"As
smart homes continue to evolve, they encompass a wide array of consumer-focused IoT devices, including
smartphones, smart TVs, virtual assistants, and CCTV cameras. These devices come equipped with cameras, microphones, and various sensors that can perceive activities within our most intimate spaces – our homes. However, can we truly trust these devices to handle and safeguard the sensitive data they collect?"
International researchers are issuing a dire warning of security and privacy concerns regarding smart homes.
studyfinds.org
"AI can 'see' people through walls using WiFi signals 'This technology may be scaled to monitor the well-being of elderly people or just identify suspicious behaviours at home,' scientists claim..."
‘This technology may be scaled to monitor the well-being of elderly people or just identify suspicious behaviours at home,’ scientists claim
www.independent.co.uk
"A public/private partnership involving NASA and IBM Research has led to the release of NASA's first open-source geospatial artificial intelligence (AI) foundation model for Earth observation data. Built using NASA’s Harmonized Landsat and Sentinel-2
(HLS) dataset, the release of the HLS Geospatial Foundation Model (HLS Geospatial FM) is a milestone in the application of AI for Earth science. The model has a wide range of potential applications, including tracking changes in land use, monitoring natural disasters, and predicting crop yields. The HLS Geospatial FM is available at
Hugging Face(link is external), a public repository for open-source machine learning models."
"Along with NASA and IBM Research, this collaborative effort included Clark University’s Center for Geospatial Analytics, ESA (European Space Agency), USGS, and the U.S. Department of Energy’s Oak Ridge National Laboratory."
"The IBM watsonx FM stack is currently running in NASA’s Science Managed Cloud Environment...."
"Organized by IMPACT in collaboration with the Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (IEEE GRSS) Earth Science Informatics Technical Committee (ESI TC), the workshop covered the development of FMs using HLS data and included a hands-on exercise in fine-tuning the FM using IBM’s watsonx.ai."
A new AI foundation model based on NASA's HLS data is a milestone in the application of AI for Earth science
www.earthdata.nasa.gov
Future Impacts by AI on Mapping and Modernization Authored By: Steve Snow Esri Senior National Government Industry Strategy Specialist Part 1 of a 7 part series exploring GIS and Artificial Intelligence We are now living in an age where technology is the backbone of successful mapping...
community.esri.com
"NASA and India's space agency are teaming up to launch a satellite that uses radar to study even the tiniest details of Earth's crust."
"NISAR is the first radar imaging satellite to use dual frequencies (the L and S microwave bands). This will let it systematically map the Earth's crust at an exceptional level of detail — it can detect changes under 1cm (0.4in) across."
NASA and India's space agency are teaming up to launch a satellite that uses radar to study even the tiniest details of Earth's crust.
www.engadget.com
"We designed this system to monitor the entire globe and generate a lot of data. From that data, we can understand patterns of activity, spot anomalies, count objects, see manufacturing rates and track objects across the supply chain. We also built our system for extremely low latency. Everywhere in the chain, from tasking to downlinking, processing and exploiting the imagery, we look for ways to make it as fast as possible. Because of those two reasons, we knew that we needed an AI-powered system.
Tasking is automated. Our AI reads the world’s news, including hyper-local foreign language news sources all the way from the Associated Press and BBC. It identifies emerging events around the world and automatically tasks our satellites to take an image. This is really helpful for natural disasters or anything that needs a quick response."
"Now, we’re processing other forms of imagery, including synthetic aperture radar."
"We use machine learning to help make that imagery consistent and ready to be analyzed by algorithms or models. We do things like detect clouds and run our analysis with the nice clear data. We use computer-vision techniques to extract objects and to identify patterns. We find roads, buildings, vessels and planes. We can identify change through time. In addition, we measure soil water content and land surface temperature."
"RF data, particularly in its raw form, can be very complex to understand. Machine learning tools help pull out trends within the raw data to help the analysts reach conclusions faster and derive value from our data."
Learn about aerospace industry developments in AIAA’s Aerospace America Magazine, the voice of the aerospace industry.
aerospaceamerica.aiaa.org
"GCA aims to virtually aggregate vast amounts of commercial and open-source satellite data that is available in multiple modes—optical, synthetic aperture radar (SAR), and radio frequency (RF)—in a common cloud-based repository with automated curation tools. The platform and tools would provide DoD geospatial analysts global situational awareness, event detection, monitoring, and tracking capabilities beneficial to U.S. forces around the world."
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