We propose a lifelong 3D mapping framework that is modular, cloud-native by design and more importantly, works for both hand-held and robot-mounted 3D LiDAR mapping systems. Our proposed framework comprises of dynamic point removal, multi-session map alignment, map change detection and map version control. First, our sensor-setup agnostic dynamic point removal algorithm works seamlessly with both hand-held and robot-mounted setups to produce clean static 3D maps. Second, the multi-session map alignment aligns these clean static maps automatically, without manual parameter fine-tuning, into a single reference frame, using a two stage approach based on feature descriptor matching and fine registration. Third, our novel map change detection identifies positive and negative changes between two aligned maps. Finally, the map version control maintains a single base map that represents the current state of the environment, and stores the detected positive and negative changes, and boundary information. Our unique map version control system can reconstruct any of the previous clean session maps and allows users to query changes between any two random mapping sessions, all without storing any input raw session maps, making it very unique. Extensive experiments are performed using hand-held commercial LiDAR mapping devices and open-source robot-mounted LiDAR SLAM algorithms to evaluate each module and the whole 3D lifelong mapping framework.

With growing interest in Procedural Content Generation (PCG) it becomes increasingly important to develop methods and tools for evaluating and comparing alternative systems. There is a particular lack regarding the evaluation of generative pipelines, where a set of generative systems work in series to make iterative changes to an artifact. We introduce a novel method called Generative Shift for evaluating the impact of individual stages in a PCG pipeline by quantifying the impact that a generative process has when it is applied to a pre-existing artifact. We explore this technique by applying it to a very rich dataset of Minecraft game maps produced by a set of alternative settlement generators developed as part of the Generative Design in Minecraft Competition (GDMC), all of which are designed to produce appropriate settlements for a pre-existing map. While this is an early exploration of this technique we find it to be a promising lens to apply to PCG evaluation, and we are optimistic about the potential of Generative Shift to be a domain-agnostic method for evaluating generative pipelines.

What may appear to be an image of Tacoma is, in fact, a simulated one, created by transferring visual patterns of Beijing onto a map of a real Tacoma neighborhood.Zhao et al., 2021, Cartography and Geographic Information Science A fire in Central Park seems to appear as a smoke plume and a line of flames in a satellite image. Colorful lights on Diwali night in India, seen from space, seem to show widespread fireworks activity. Both images exemplify what a new University of Washington-led study calls "location spoofing." The photos -- created by different people, for different purposes -- are fake but look like genuine images of real places. And with the more sophisticated AI technologies available today, researchers warn that such "deepfake geography" could become a growing problem.

What may appear to be an image of Tacoma is, in fact, a simulated one, created by transferring visual patterns of Beijing onto a map of a real Tacoma neighborhood. A fire in Central Park seems to appear as a smoke plume and a line of flames in a satellite image. Colorful lights on Diwali night in India, seen from space, seem to show widespread fireworks activity. Both images exemplify what a new University of Washington-led study calls "location spoofing." The photos -- created by different people, for different purposes -- are fake but look like genuine images of real places. And with the more sophisticated AI technologies available today, researchers warn that such "deepfake geography" could become a growing problem.

I'm not sure if you're aware, but the launch of Apple Maps went poorly. After a rough first impression, an apology from the CEO, several years of patching holes with data partnerships and some glimmers of light with long-awaited transit directions and improvements in business, parking and place data, Apple Maps is still not where it needs to be to be considered a world-class service. Apple, it turns out, is aware of this, so it's re-building the maps part of Maps. It's doing this by using first-party data gathered by iPhones with a privacy-first methodology and its own fleet of cars packed with sensors and cameras. The new product will launch in San Francisco and the Bay Area with the next iOS 12 beta and will cover Northern California by fall. Every version of iOS will get the updated maps eventually, and they will be more responsive to changes in roadways and construction, more visually rich depending on the specific context they're viewed in and feature more detailed ground cover, foliage, pools, pedestrian pathways and more. This is nothing less than a full re-set of Maps and it's been four years in the making, which is when Apple began to develop its new data-gathering systems. Eventually, Apple will no longer rely on third-party data to provide the basis for its maps, which has been one of its major pitfalls from the beginning.

I went for a drive in San Francisco's Mission District last month. It was late morning, and there wasn't much traffic. As I wended my way through the side streets, I avoided a double-parked armored car and steered around construction sites. Though it might have seemed like an aimless outing, my brief sortie was anything but. Every centimeter I drove, every object I encountered, and even the double line I crossed to avoid the Brinks truck was being recorded by a device affixed across the top edge of the windshield, just above the rearview mirror.