Cercana Systems

What if interacting with geospatial data was as simple as asking a question? Our latest blog post explores how Large Language Models (LLMs) can serve as intelligent orchestrators for geospatial analysis—translating human language into precise spatial queries while ensuring reliable, verifiable results. By combining AI-driven workflows with trusted data sources and human oversight, we’re designing interfaces that are intuitive, multilingual, and useful for decision-makers, regardless of their GIS background. (Blog link in comments)

"Crunchy Data Warehouse gives you Zero ETL"? We load data directly into a Crunchy Data Warehouse, powered by PostgreSQL, without the need for extensive data transformation beforehand. Crunchy Data Warehouse is eliminating the traditional "Extract, Transform, Load" (ETL) process, allowing for faster data access and analysis with minimal data manipulation required. This is achieved by leveraging features like materialized views and integrations with external data sources like S3, enabling near-real-time querying on raw data. How do we achieve zero ETL: → Direct Data Access: Users can query data stored in external systems like S3 directly within PostgreSQL using foreign tables, minimizing the need to pre-load and transform data. → Materialized Views: By creating materialized views, pre-calculated summaries of data can be readily available for faster querying, further reducing the need for complex ETL pipelines. → Iceberg Integration: Crunchy Data Warehouse leverages the Iceberg table format, which allows for efficient data management and flexible schema changes while maintaining a unified view of the data. What are the benefits of a zero ETL data warehouse? ◆ Reduced Latency:?Faster access to data for analytics due to minimal data transformation. ◆ Simplified Data Pipelines:?Less complex ETL processes, making data integration easier to manage. ◆ Cost Efficiency:?Potentially lower compute costs by avoiding unnecessary data processing steps.

When disasters strike, the first response doesn't always come from professionals, but often from those directly affected: local communities. Integrating community-based monitoring systems into disaster management leverages the firsthand experience and immediate presence of local populations. These systems empower residents to play a crucial role in monitoring threats, sharing critical information, and mobilizing quicker responses, which is essential for reducing the impact of disasters. Here's how these systems are changing the landscape of emergency management: 1?? ?????????? ?????????????????? ??????????????????????: People understand their environment and are the first to spot changes, making their input invaluable for identifying risks early. 2?? ???????????? ???????????????? ??????????: Systems managed at the community level can relay information rapidly, speeding up emergency responses when every second counts. 3?? ?????????????????? ???????????? ??????????????????: Involving communities in monitoring efforts raises awareness and preparedness, making safety a shared responsibility. 4?? ????????-?????????????????? ??????????????????: Utilizing local networks and volunteers can reduce costs while enhancing the reach and effectiveness of disaster management programs. 5?? ???????????????????????? ????????????????????: Continuous involvement in disaster preparedness activities builds a community's capacity to withstand and recover from disasters. ?? Interested in seeing how these approaches can be tailored to aid your community? Visit our page to discover the tools and techniques that can elevate your community's readiness and response capabilities! ?? ?????????? ???????? ?????????? ?????? ?????????????????? ???????????????????? ??????????????????: https://hubs.la/Q034pxLd0 #CommunityMonitoring #DisasterReadiness #EmergencyResponse #PublicSafety #SustainableSolutions #CommunityEmpowerment #NewLightTech

Geospatial Foundation Models (GeoFMs) have the potential to dramatically reduce barriers to working with satellite imagery. ?? https://ds.io/geofm Instead of spending months collecting training data, you can now get reliable results across geographic and temporal features. You can process continental-scale datasets in hours and extract precise insights from multiple data sources (satellite, drone, and ground-level imagery). GeoFMs create specialized embeddings for every location on Earth, providing a unified foundation for diverse applications—from ecosystem monitoring to urban development tracking. We've spent the last year delving into the details of creating and applying foundation models, and today, we're sharing some of our learnings in this blog. ?? https://ds.io/geofm We'd love to hear how you might apply these capabilities in your work. #EarthObservation #AI #RemoteSensing #SpatialComputing #FoundationModels #Geospatial

I was happy to work on this post with Linda Stevens and the Cloud-Native Geospatial Forum (CNG). In the journey from workstations to desktops to client-server to the #web and now the #cloud, mainstream #GIS has seen numerous shifts in its approach to #data management and #geospatial #analytics. Cloud-native formats and tools optimize GIS for modern computing. Check it out here. https://lnkd.in/e2fY7kjw