, and about 500 million synapses -- all contained in a piece of mouse brain no bigger than a grain of sand. Dr Clay Reid of the Allen Institute for Brain Science in Seattle said: 'Inside this tiny speck is an exquisite forest of connections, filled with rules we're only beginning to understand.' The sample comes from an outer part of the brain - known as the cortex - a region which is involved in sight, the Times reports. Dr Forrest Collman, of the same Institute, said: 'By studying how the cortex functions in the mouse brain, we can generate better ideas and hypotheses about how our own brains work.'