Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot, that level of imprecision is the difference between a successful mission and a costly failure. These machines require pinpoint accuracy to operate safely and efficiently. Addressing this critical challenge, researchers from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland have introduced a groundbreaking new method for visual localization during CVPR 2025 Their new paper, “FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching,” presents a novel AI model that significantly enhances the ability of a ground-level system, like an autonomous car, to determine its exact position and orientation using only a camera and a corresponding aerial (or satellite) image. The new approach has demonstrated a remarkable 28% reduction in mean localization error compared to the previous state-of-the-art on a challenging public dataset. Key Takeaways: Superior Accuracy: The FG2 model reduces the average localization error by a significant 28% on the VIGOR cross-area test set, a challenging benchmark for this task. Human-like Intuition: Instead of relying on abstract descriptors, the model mimics human reasoning by matching fine-grained, semantically consistent features—like curbs, crosswalks, and buildings—between a ground-level photo and an aerial map. Enhanced Interpretability: The method allows researchers to “see” what the AI is “thinking” by visualizing exactly which features in the ground and aerial images are being matched, a major step forward from previous “black box” models. Weakly Supervised Learning: Remarkably, the model learns these complex and consistent feature matches without any direct labels for correspondences. It achieves this using only the final camera pose as a supervisory signal. Challenge: Seeing the World from Two Different Angles The core problem of cross-view localization is the dramatic difference in perspective between a street-level camera and an overhead satellite view. A building facade seen from the ground looks completely different from its rooftop signature in an aerial image. Existing methods have struggled with this. Some create a general “descriptor” for the entire scene, but this is an abstract approach that doesn’t mirror how humans naturally localize themselves by spotting specific landmarks. Other methods transform the ground image into a Bird’s-Eye-View (BEV) but are often limited to the ground plane, ignoring crucial vertical structures like buildings. FG2: Matching Fine-Grained Features The EPFL team’s FG2 method introduces a more intuitive and effective process. It aligns two sets of points: one generated from the ground-level image and another sampled from the aerial map. Here’s a breakdown of their innovative pipeline: Mapping to 3D: The process begins by taking the features from the ground-level image and lifting them into a 3D point cloud centered around the camera. This creates a 3D representation of the immediate environment. Smart Pooling to BEV: This is where the magic happens. Instead of simply flattening the 3D data, the model learns to intelligently select the most important features along the vertical (height) dimension for each point. It essentially asks, “For this spot on the map, is the ground-level road marking more important, or is the edge of that building’s roof the better landmark?” This selection process is crucial, as it allows the model to correctly associate features like building facades with their corresponding rooftops in the aerial view. Feature Matching and Pose Estimation: Once both the ground and aerial views are represented as 2D point planes with rich feature descriptors, the model computes the similarity between them. It then samples a sparse set of the most confident matches and uses a classic geometric algorithm called Procrustes alignment to calculate the precise 3-DoF (x, y, and yaw) pose. Unprecedented Performance and Interpretability The results speak for themselves. On the challenging VIGOR dataset, which includes images from different cities in its cross-area test, FG2 reduced the mean localization error by 28% compared to the previous best method. It also demonstrated superior generalization capabilities on the KITTI dataset, a staple in autonomous driving research. Perhaps more importantly, the FG2 model offers a new level of transparency. By visualizing the matched points, the researchers showed that the model learns semantically consistent correspondences without being explicitly told to. For example, the system correctly matches zebra crossings, road markings, and even building facades in the ground view to their corresponding locations on the aerial map. This interpretability is extremenly valuable for building trust in safety-critical autonomous systems. “A Clearer Path” for Autonomous Navigation The FG2 method represents a significant leap forward in fine-grained visual localization. By developing a model that intelligently selects and matches features in a way that mirrors human intuition, the EPFL researchers have not only shattered previous accuracy records but also made the decision-making process of the AI more interpretable. This work paves the way for more robust and reliable navigation systems for autonomous vehicles, drones, and robots, bringing us one step closer to a future where machines can confidently navigate our world, even when GPS fails them. Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Jean-marc MommessinJean-marc is a successful AI business executive .He leads and accelerates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford.Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/AI-Generated Ad Created with Google’s Veo3 Airs During NBA Finals, Slashing Production Costs by 95%Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Highlighted at CVPR 2025: Google DeepMind’s ‘Motion Prompting’ Paper Unlocks Granular Video ControlJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Snowflake Charts New AI Territory: Cortex AISQL & Snowflake Intelligence Poised to Reshape Data AnalyticsJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Exclusive Talk: Joey Conway of NVIDIA on Llama Nemotron Ultra and Open Source Models

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Manus has kick-started an AI agent boom in China Last year, China saw a boom in foundation models, the do-everything large language models that underpin the AI revolution. This year, the focus has shifted to AI agents—systems that are less about responding to users’ queries and more about autonomously accomplishing things for them.There are now a host of Chinese startups building these general-purpose digital tools, which can answer emails, browse the internet to plan vacations, and even design an interactive website. Many of these have emerged in just the last two months, following in the footsteps of Manus—a general AI agent that sparked weeks of social media frenzy for invite codes after its limited-release launch in early March.As the race to define what a useful AI agent looks like unfolds, a mix of ambitious startups and entrenched tech giants are now testing how these tools might actually work in practice—and for whom. Read the full story. —Caiwei Chen Inside the race to find GPS alternatives Later this month, an inconspicuous 150-kilogram satellite is set to launch into space aboard the SpaceX Transporter 14 mission. Once in orbit, it will test super-accurate next-generation satnav technology designed to make up for the shortcomings of the US Global Positioning System (GPS). Despite the system’s indispensable nature, the GPS signal is easily suppressed or disrupted by everything from space weather to 5G cell towers to phone-size jammers worth a few tens of dollars. The problem has been whispered about among experts for years, but it has really come to the fore in the last three years, since Russia invaded Ukraine.Now, startup Xona Space Systems wants to create a space-based system that would do what GPS does but better. Read the full story. —Tereza Pultarova Why doctors should look for ways to prescribe hope —Jessica Hamzelou This week, I’ve been thinking about the powerful connection between mind and body. Some new research suggests that people with heart conditions have better outcomes when they are more hopeful and optimistic. Hopelessness, on the other hand, is associated with a significantly higher risk of death. The findings build upon decades of fascinating research into the phenomenon of the placebo effect. Our beliefs and expectations about a medicine (or a sham treatment) can change the way it works. The placebo effect’s “evil twin,” the nocebo effect, is just as powerful—negative thinking has been linked to real symptoms. Researchers are still trying to understand the connection between body and mind, and how our thoughts can influence our physiology. In the meantime, many are developing ways to harness it in hospital settings. Is it possible for a doctor to prescribe hope? Read the full story. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Elon Musk threatened to cut off NASA’s use of SpaceX’s Dragon spacecraftHis war of words with Donald Trump is dramatically escalating. (WP $)+ If Musk actually carried through with his threat, NASA would seriously struggle. (NYT $)+ Silicon Valley is starting to pick sides. (Wired $)+ It appears as though Musk has more to lose from their bruising breakup. (NY Mag $) 2 Apple and Alibaba’s AI rollout in China has been delayedIt’s the latest victim of Trump’s trade war. (FT $)+ The deal is supposed to support iPhones’ AI offerings in the country. (Reuters) 3 X’s new policy blocks the use of its posts to ‘fine-tune or train’ AI modelsUnless companies strike a deal with them, that is. (TechCrunch)+ The platform could end up striking agreements like Reddit and Google. (The Verge) 4 RJK Jr’s new hire is hunting for proof that vaccines cause autismVaccine skeptic David Geier is seeking access to a database he was previously barred from. (WSJ $)+ How measuring vaccine hesitancy could help health professionals tackle it. (MIT Technology Review) 5 Anthropic has launched a new service for the militaryClaude Gov is designed specifically for US defense and intelligence agencies. (The Verge)+ Generative AI is learning to spy for the US military. (MIT Technology Review) 6 There’s no guarantee your billion-dollar startup won’t failIn fact, one in five of them will. (Bloomberg $)+ Beware the rise of the AI coding startup. (Reuters) 7 Walmart’s drone deliveries are taking offIt’s expanding to 100 new US stories in the next year. (Wired $) 8 AI might be able to tell us how old the Dead Sea Scrolls really are Models suggest they’re even older than we previously