Sometimes you need to create a satellite navigation tracking device that communicates via a low-power mesh network. [Powerfeatherdev] was in just that situation, and they whipped up a particularly compact solution to do the job. As you might have guessed based on the name of its creator, this build is based around the ESP32-S3 PowerFeather board. The PowerFeather has the benefit of robust power management features, which makes it perfect for a power-sipping project that’s intended to run for a long time. It can even run on solar power and manage battery levels if so desired. The GPS and LoRa gear is all mounted on a secondary “wing” PCB that slots directly on to the PowerFeather like a Arduino shield or Raspberry Pi HAT. The whole assembly is barely larger than a AA battery. It’s basically a super-small GPS tracker that transmits over LoRa, while being optimized for maximum run time on limited power from a small lithium-ion cell. If you’re needing to do some long-duration, low-power tracking task for a project, this might be right up your alley. https://hackaday.com/2024/10/17/tiny-lora-gps-node-relies-on-esp32/

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.