Search Infrastructure Built for Location Intelligence
Fixing what modern geo search keeps getting wrong. From STAC catalogs to AI retrieval, Lucenia is built for the geo workloads that break other systems.
Geo-native, not just geo-enabled.
"Intelligence analysts spend as much as half their time reviewing imagery they're already familiar with."— U.S. Department of Defense / NGA, 2018
Problems Not Being Addressed Today
Geospatial search has been treated as an afterthought. These are the real challenges that geo professionals face daily—and that most platforms ignore.
Geospatial Catalogs are Broken
Most systems just store catalog metadata (STAC, OGC Records, etc.) with basic bbox + datetime filtering. Missing: fast spatial intersection, geo-aware ranking, and billion-scale performance.
Analytics Tools Misused for Search
Parquet, Iceberg, and DuckDB excel at batch analytics but result in full scans, brute-force geo predicates, and latency spikes under concurrency.
Geo is Just a Filter
Most engines reduce geo to simple intersection checks. Real search needs coverage scoring, distance ranking, and spatial relevance.
Tessellation Cost Explosion
Over-tessellation leads to ballooning indexes, erratic latency, and teams simplifying geometry just to survive operationally.
Multi-Tenant Noisy Neighbors
One tenant's large polygon query can evict caches and spike CPU, causing fragile multi-tenancy and runaway costs.
Hybrid Geo + Vector is Aspirational
Most stacks retrieve vectors then filter geo (or vice versa), overscan aggressively, and collapse under load.
Why Aren't These Problems Being Fixed?
The geospatial industry has structural incentives that prevent progress.
Geo Search is a Data-Structure Problem, Not a Feature Problem
Doing geo right requires deep work: spatial indexing theory, numerical stability, approximation tradeoffs, query planning, merge behavior, cache locality, and distributed execution. Most vendors focus on visible features, not invisible correctness and performance.
Analytics Platforms Are Incentivized to Overreach
Columnar and lakehouse systems are incredibly successful at analytics, so there's pressure to claim they can also do search. But analytics engines are optimized for throughput over latency, scans over point lookups, and batch over concurrency. Trying to make one system do both well almost always results in doing one poorly.
Cloud Economics Reward Inefficiency
Managed services profit when indexes are bigger, queries are slower, clusters need more nodes, and workloads are overprovisioned. Fixing geo efficiency cuts directly against those incentives.
How Lucenia Addresses These Problems
Purpose-built for the unique demands of geospatial data: massive scale, complex queries, and real-time performance.
STAC-Native Search
First-class STAC support with fast spatial indexing, accurate spatiotemporal queries, geo-aware AOI ranking, and metadata-rich retrieval without brute-force scans.
Billions of records, sub-second queriesSpatial Joins That Push Down to the Index
A spatial join normally means building an R-Tree and testing candidate geometry pair by pair. Lucenia pushes the predicate into the BKD index instead, probing only the documents that can possibly match, with concurrent segment search. Benchmarked at 295x faster than an Apache Sedona R-Tree join on the same workload.
295x faster than Sedona R-TreeSearch Where Search Belongs
Lucenia handles retrieval, filtering, ranking, and geo relevance. Analytics systems handle what they're built for. Clear separation improves performance and lowers cost.
Right tool for each jobAccuracy Without Index Bloat
Controlled approximation, predictable index size, and stable query latency. Avoids the tessellation death spiral that plagues other systems.
Predictable performance at scaleMulti-Tenant Efficiency
Fewer nodes, less memory pressure, better cache behavior, and stable latency under concurrent spatial queries. Lower TCO and fewer surprises.
True isolation, lower costsGeo + Vector + AI Ready
Built for geo-constrained vector search, geo-aware reranking, spatial grounding for RAG, and multimodal retrieval pipelines from day one.
Future-proof architectureAn Agent Toolbelt for Geospatial Work
Geospatial questions are rarely one query. Lucenia ships the tools an agent needs to work the way an analyst does — locate, compare against a baseline, check the finding, and write it up — each one a registered tool the model selects by reading what it does.
Where things are
GeoLayerToolAnswers “where is this?” with something an agent can reason about and a map can draw.ContourToolAnswers “where is this concentrated?” — the shape of the data rather than the things in it.GeoLineToolAnswers “what path did this take?” — the route something actually travelled, from its fixes.RouteToolAnswers “how do I get from here to there” over an indexed network.
What the imagery shows
ChangeDetectionToolAnswers “what changed between these two dates?” — the question a revisiting sensor exists to answer.TemporalCoverageToolAnswers “when was this imaged, and what am I missing?” — the baseline behind every change answer.SimilarImageryToolAnswers “show me more that look like this” over an archive of imagery.ReprojectImageryToolFetches imagery already moved into the projection the map is drawn in.
Meaning and reasoning
ConceptSearchToolResolves a term through your ontology and searches the classes it actually covers.QueryPlanningToolTurns a question into a plan the engine can execute, before anything is retrieved.RAGToolGrounded generation over what was retrieved, with the retrieval still inspectable.VectorDBToolEmbedding search when the question is genuinely about similarity rather than meaning.
Finishing the work
CheckAnalysisToolChecks an analysis before it is presented as an answer.ReportToolAssembles findings into a report an analyst can hand to someone else.VisualizationToolProduces the chart or overlay that makes the result legible.WriteToScratchPadToolKeeps intermediate state across a multi-step investigation.
Plus the index and connector tools an agent needs to orient itself — ListIndexTool, CatIndexTool, IndexMappingTool, SearchIndexTool, MLModelTool, and ConnectorTool — all reachable over the Model Context Protocol.
Built for Real Geospatial Workloads
From imagery archives to real-time tracking, Lucenia powers search across every geospatial workflow.
Satellite Imagery & Earth Observation
Index and search petabytes of satellite imagery metadata. Find the exact imagery you need by location, date, cloud cover, or custom attributes in milliseconds.
STAC Catalog Search at Scale
Native STAC support with proper spatiotemporal indexing. Search billions of assets with geo-aware ranking and metadata-rich retrieval.
Climate & Environmental Data
Query massive climate datasets, weather patterns, and environmental monitoring data with complex spatial and temporal constraints.
Agriculture & Precision Farming
Power precision agriculture with field-level search across crop health imagery, soil data, and weather patterns at farm scale.
Defense & Intelligence Workflows
Support analyst workflows with fast retrieval across classified imagery archives. Reduce time spent reviewing familiar imagery.
Logistics & Fleet Tracking
Track and search for assets, vessels, and vehicles across global geographies with sub-second query response and real-time updates.
"Many platforms support geo. Many store STAC. Many query Parquet. Very few understand that geo search is a retrieval problem, not an analytics problem."
Lucenia isn't just geo-enabled — it's geo-native.
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