thought. (The Economist $)+ How AI is helping historians better understand our past. (MIT Technology Review) 9 All-in-one super apps are a hit in the Gulf They’re following in China’s footsteps. (Rest of World) 10 Nintendo’s Switch 2 has revived the midnight launch eventFans queued for hours outside stores to get their hands on the new console. (Insider $)+ How the company managed to dodge Trump’s tariffs. (The Guardian) Quote of the day “Elon finally found a way to make Twitter fun again.” —Dan Pfeiffer, a host of the political podcast Pod Save America, jokes about Elon Musk and Donald Trump’s ongoing feud in a post on X. One more thing This rare earth metal shows us the future of our planet’s resources We’re in the middle of a potentially transformative moment. Metals discovered barely a century ago now underpin the technologies we’re relying on for cleaner energy, and not having enough of them could slow progress.  Take neodymium, one of the rare earth metals. It’s used in cryogenic coolers to reach ultra-low temperatures needed for devices like superconductors and in high-powered magnets that power everything from smartphones to wind turbines. And very soon, demand for it could outstrip supply. What happens then? And what does it reveal about issues across wider supply chains? Read our story to find out. —Casey Crownhart We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + Sightings of Bigfoot just happen to correlate with black bear populations? I smell a conspiracy!+ Watch as these symbols magically transform into a pretty impressive Black Sabbath mural.+ Underwater rugby is taking off in the UK.+ Fed up of beige Gen Z trends, TikTok is bringing the 80s back.

© Henrique Ferreira via Unsplash Finding property lines can be tricky, especially for individuals who are looking to purchase or list a home for sale. Conventional techniques, including engaging surveyors, are costly and labor-intensive. As an alternate solution, technology provides apps that profess to show property lines. This raises the question: How precise are these digital resources at demarcating boundary lines? The emergence of an app that shows property lines has revolutionized how property owners, buyers, and real estate professionals interact with land boundaries. These digital tools leverage advanced mapping technologies to provide visual representations of property boundaries, offering a more accessible alternative to traditional surveying methods while raising important questions about their accuracy and reliability. What Are Property Line Apps? Apps that display property lines use Geographic Information Systems (GIS) and satellite imagery. They offer users a visual display of land boundaries, most commonly available on smartphones or tablets. These apps are meant to help make property borders easier to identify and are a helpful tool for property owners, real estate agents, and buyers. These applications typically draw information from public records, county assessor data, and other official sources to create digital representations of property boundaries. Many also incorporate user-friendly features like measurement tools, parcel information displays, and the ability to save or share property data with others. How Various Factors Affect Accuracy Several factors impact the reliability of property line apps. First, the source of the data is significant. Apps usually have access to government databases and public records that vary in accuracy based on when the data was last refreshed. Second, GPS technology limitations impact accuracy. Although GPS technology is becoming more advanced, apps might still have discrepancies where tree cover or high buildings block signals from satellites and influence tracking accuracy. The resolution of satellite imagery also plays a crucial role in determining how precisely property lines can be displayed. Higher-resolution images allow for more detailed and accurate boundary placements, while lower-quality imagery may result in less precise representations. Additionally, the frequency of data updates affects whether the app reflects recent property divisions, consolidations, or boundary adjustments. Comparing Traditional Surveying and Apps Professional surveyors approach property line determination using high-precision equipment and established methodologies. This traditional approach yields highly accurate results with precise and legally enforceable boundaries. Apps offer more general information on property lines compared to professional surveys. While they are fast and convenient, they provide no substitute for the precision of a professional survey. App-generated boundaries should not be relied upon as definitive indications of legal property lines. Traditional surveys involve physical measurements taken directly on the property, considering historical markers, neighboring properties, and legal descriptions. In contrast, apps rely on digital interpretations of existing records, which may not account for all the nuances that a professional surveyor would observe in person. Advantages of property line applications App-Generated Property Lines Property line apps may have their limitations, but they do have valuable uses. They are useful for general assessments where an immediate overview of boundaries may be required. The applications also have user-friendly interfaces