PRISM·lidarcloud turns raw drone and aerial LiDAR into bare-earth DEMs, surface and canopy models, and classified point clouds — one fully automated cloud run, reproducible to the byte, validated against surveyed control and USGS 3DEP. The compute takes a few hours. Your part takes a click.
If you process LiDAR for a living, none of this is news — it's the standard toolchain, and everyone fights it. If these sound like your week, you're who we built this for.
The desktop pipeline is the bottleneck — not flying. Field crews collect faster than legacy tools can process.
The capable tools are expensive and license-locked — seats, dongles, maintenance, and one machine that can run the job.
Conventional DEMs round off the banks, scarps and breaklines your clients are actually paying you to capture.
Legacy desktop pipelines won't reproduce their own surface — rerun the same data and get a slightly different DEM, which is hard to defend in review.
In the desktop stack, classification is a separate manual step — another tool, another license, another day of cleanup.
Four steps from raw flight data to client-ready files — all in the browser.
Send your LAS/LAZ securely to the cloud. No conversion, no preprocessing on your side.
Automated processing produces the bare-earth DEM, DSM, canopy height model at 25 cm, a classified point cloud, and per-cell uncertainty.
Review everything in the browser — 2D map, 3D point cloud, elevation profiles, and click-to-identify on any surface.
Upload survey control points for an accuracy report following ASPRS Positional Accuracy Standards, cross-check independently against USGS 3DEP, and export CAD-ready DXF / LandXML / GeoTIFF with FGDC metadata. Aligning to a prior survey? Change detection runs automatically — erosion and deposition volumes, canopy damage and growth — with the maps and statistics as pages in the report.
Every image on this page is rendered directly from PRISM·lidarcloud output rasters — real coastal mangrove and creek-cut forest sites, real data.
Bare earth recovered beneath heavy coastal canopy — with the subtle relief intact, not flattened into a guess.
The same run that strips canopy from your DEM hands it back as a 25 cm canopy height model, ready for habitat, forestry, and clearance work.
Unretouched screenshots of the PRISM·lidarcloud workspace on the same real sites shown above. Everything here — every layer, panel and tool — ships with every job.
Most pipelines optimize for pretty. PRISM·lidarcloud optimizes for defensible.
Banks, scarps and grade breaks are preserved, not smoothed away — measured: scarp crest within ~1 cm of survey, where the reference workflow rounds the same crest off by ~26 cm.
The same input produces the byte-identical deliverable, run after run. When it's questioned in audit, you can prove it.
Ground, vegetation tiers, buildings and water — labeled in the same run, not bolted on as a manual second pass.
NAVD88 (GEOID18) heights and State Plane coordinates out of the box — including county-correct Florida zones.
Accuracy statements following ASPRS Positional Accuracy Standards (2014 & 2023 conventions) from your checkpoints, plus an independent cross-validation against USGS 3DEP — in a client-ready report with a plain-language interpretation.
Nothing to install, nothing to maintain, no seats to count. Open the browser, upload, and pay for what you process.
Every deliverable PRISM·lidarcloud ships is backed by a validation chain: cross-validation against the industry-reference workflow, surveyed ground control, independent national references, public benchmarks, and bit-level reproducibility. This page shows the methodology and the measured results.
The engine is a proprietary, self-contained scientific pipeline — no third-party GIS licenses anywhere in the chain. Each stage is versioned, and every run records the exact configuration that produced it.
Before any classification, the cloud is profiled — point density, canopy structure, ground visibility, terrain roughness. Processing parameters adapt to the regime (coastal mangrove behaves nothing like an open ag field), including automatic adaptation across two orders of magnitude in point density, from sparse aerial collections to dense drone surveys.
A multi-stage adaptive ground filter separates bare earth from vegetation, buildings, and noise — engineered specifically to keep the terrain features generic tools smooth away: bank tops, erosion scarps, ditch inverts, grade breaks. Outlier scrubbing is density-aware, so legitimate sparse returns under canopy survive.
