Geospatial Analytics Platform for Energy and Utilities Accelerate analysis to eliminate risks, accelerate site selection, deliver on drilling and work-over planning, and grid modernization. REQUEST A DEMO Companies Accelerating Outcomes with Wherobots and Apache Sedona How Energy Companies Solve Infrastructure Analytics Challenges The fastest, most complete spatial intelligence for site selection, wildfire mitigation, complete infrastructure and supply chain visibility and more. Slow wildfire risk analysis Complex renewable siting Limited grid visibility Siloed infrastructure data Manual transmission planning Slow wildfire risk analysis Industry Problem Why Wildfire Risk Analysis Takes Days or Weeks Assessing wildfire ignition probability and vegetation encroachment across thousands of miles of transmission lines takes days or weeks with traditional GIS tools, delaying critical mitigation decisions during high risk seasons. Wherobots solution How to Analyze Wildfire Risk 90% Faster Wherobots analyzes vegetation proximity, weather patterns, historical ignition data, and infrastructure conditions across your entire infrastructure territory in minutes instead of days. The platform processes raster and vector data together to identify high-risk zones requiring immediate attention, delivering 90% faster risk identification than traditional approaches.. 90% Faster Risk ID Complex renewable siting Industry Problem Why Renewable Site Selection Delays Clean Energy Deployment Evaluating optimal locations for utility-scale solar and wind projects requires analyzing multiple factors including solar radiation, wind patterns, land use, topography, environmental constraints, and grid proximity—a time-intensive process that delays clean energy deployment. Wherobots solution How to Evaluate Millions of Renewable Energy Sites in Hours Wherobots processes terabytes of satellite imagery, meteorological data, and environmental datasets to identify and rank optimal renewable energy sites in hours. The platform handles multi-criteria spatial analysis at scale, evaluating millions of potential sites per minute while considering transmission proximity and hosting capacity. Millions of Sites Analyzed/minute Limited grid visibility Industry Problem Why Inaccurate Asset Data Prevents Real-Time Grid Capacity Decisions Inaccurate asset locations, incomplete spatial data assets, and batch updates to network models prevent energy teams from making real-time decisions about grid capacity, DER integration, and infrastructure investments. Wherobots solution How to Create a Spatial Digital Twin of Grid Infrastructure Create a comprehensive spatial digital twin of your infrastructure. Process billions of asset records, network topology data, and real-time sensor feeds to maintain an always-current view of grid and field conditions and capacity across all capacity levels. 100% Real-time Grid View Siloed infrastructure data Industry Problem Why Disconnected GIS, SCADA, and AMI Systems Make Predictive Maintenance Difficult Critical asset information exists in disconnected systems including GIS, SCADA, AMI, and work management platforms—making it difficult to correlate spatial data with operational performance for predictive maintenance and capital planning. Wherobots solution How to Unify Spatial Data Using 300+ Spatial SQL Functions Unify spatial data from multiple sources in Wherobots Cloud. Join asset records with real-time operational data, outage history, and maintenance records using Spatial SQL. WherobotsDB’s 300+ spatial and spark functions enable complex analyses that reveal hidden patterns in asset performance across your network. 300+ Spatial Functions Manual transmission planning Industry Problem Why Manual Transmission Corridor Planning Takes Months and Bottlenecks Grid Expansion Planning new transmission corridors requires analyzing right-of-way constraints, environmental impacts, population density, and existing infrastructure across vast geographic areas—a months-long process that bottlenecks grid expansion projects Wherobots solution How to Evaluate Thousands of Transmission Routes Simultaneously Evaluate thousands of potential transmission routes simultaneously by analyzing terrain, land use, environmental constraints, and cost factors. Wherobots processes multi-terabyte raster datasets representing elevation, land cover, protected areas, and infrastructure to identify optimal corridors. 