that allow a wider audience to use these applications and learn technical skills. Additionally, they are often enhanced with more functionalities, such as calculating areas and providing land parcel information, thus expanding their usefulness for users. According to the U.S. Bureau of Land Management, these digital tools have significantly increased public access to property information that was previously difficult to obtain without professional assistance. This democratization of property data allows property owners to be more informed about their land assets and helps potential buyers better understand properties of interest before making major decisions. Potential Limitations and Risks Property line apps may be convenient, but they have clear limitations. Since the data relies heavily on public records, data errors are common. It can be outdated or incomplete, which can lead to misunderstandings or disputes. In addition, these apps are incapable of recognizing legal nuances, such as easements or encroachments, which can significantly impact property rights and boundaries. There is also a risk that users might place too much confidence in app-generated boundaries when making important decisions. While these tools can provide helpful guidance, they should not be the sole basis for resolving boundary disputes, building structures near property lines, or making purchase decisions without professional verification. Best Practices for Users Users should follow a few best practices to make the best and most effective use of a property line app. Cross-referencing results with official records verifies data accuracy, minimizing potential inaccuracies. Moreover, when app data is paired with physical inspections, it provides a fuller picture of property lines. Advice from professionals, including surveyors or real estate agents, can also be beneficial, especially for legal transactions. For important matters such as property purchases, boundary disputes, or construction projects near property lines, it’s advisable to use apps as preliminary tools only, following up with professional surveys before making final decisions. Understanding the limitations of these digital tools helps users utilize them appropriately within a broader strategy for property boundary determination. Conclusion Instead, property line apps provide a convenient and accessible way to determine where your land ends and where your neighbor’s begins. Yet, the precision of these tools is contingent on multiple factors such as data sources and technological limitations. Although useful as an initial step, these tools should not be used, and they should not be used for legal purposes, instead of professional surveys. Users can properly contextualize property boundary information by understanding what these applications can and cannot do. Technology continues to shape how we deal with real estate by digitalizing and providing easy access to tools that simplify complex processes. These apps will likely improve accuracy over time and become increasingly integral to property transactions. Until then, users must balance convenience with reliability, ensuring that the information they obtain is helpful and accurate. Smart Technologytechnology by ArchEyes Team Leave a comment

We may earn a commission from links on this page.In 2023, I decided to update my Apple Watch after consistently failing to wear my Series 4 for a number of years. I sold that one on Poshmark and began looking at newer models to find one with enough features to convince me to actually wear it. I opted to get a Series 8, although the Series 9 had just been released, as I was buying two: one for my mom and one for myself. As it turns out, that was a great decision.If you're searching for a new wearable or considering upgrading yours, you might also be wondering which of the older Apple Watch models is still useful today. My Series 8 is holding up beautifully three years after it was introduced, so I'm a big proponent of using older devices as long as possible. But not all Apple Watches will work as well as the Series 8 does in 2025. Don’t buy a watch Apple doesn’t support anymoreWe have to draw the line somewhere: Seven of Apple's watches are no longer supported, meaning they won't receive any software or security updates anymore. In addition, you run the risk that the watch will no longer be compatible with your iPhone or certain apps. In short, you shouldn't buy a watch that Apple doesn't support. That includes the following:Apple Watch Series 0Apple Watch Series 1Apple Watch Series 2Apple Watch Series 3Apple Watch Series 4Apple Watch Series 5Apple Watch SE (first-gen) While the company does currently support the Series 6, it is next in line to join this list. It's not clear when that will happen, but you can be sure it will. We'll see next week—when Apple reveals watchOS 26—whether the watch will be supported another year. If not, it'll be stuck on watchOS 11 for good.Performance and other generational Watch improvementsThere are considerations for older Apple Watch models that extend beyond their ability to simply run the latest operating system. With each generation, improvements are made in some form or another. For instance, the Series 4 introduced the ECG sensor, while the Series 6 introduced the blood oxygen sensor (though Apple had to disable the