Three residents filed a lawsuit this week against Sonoma County seeking to block code enforcement from using drones to take aerial images of their homes in what the American Civil Liberties Union is calling a "runaway spying operation." The lawsuit, filed by the ACLU Wednesday on behalf of the three residents, alleges that the county began using drones with high-powered cameras and zoom lenses in 2019 to track illegal cannabis cultivation, but in the years since, officials have used the devices more than 700 times to find other code violations on private property without first seeking a warrant. "For too long, Sonoma County code enforcement has used high-powered drones to warrantlessly sift through people's private affairs and initiate charges that upend lives and livelihoods. All the while, the county has hidden these unlawful searches from the people they have spied on, the community, and the media," Matt Cagle, a senior staff attorney with the ACLU Foundation of Northern California, said in a statement. A spokesperson for Sonoma County said the county is reviewing the complaint and takes "the allegations very seriously."

Large Language Models (LLMs) are prone to generating factually incorrect information when responding to queries that involve numerical and statistical data or other timely facts. In this paper, we present an approach for enhancing the accuracy of LLMs by integrating them with Data Commons, a vast, open-source repository of public statistics from trusted organizations like the United Nations (UN), Center for Disease Control and Prevention (CDC) and global census bureaus. We explore two primary methods: Retrieval Interleaved Generation (RIG), where the LLM is trained to produce natural language queries to retrieve data from Data Commons, and Retrieval Augmented Generation (RAG), where relevant data tables are fetched from Data Commons and used to augment the LLM's prompt. We evaluate these methods on a diverse set of queries, demonstrating their effectiveness in improving the factual accuracy of LLM outputs. Our work represents an early step towards building more trustworthy and reliable LLMs that are grounded in verifiable statistical data and capable of complex factual reasoning.

Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. However, few studies focus on accurate travel demand forecasting in large-scale emergency evacuations. Therefore, this study develops and tests a new methodological framework for modeling trip generation in wildfire evacuations by using (a) large-scale GPS data generated by mobile devices and (b) state-of-the-art AI technologies. The proposed methodology aims at forecasting evacuation trips and other types of trips. Based on the travel demand inferred from the GPS data, we develop a new deep learning model, i.e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations. The proposed methodological framework is tested in this study for a real-world case study: the 2019 Kincade Fire in Sonoma County, CA. The results show that SA-MGCRN significantly outperforms all the selected state-of-the-art benchmarks in terms of prediction performance. Our finding suggests that the most important model components of SA-MGCRN are evacuation order/warning information, proximity to fire, and population change, which are consistent with behavioral theories and empirical findings.

During his eight years as community alert and warning manager in Sonoma County, California, Sam Wallis has repeatedly watched wildfires roar through the cities and small towns he protects. Often with little warning, fires have razed homes and charred the area's picturesque hillsides, valleys and vineyards just north of San Francisco. Wallis had to evacuate his own home last year. And in 2017 his property was strewn with wind-blown debris from the deadly, 37,000-acre Tubbs Fire, one of the most destructive in California's history. "The Tubbs Fire was the seminal event, an absolutely massive and fast-moving fire that we had no way of tracking," Wallis says.

At this time of year, periodic rain showers on the north coast of California give way to months of daily sunshine and a wildfire risk that grows in severity until the next fall rains arrive. In Sonoma County, a new set of eyes is watching over the forest. Those eyes will be able to tap into an artificial intelligence program to make sure emergency dispatchers are alerted to actual fires instead of mist rising off the forest floor or steam from the region's numerous natural geysers. The county has entered into a $300,000 contract with South Korea technology firm Alchera to provide artificial intelligence software that can alert fire dispatchers to the precise location of flames or smoke. The two-year pilot project is funded through $3 million in hazard mitigation grants that the Federal Emergency Management Agency awarded to the county.

Sonoma County will bolster its nascent network of fire-lookout cameras with artificial intelligence that aims to automatically identify potential wildfire starts and provide alerts even when no one is watching. County officials announced the program Wednesday after awarding a $300,000 contract to Alchera, Inc., a South Korea-based company that develops algorithms for visual artificial intelligence systems. The technology, which is promising but still in development, is meant to automate Sonoma County's alert-and-warning efforts to provide more of a heads-up in case a wildfire starts, said Chris Godley, the county's emergency management director. "This is really designed to help us catch those extremely early starts, so it gives us that much more time to investigate and, if need be, respond," Godley said. Most of the funding for the new technology comes from a $2.7 million grant the county received from the Federal Emergency Management Agency, with the county chipping in about $75,000.

Sonoma County officials say they will add artificial intelligence technology to help fight wildfires with a 24-7 monitor to track fire outbreaks. The technology will be added to the county's network of wildfire detection cameras that monitor California's backcountry to spot the first outbreak of flames. Many of the cameras are affixed to existing radio communication towers. "This early detection technology will provide emergency managers and first responders with round-the-clock monitoring, a sophisticated addition we are excited to add to our alert and warning toolkit," Sonoma County Board of Supervisors Chair Lynda Hopkins said. Sonoma County, in the heart of Northern California's wine country, has been hit hard by devastating wildfires in recent years.

The most vulnerable silo in the world isn't a forgotten storage drive or an isolated repository. When the flow of information is severed between people, body or byte, they wither away. Who, then, is more siloed in communities than a person without a home? Homelessness is a crisis of isolation and a crisis of information access. Homeless people often have a multitude of needs that span across government services and programs.