Multiple motors or servos are the norm for drones to achieve controllable flight, but a team from MARS LAB HKU was able to a 360° lidar scanning drone with full control on just a single motor and no additional actuators. Video after the break. The key to controllable flight is the swashplateless propeller design that we’ve seen a few times, but it always required a second propeller to counteract self-rotation. In this case, the team was able to make that self-rotation work so that they could achieve 360° scanning with a single fixed LIDAR sensor. Self-rotation still needs to be slowed, so this was done with four stationary vanes. The single rotor also means better efficiency compared to a multi-rotor with similar propeller disk area. The LIDAR comprises a full 50% of the drone’s weight and provides a conical FOV out to a range of 450m. All processing happens onboard the drone, with point cloud data being processed by a LIDAR-inertial odometry framework. This allows the drone to track and plan its flight path while also building a 3D map of an unknown environment. This means it would be extremely useful for indoor or underground environments where GPS or other positioning systems are not available. All the design files and code for the drone are up on GitHub, and most of the electronic components are off-the-shelf. This means you can build your own, and the expensive lidar sensor is not required to get it flying. This seems like a great platform for further experimentation, and getting usable video from a normal camera would be an interesting challenge.   Single Rotor Drone Spins For 360 Lidar Scanning | Hackaday

The fall update to Global Mapper includes numerous usability updates, processing improvements, and with Pro, beta access to the Global Mapper Insight and Learning Engine which contains deep learning-based image analysis tools. Global Mapper is a complete geospatial software solution. The Standard version excels at basic vector, raster, and terrain editing, with Global Mapper Pro expanding the toolset to support drone-collected image processing, point cloud classification and extraction, and many more advanced image and terrain analysis options. Version 26.0 of Global Mapper Standard focuses on ease-of-use updates to improve the experience and efficiency of the software. A Global Search acts as a toolbox to locate any tool within the program, and a source search in the online data streaming tool makes it easier to bring online data into the application. Updates for working with 3D data include construction site planning to keep all edited terrain for a flattened site within a selected area and the ability to finely adjust the vertex position of 3D lines in reference to terrain in the Path Profile tool. Perhaps the largest addition to Global Mapper Pro v26.0 is the availability of the new Insight and Learning Engine which provides deep learning-based image analysis. Available with Global Mapper Pro for a limited time for users to test and explore, users can leverage built-in models for building extraction, vehicle detection, or land cover classification. These models can even be fine-tuned with iterative training to optimize the analysis for the data area.

Responding to the escalating threats from climate change, biodiversity loss, pollution and extreme weather and the need to take action to address these threats, this forward-looking strategy outlines a bold vision for Earth science through to 2040. By leveraging advanced satellite-based monitoring of our planet, ESA aims to provide critical data and knowledge to guide action and policy for a more sustainable future. ESA’s Director of Earth Observation Programmes, Simonetta Cheli, said, “As a space agency, it is our duty to harness the unique power of Earth observing technology to inform the critical decisions that will shape our future. “Our new Earth Observation Science Strategy underscores a science-first approach where satellite technology provides data that contribute to our collective understanding of the Earth system as a whole, so that solutions can be found to address global environmental challenges.” “The choices we make today help create a more sustainable world and propel the transformation towards a resilient, thriving global society.” The new Science Strategy presents a bold and ambitious vision for the future of ESA’s Earth Observation Programmes. It shifts focus towards understanding the feedbacks and interconnections within the Earth system, rather than targeting specific Earth system domains.