feature for the Series 9 and Ultra 2 in the U.S. due to a lawsuit). The Series 7 charges faster than its predecessors, and Apple has included fast charging on most watch models since (sorry, Apple Watch SE users).In general, each Apple Watch is faster than the last. Apple tends to put its newest S-Chip—the Apple Watch's processor—in its latest watch series. Simply put, a newer S-chip gives you a faster, more productive product. The Series 6 has an S6 chip, Series 7 has S7, and so on until you hit the Ultras. (The first-generation Ultra has an S8 chip like the Series 8, while the Ultra 2 has an S9 chip like the Series 9.)While there are some core features all currently supported watches share—like workout and swim tracking, sleep tracking, Apple Pay, ECG scanning, and the ability to read and respond to messages—newer models also each have some of their own special advancements and upgrades. Here's a brief list:The Series 7 introduced faster charging, a larger display, and more durable screen.The Series 8 brought temperature sensing, crash detection, and a low-power mode for conserving battery (as did the second-gen Apple Watch SE).The Series 9 debuted new gesture controls, on-device Siri access, more precise location tracking in Find My, and a display with double the brightness of the Series 8.The first-gen Apple Watch Ultra introduced a more durable titanium casing, custom shortcuts to apps and modes via the Action button, a depth gauge and water temperature sensor, more accurate GPS, a 36-hour battery life, and an emergency siren.The Apple Watch Ultra 2 introduced a display with a maximum brightness of 3,000 nits and on-device media playback. The Series 10 introduced the largest display available on a standard Apple Watch and faster charging. If you see a feature you absolutely need in a particular watch model, you'll have to spring for it. But if you just want something for core Apple Watch tasks, you can start to consider older options. Apple's watch comparison site can be a helpful tool for identifying different features among models. Battery degradation All tech degrades to some extent and the Apple Watch is no different—particularly when it comes to the battery. While there are ways to mitigate the problem, over time, the lithium-ion battery powering your wrist computer won't last as long as it used to. That might be a bigger issue than your watch's ability to download and support a new operating system. Apple's warranty doesn't cover batteries that wear down from normal use, and charges $99 for the repair, which you could instead put towards the purchase of a new watch. There is one exception: Battery service is free if you have AppleCare+ and your watch's battery holds less than 80% of its original capacity. You need to take your watch in to an Apple Store or service provider to have it tested. My watch was pre-owned, and while I have no way of knowing if it has its original battery, my battery life has not declined substantially in the two years I've been using it daily. I primarily use mine to track my workouts, vitals, and sleep, which means it's always running. I charge it while I'm in the shower and occasionally for a few minutes before bed, and that's about it. On an average day of constant notifications, mine lasts me a bit longer than the advertised 18-hour mark. Because I have little interest in the small improvements offered by the Series 9 and Series 10—like extra brightness, larger screen size, performance bumps, and advanced cycle tracking—the battery life is what would (or will) compel me to upgrade in the future, but for now, I have not noticed any problems. I asked my mom if she's noticed any battery degradation on hers, since I bought it at the same time and place as mine, and she said no. She uses hers to track walking workouts, talk on the phone, and monitor her sleep and vitals, too.Stick with the Series 7 or newerThoroughly consider which of the features on newer models are actually important to you before making any buying decision and, if you can, stay above a Series 7. The Series 6 is still functional, but, again, it's a matter of time until the company stops acknowledging that one completely. For now, I have been pleasantly surprised by how well my Series 8 has held up for two years. Its touchscreen has never faltered, the external buttons function perfectly, it syncs to all of my apps and devices with no problem, and it does exactly what I need it to do—which is to tell me how many steps I'm taking and how hard I'm exerting myself at the gym. If you're in the market for a smart watch, I see no reason that an older version shouldn't be considered, as long as it still runs the latest operating system. You can save a chunk of change by sourcing an older model from the resale or refurbished markets and put that money away for when Apple drops something super revolutionary in the wearable space. Apple doesn't sell anything below a Series 10 or SE directly anymore, so if you want a 6, 7, 8, or 9, you'll have to check the resale and refurbished markets. You'll definitely save some money that way (a new Series 10 starts at $399, though it can be found on sale, and the refurbished Series 8 I got is selling right now for $219). Apple Watch Series 8 (Renewed) $209.00 at Amazon $220.00 Save $11.00 Learn More Learn More $209.00 at Amazon $220.00 Save $11.00