Geospatial Engineering · Software · Technical Services
LCIC builds the cluster processing pipelines, open-format reformatting, and AI inferencing layer that turns petabyte-scale geospatial and remote sensing data into mission-ready intelligence. A force multiplier that takes the basemap lifecycle off customer teams — in the cloud, in the enclave, at the edge.
Pipeline
Every dataset we touch resolves into one of five layers — base map, raster, elevation, vector, point cloud. We ingest each one, optimize it for streaming, run inferencing where it pays, and disseminate on open formats. Scroll through the stack.
Data products are currently sold through SDSR (opens in new tab) .
The geographic reference plane every other layer registers against. Coastlines, administrative boundaries, transportation networks — the canvas the rest of the stack is painted on, served from a tile pyramid for sub-second pan-and-zoom at any scale.
Overhead imagery from satellite, aerial, and SAR sources, ingested at petabyte scale and reformatted into GeoPackage, ATAK, MBTiles, Cloud Optimized GeoTIFF and NITF, with multi-scale overviews and spatial indexes for sub-second query against any tile.
Digital terrain and surface models with slope, contour, and line-of-sight derivatives generated alongside. Produced from photogrammetric, LiDAR, and SAR-derived sources without privileging any modality.
Points, lines, polygons, feature collections as GeoPackage, GDB, Shapefiles and Mapbox Vector Tiles, R-tree-indexed for fast spatial query. Inferenced features from the imagery and point-cloud layers above are written back into this same vector substrate.
Dense point clouds segmented by a 3D Transformer into ground, vegetation, vehicles, power lines, fences, poles, and structures. Served as Cloud Optimized Point Clouds for streaming at standoff.
Pipeline
Every dataset we touch resolves into one of five layers — base map, raster, elevation, vector, point cloud. We ingest each one, optimize it for streaming, run inferencing where it pays, and disseminate on open formats. Scroll through the stack.
Data products are currently sold through SDSR (opens in new tab) .
The geographic reference plane every other layer registers against. Coastlines, administrative boundaries, transportation networks — the canvas the rest of the stack is painted on, served from a tile pyramid for sub-second pan-and-zoom at any scale.
Overhead imagery from satellite, aerial, and SAR sources, ingested at petabyte scale and reformatted into GeoPackage, ATAK, MBTiles, Cloud Optimized GeoTIFF and NITF, with multi-scale overviews and spatial indexes for sub-second query against any tile.
Digital terrain and surface models with slope, contour, and line-of-sight derivatives generated alongside. Produced from photogrammetric, LiDAR, and SAR-derived sources without privileging any modality.
Points, lines, polygons, feature collections as GeoPackage, GDB, Shapefiles and Mapbox Vector Tiles, R-tree-indexed for fast spatial query. Inferenced features from the imagery and point-cloud layers above are written back into this same vector substrate.
Dense point clouds segmented by a 3D Transformer into ground, vegetation, vehicles, power lines, fences, poles, and structures. Served as Cloud Optimized Point Clouds for streaming at standoff.
High-velocity geospatial pipelines
A turn-key lifecycle for every geospatial modality — raster, vector, elevation, video, LiDAR. Sources route into a single C++ processing engine and emit cloud-optimized open formats for the tools customer teams already run.
Acquired or customer-supplied source data across 1D, 2D, and 3D modalities.
A unified C++ engine runs spatial indexing, multi-scale overview generation, tile schema standardization, and specialized compression.
Cloud-optimized open formats, served over OGC-compliant endpoints. No vendor lock-in.
Turn-key acquisition or customer-supplied source data. On-premise deployment available for sensitive or extreme-volume archives where data egress is not on the table.
Spatial indexes, multi-scale overviews, standardized tile schemes, and specialized compression — built to run hardware at its limit.
Compute drawn to data gravity
At petabyte scale, moving data costs more than moving compute. Pipelines run where the data already lives — at the edge, in the customer enclave, in whichever cloud holds the archive.