You're a hotshot working to contain a wildfire. The conflagration jumps the fire line, forcing your crew to flee using pre-determined escape routes. At the start of the day, the crew boss estimated how long it should take to get to the safety zone. With the flames at your back, you check your watch and hope they were right. Firefighters mostly rely on life-long experience and ground-level information to choose evacuation routes, with little support from digital mapping or aerial data. The tools that do exist tend to consider only a landscape's steepness when estimating the time it takes to traverse across terrain. However, running up a steep road may be quicker than navigating a flat boulder field or bushwacking through chest-high shrubs. Firefighters, disaster responders, rural health care workers and professionals in myriad other fields need a tool that incorporates all aspects of a landscape's structure to estimate travel times. In a new study, researchers from the University of Utah introduced Simulating Travel Rates in Diverse Environments (STRIDE), the first model that incorporates ground roughness and vegetation density, in addition to slope steepness, to predict walking travel times with unprecedented accuracy. "One of the fundamental questions in firefighter safety is mobility. If I'm in the middle of the woods and need to get out of here, what is the best way to go and how long will it take me?" said Mickey Campbell, research assistant professor in the School of Environment, Society and Sustainability (ESS) at the U and lead author of the study. The authors analyzed airborne Light Detection and Ranging (LiDAR) data and conducted field trials to develop a remarkably simple, accurate equation that identifies the most efficient routes between any two locations in wide-ranging settings, from paved, urban environments to off-trail, forested landscapes. They found that STRIDE consistently chose routes resembling paths that a person would logically seek out—a preference for roads and trails and paths of least resistance. STRIDE also produced much more accurate travel times than the standard slope-only models that severely underestimated travel time. "If the fire reaches a firefighter before they reach safety, the results can be deadly, as has happened in tragedies such as the 2013 Yarnell Hill fire," said Campbell. "STRIDE has the potential to not only improve firefighter evacuation but also better our understanding of pedestrian mobility across disciplines from defense to archaeology, disaster response and outdoor recreation planning." Airborne estimates of on-the-ground travel STRIDE is the first comprehensive model to use airborne LiDAR data to map two underappreciated factors that inhibit off-road travel—vegetation density and ground surface roughness—as well as steepness. LiDAR is commonly used to map the structure of a landscape from the air, Campbell explained. A LiDAR-equipped plane has sensors that shoot millions of laser pulses in all directions, which bounce back and paint a detailed map of structures on the ground. The laser pulses bounce off leaf litter, gravel, boulders, shrubs and tree canopies to build three-dimensional maps of terrain and vegetation with centimeter-level precision. The authors compared STRIDE performance against travel rates gleaned from three field experiments, in which volunteers walked along 100-meter-transects through areas with existing LiDAR data. "Getting travel times from a variety of volunteers allowed us to account for a range of human performance so we can make the most accurate predictions of travel rates in a diversity of environments," said co-author Philip Dennison, professor and director of ESS. The first field trials were in September of 2016. At the time, LiDAR datasets were relatively rare in the western U.S. Over the last decade, the U.S. Geological Society has developed LiDAR maps covering most of the country. "When we first started looking into wildland firefighter-mobility a decade ago, there were lots of people studying how fire spreads across the landscape, but very few people were working on the problem of how firefighters move across the landscape," said Campbell, then a doctoral student in Dennison's lab at ESS. "Only by combining these two pieces of information can we truly understand how to improve firefighter safety." That study, published in 2017, was the first attempt to map escape routes for wildland firefighters using LiDAR. The second trial took place in August of 2023 in the central Wasatch Mountains of Utah to capture a wider set of undeveloped, off-path landscape conditions than did the first experiment, including nearly impassibly steep slopes and extremely dense vegetation. The final experiment was in January of 2024 in Salt Lake City to test the STRIDE model in an urban environment. In total, about 50 volunteers walked more than 40 100-meter transects of highly varied terrain. Putting it together The study compared STRIDE against a slope-only model to generate the most efficient routes, or the least-cost paths, in the mountains surrounding Alta Ski Resort in the Wasatch Mountains, Utah. Geographers and archaeologists have been using least-cost path modeling to simulate human movement for decades; however, to date most have relied almost exclusively on slope as the sole landscape impediment. The authors imagined a scenario in which emergency responders are planning to rescue an injured hiker. From a central point, they chose 1,000 random locations for the hiker and asked both models to find the least-cost path. STRIDE chose established roads around the ski areas, followed trails and in some cases major ski slopes, to avoid patches of forest or dense vegetation. STRIDE reused established paths as long as possible before branching off, reinforcing the idea that STRIDE identified the routes most intuitive for somebody on the ground. "The really cool thing is that we didn't supply the algorithm with any knowledge of existing transportation networks. It just knew to take the roads because they're smoother, not vegetated and tend to be less steep," said Campbell. In contrast, the slope-only model had few overlapping pathways, with little regard for roads or trails. It sent rescuers through dense vegetation, dangerous scree fields and forested areas. The authors believe that STRIDE will have an immediate impact in the real world—they've made the STRIDE model publicly available so that anyone with LiDAR data and gumption can make their work or recreation more efficient, with a higher safety margin. "If you don't consider the vegetation cover and ground-surface material, you're going to significantly underestimate your total travel time. The U.S. Forest Service has been really supportive of this travel rate research because they recognize the inherent value of understanding firefighter mobility," said Campbell. "That's what I love about this work. It's not just an academic exercise, but it's something that has real, tangible implications for firefighters and for professionals in so many other fields." The authors recently used a slope-based travel rate model to update the U.S. Forest Service Ground Evacuation Time (GET) layer, which allows wildland firefighters to estimate travel time to the nearest medical facility from any location in the contiguous U.S. Campbell hopes to use STRIDE to improve GET, allowing for more accurate estimates of evacuation times. links: https://www.nature.com/articles/s41598-024-71359-6