For tourists visiting South Korea, one of the world's most technologically advanced nations, navigating the country's urban heartlands can prove surprisingly frustrating for one simple reason: Google Maps just doesn't work effectively. But on 11 August that could change, as South Korean authorities are set to decide whether to finally grant Google's request to export the country's detailed mapping data to overseas servers. Such a move would open up functionality that allows the app to give detailed directions and show users the best routes to travel. It is a debate spanning nearly two decades which has evolved into a broader test of how democracies balance digital sovereignty with economic openness. Local industry groups are warning of market domination from foreign companies, while those who back Google's request argue restrictions harm tourism and innovation.

This paper presents the design, implementation, and evaluation of an IoT-based robotic system for mapping and monitoring indoor air quality. The primary objective was to develop a mobile robot capable of autonomously mapping a closed environment, detecting concentrations of CO$_2$, volatile organic compounds (VOCs), smoke, temperature, and humidity, and transmitting real-time data to a web interface. The system integrates a set of sensors (SGP30, MQ-2, DHT11, VL53L0X, MPU6050) with an ESP32 microcontroller. It employs a mapping algorithm for spatial data acquisition and utilizes a Mamdani fuzzy logic system for air quality classification. Empirical tests in a model room demonstrated average localization errors below $5\%$, actuator motion errors under $2\%$, and sensor measurement errors within $12\%$ across all modalities. The contributions of this work include: (1) a low-cost, integrated IoT robotic platform for simultaneous mapping and air quality detection; (2) a web-based user interface for real-time visualization and control; and (3) validation of system accuracy under laboratory conditions.

I miss having clean floors. I've been using a variety of Roombas over the years to help out with vacuuming, but ever since my wife and I had our second child in 2022, mopping has become an afterthought. And really, vacuuming can only clean your floor so much. I missed the shimmer of a mopped hardwood floor and the smell of Murphy Oil cleaner lingering in the air. Instead, I've been living with even more toys, crumbs and an assortment of bodily waste (which three cats contribute to) on my flooring and carpets.

Manual analysis of XRD data is usually laborious and time consuming. The deep neural network (DNN) based models trained by synthetic XRD patterns are proved to be an automatic, accurate, and high throughput method to analysis common XRD data collected from solid sample in ambient environment. However, it remains unknown that whether synthetic XRD based models are capable to solve u-XRD mapping data for in-situ experiments involving liquid phase exhibiting lower quality with significant artifacts. In this study, we collected u-XRD mapping data from an LaCl3-calcite hydrothermal fluid system and trained two categories of models to solve the experimental XRD patterns. The models trained by synthetic XRD patterns show low accuracy (as low as 64%) when solving experimental u-XRD mapping data. The accuracy of the DNN models was significantly improved (90% or above) when training them with the dataset containing both synthetic and small number of labeled experimental u-XRD patterns. This study highlighted the importance of labeled experimental patterns on the training of DNN models to solve u-XRD mapping data from in-situ experiments involving liquid phase.

Ecopia AI announced that it was selected by a Snap Inc. subsidiary to provide high-precision vector mapping data. Ecopia has proven their ability to deliver highly-accurate mapping data at a large-scale with unparalleled speed, said Snap, Inc subsidiary spokesperson. Ecopia leverages advanced AI-based mapping systems to mine the most up-to-date commercially-available geospatial imagery, accessed through its global partner network, outputting high-precision vector maps. For this initiative, Ecopia turned to Airbus for access to their global premium 30-50cm high-resolution imagery database, which is serving as the input imagery for large-scale map content production. "Ecopia has proven their ability to deliver highly-accurate mapping data at a large-scale with unparalleled speed," said Snap, Inc subsidiary spokesperson.

Maps are a great visual tool. We all collect spatial data - holiday destinations, favourite places, areas of interest. But paper maps are limited, and the tools to plot digital maps on a computer can seem daunting. Do you have data that you would like to show on a map but you don't know how to? Or would you like to try a first coding project and want something simple and very visual as a starting project?

In-car satnav systems and mobile mapping apps have made it much easier to travel from one place to another without getting lost, but a new innovation promises to help fix a remaining pain point – getting in the right lane at intersections. Today's mapping apps aren't always much help if you're at an unfamiliar intersection and aren't sure exactly where on the road your car is supposed to be: the apps often don't have the detail or the knowledge to warn you in good time about changing lanes. The system developed by researchers at MIT and the Qatar Computing Research Institute uses satellite imagery to augment existing mapping data, but the smart part is applying artificial intelligence to work out the layout of roads hidden by trees and buildings. It's called RoadTagger, and by deploying machine learning on satellite imagery, the system is able to figure out with a high degree of accuracy some extra details on roads – including, for example, how many lanes they have. That could give drivers an early warning about diverging or merging lanes.

Google and Apple loom so large over the field of digital mapping that it's understandable why it may seem they represent the beginning and the end of this market. But the demands of a wide range of services such autonomous vehicles and smart cities are giving rise to a new generation of mapping competitors who are pushing the boundaries of innovation. The fundamental approach to mapping used by the two giants, mixing satellite imagery and fleets of cars roaming the streets, is becoming archaic and too slow to meet the fast-moving needs of businesses in areas like ecommerce, drones, and forms of mobility. These services often have very specific needs that require real-time updates and far richer data. To address these challenges, new mapping companies are turning to artificial intelligence and crowdsourcing, among other things, to deliver far more complex geodata.

Self-driving cars navigate using both onboard sensors that spot obstacles and detailed, 3-D maps of streets, signs, and infrastructure. But building these maps, and keeping them up to date, is a huge undertaking. Mapper.ai, a San Francisco–based startup, wants to make the process simpler with a service that provides continuously updated maps on demand. The service, which launches publicly today, lets companies select any place in the world they want mapped, provided it has public roads. Mapper then hires local drivers to collect geographic data, converts the data into 3-D maps, and sells the maps--and subsequent updates--via a subscription service.

The robot vacuum now comes with an auto-emptying feature, wherein it attaches to a dock and any dirt or debris is sucked up into a reservoir that houses a large trash bag. This way, users don't have to worry about emptying their Roomba as often and, in turn, it becomes a more hands-free process. These features will come at a price, however. The i7 is priced at $950, making it the company's most expensive model to date. The dock also serves as a charging station for the i7 .