AWS, Azure, and GCP — the same pipelines run across all three. No re-platforming, no proprietary adapter layer, no managed-service lock-in.
Most organizations have the data. Few have the optimization expertise to make it usable at the tactical edge. That's the work.
Egressing a petabyte is rarely the right answer. Pipelines deploy into the customer enclave or onto the edge platform itself, so the compute moves and the data stays.
Full-stack high-performance software ecosystem
A production-grade C and C++ software ecosystem for the geospatial lifecycle — built for resource saturation, OGC compliance, and edge-to-cloud portability.
Performance over interpreted pipelines
Engineering basis: native C and C++ with zero-copy I/O, NUMA-aware threading, SIMD vectorization, and CUDA acceleration — measured against equivalent Python or scripting-language implementations.
Multi-threaded, asynchronous execution engineered for resource saturation.
Memory and I/O efficiency tuned for petabyte-scale archives.
MQTT, gRPC, and video codec primitives wired in by default.
C++ APIs for high-throughput training data prep and on-device inferencing.
Proactive metrics surface across distributed pipeline stages.
Async I/O and parallel-computing primitives, surfaced — not abstracted away. The pipeline composes on top of them.
Linux io_uring, Windows IOCP, splice() / vmsplice() for zero-copy data movement.
NUMA-aware thread placement, SIMD vectorization, CUDA and TensorRT for GPU acceleration.
Open Geospatial Consortium service classes we deliver against. Every output is a public format; every endpoint speaks a published wire protocol.
AI inferencing for geospatial phenomenology
A 1D GNN prunes the vector substrate to topologically valid targets, a 2D CNN inferences overhead imagery, and a 3D Transformer segments point clouds — ensembled for cross-modal confidence. Compiled for the tactical edge so derived intelligence ships without the backhaul.
Message-passing architecture that treats 1D vector strings as a dual graph, propagating geometric and semantic context before rasterization. Prunes the search space to high-probability zones so downstream 2D compute is only spent on topologically valid targets.
Topological priors
Transfer-learning baseline designed to fine-tune against LIMDIS and FOUO phenomenology without re-training from scratch. Inferenced features are written back into the same vector substrate analysts already query.
Detection targets
Segments dense LiDAR into named semantic classes. Output drives streaming 3D tiles and feeds back into the vector layer for cross-modal correlation against imagery detections.
Segmented classes
The 1D GNN, 2D CNN, and 3D Transformer are correlated for cross-modal confidence — a topologically primed vector segment, a LiDAR-segmented vehicle, and an imagery-detected vehicle co-located in space score higher than any one alone. Ensemble output is written back into the vector layer as confidence-weighted features.
On-device inferencing across CPU and GPU. CUDA NPP for NVIDIA hardware; Intel IPP and SIMD for CPU-only deployments. Compiled for low-power edge platforms so derivative intelligence ships from the sensor — no cloud backhaul assumed.
Tactical edge readiness
Capabilities the pipeline architecture, training data, and deployment targets are tuned for — disconnected operation, end-to-end metadata persistence, and out-of-the-box compatibility with the tools customer teams already run.
Turn-key high-resolution basemaps for disconnected, disrupted, intermittent, and limited environments. Edge-deployed, no internet assumed.
Acquisition dates, security classifications, data licenses, and provenance records persist end-to-end through every stage of the pipeline.
Derivative products compatible out of the box with ATAK, WinTAK, Cesium, ESRI, and QGIS. Open formats throughout — no proprietary viewer required.
Key differentiators
A short statement of what the engineering team brings to the work, phrased to remain accurate without specific customer disclosure.
Tactical geospatial delivery to U.S. defense and intelligence end-users.
Onboard platform software and petabyte-scale cloud-native pipelines from the same engineering team.
Fault-tolerant pipelines that run hardware at its limit, the same across enterprise clusters and tactical-edge platforms.
Contact
Working a basemap, lifecycle, or tactical-edge inferencing problem that doesn't fit a vendor catalog? Tell us about it.
Castle Pines, Colorado USA