90% Time Savings “We’ve added ten times more data than earlier versions of this concept, and we can now do things with AI and on-demand analytics that have never been done before.” Eric Pollard CEO, ParGo Process Billions of Infrastructure Data Points with Unified Spatial Analytics Schedule a demo to see how leading energy teams process billions of location insights and work with raster and vector data in the same platform—accelerating wildfire risk analysis, renewable siting, grid modernization, and other energy and utility use cases. Talk to us Energy & Utility Resources: Technical Examples and Case Studies Explore use cases, code examples, and webinars showing how energy companies solve wildfire risk, renewable siting, and infrastructure challenges with spatial analytics How to Detect Solar Panels in Satellite Imagery Using Raster Inference Learn how to extract solar panel locations from satellite and aerial imagery using SQL or Python with open-source machine learning models—useful for competitive analysis and renewable asset mapping. LEARN MORE Raster Data Processing Benchmark: Wherobots vs Google Earth Engine vs BigQuery Compare performance for raster operations, spatial joins at scale, and running Earth observation models across three leading geospatial platforms. READ MORE RasterFlow for Planetary-Scale Earth Intelligence See how RasterFlow bridges AI and physical world data to accelerate Earth observation analysis for energy and utility applications. WATCH NOW Frequently Asked Questions About Geospatial Analytics for Energy & Utilities How does Wherobots process billions of utility infrastructure data points at scale? Wherobots is built on Apache Sedona, the leading open-source distributed spatial analytics engine, optimized for planetary-scale workloads. WherobotsDB automatically scales compute resources to handle datasets from gigabytes to petabytes—whether you’re analyzing a single substation or processing decades of satellite imagery across your entire service territory. The serverless architecture eliminates cluster provisioning and performance tuning, allowing energy teams to write spatial queries and let the platform handle scaling automatically. What deployment options does Wherobots offer for energy and utility companies? Wherobots offers three deployment options: (1) fully managed cloud service, (2) bring-your-own-cloud (BYOC), and (3) Virtual Private Cloud (VPC) deployment for energy and utility companies. How much faster is Wherobots than traditional GIS platforms for utility analytics? Wherobots delivers up to 20x faster performance than standard Apache Sedona for complex spatial joins, raster processing, and multi-layered analyses. Traditional GIS tools were designed for single-machine processing and struggle with modern utility data volumes, while Wherobots provides distributed processing across cloud compute clusters with true parallel execution. In practice, wildfire risk assessments that previously took overnight complete in under an hour, and hosting capacity calculations that required days finish in minutes, enabling 90% faster analysis for critical utility decisions. Can Wherobots process both vector and raster data for energy and utility applications? Yes. Wherobots excels at unified vector-raster analytics, which is essential for utility applications that combine infrastructure data (vector) with satellite imagery and environmental data (raster). The platform’s out-of-database raster support processes massive satellite imagery collections, elevation models, and vegetation indices without loading them into memory. WherobotsAI enables utilities to run computer vision models on satellite imagery at scale—such as detecting vegetation encroachment near transmission lines or identifying suitable land parcels for solar development. How long does it take to implement Wherobots for utility analytics? Most utilities deploy a proof-of-concept in 2-4 weeks, focusing on a specific use case like wildfire risk mapping or renewable site analysis. Wherobots Cloud is a fully managed service compatible with Apache Sedona APIs—existing Sedona code or SQL queries run on Wherobots with minimal changes. The platform provides Jupyter notebook environments, sample datasets, and 300+ ready-to-use spatial functions, allowing energy teams to start building spatial analytics immediately without extensive setup or infrastructure provisioning. Is Wherobots suitable for real-time utility data processing? Wherobots excels at batch processing large-scale spatial analytics but is less suited for millisecond-latency real-time applications. The platform works best for analytical workloads that process substantial volumes of spatial data, such as daily vegetation monitoring, hourly hosting capacity updates, or scenario planning for grid expansion. For operational systems requiring sub-second response times, utilities can use Wherobots for computationally intensive analytics and spatial ETL pipelines that feed into real-time operational systems. Get started Ready to learn more about how Wherobots can transform your energy workflows? Request Demo try wherobots now
How to Detect Solar Panels in Satellite Imagery Using Raster Inference Learn how to extract solar panel locations from satellite and aerial imagery using SQL or Python with open-source machine learning models—useful for competitive analysis and renewable asset mapping. LEARN MORE
Raster Data Processing Benchmark: Wherobots vs Google Earth Engine vs BigQuery Compare performance for raster operations, spatial joins at scale, and running Earth observation models across three leading geospatial platforms. READ MORE
RasterFlow for Planetary-Scale Earth Intelligence See how RasterFlow bridges AI and physical world data to accelerate Earth observation analysis for energy and utility applications. WATCH NOW