Bare-earth, surface, and canopy-height models at 25 cm. The ground surface uses continuity-preserving interpolation that recovers the convex breaks — crest lines, scarp lips — that straight-line interpolation rounds off. Every DEM ships with a per-cell uncertainty raster, so you can see exactly where the model is well-supported and where canopy occlusion forced interpolation.
The delivered cloud carries ground, vegetation height tiers, buildings, and water — assigned with shape- and regime-gated rules tuned to avoid the classic false positives (mangrove canopy is not a building; a dry field is not a lake).
Your surveyed checkpoints produce an accuracy assessment following ASPRS Positional Accuracy Standards (both the 2014 edition and the 2023 second edition conventions). An independent cross-check against the USGS 3DEP national elevation model runs automatically. Deliverables export with FGDC metadata, and the accuracy report is a client-ready PDF. Processing against a reference adds double-round co-registration on outlier-excluded stable ground and two-epoch change detection — erosion/deposition volumes with a printed minimum level of detection, canopy damage and growth — rendered as report pages. Comparisons against the coarse national raster are explicitly labelled screening-grade in the report.
One upload starts the whole chain. The only decision you make is which reference your survey is aligned to and compared against — a prior survey you upload, the USGS 3DEP national model fetched automatically, or none. Everything else runs without you.
Co-registration runs before differencing, so what the change maps show is change — not misregistration; the alignment residual is printed next to the result. Validation runs against references you don't control: checkpoints you surveyed and a national model we can't tune. And the report discloses everything that was applied — every offset, every comparison, every skipped step — so a reviewer can retrace the run.
The manual tools are for verification and refinement; the pipeline has already done these steps automatically. Upload control points, run the independent 3DEP cross-check, re-run the alignment or apply a vertical adjustment whenever you want to see the evidence yourself — the deliverables never depend on you doing any of it.
Calibration runs on full-scale sites spanning the toughest cover types — coastal mangrove, dense creek-cut forest, and agricultural research plots, 66 to 342 million points each — processed end-to-end and compared against the industry-reference commercial workflow, surveyed control, and national reference data.
| What was measured | Result | Reference |
|---|---|---|
| Bare-earth DEM agreement across terrain regimes | within 2–5 cm mean differencemangrove coast, creek-cut forest, agricultural plots | industry-reference commercial workflow |
| Coastal erosion-scarp crest elevation | within ~1 cm of surveyed truththe reference workflow rounds the same crest off by ~26 cm | field survey |
| Reproducibility of every raster & cloud | byte-identical (MD5-verified)same input ⇒ same output, independent of machine load or core count | repeated end-to-end runs |
| Agreement with the national elevation model | σ ≈ 0.4–0.5 m on dense-forest terrainsurface-to-surface check dominated by the reference's own uncertainty and collection-epoch differences — not PRISM checkpoint accuracy | USGS 3DEP |
| Public benchmark — forest & rural ground filtering | median ≈5% total error (2–11% per sample)terrain-matched configuration, documented; in the range of the best published filters of its class | ISPRS ground-filter test (independent labels) |
| Public benchmark — noise-contaminated metropolis | 70.2 ground-IoU, untuned — the best non-learned resultthe benchmark authors "carefully chose the fine-tuned parameters of the four filters for each test data, through the trial-and-error strategy" (Qin et al. 2023, ISPRS J. Photogramm. Remote Sens. 202:246–261); each still scored ≤ 39.9 and the commercial reference collapsed to 0.0. PRISM ran production defaults — no tuning, no training. Neural networks trained on the benchmark: 76.6–89.1. | OpenGF Test II (independent labels) |
| Building removal from the bare-earth DEM | all reference buildings correctly removedroof points classified, ground interpolated from true surrounding terrain | surveyed ag site with known structures |
What we don't claim: benchmark scores we haven't earned yet. Our public-benchmark program is ongoing — dense-urban scenes at sparse aerial density are an active engineering focus, and we publish results when they are real. Accuracy on your site, with your checkpoints, is the test that matters — which is why the trial is free.
The engine's development discipline: no change ships unless it is quality-up, or provably neutral — verified on full-scale calibration sites before promotion, every time.
Coastal mangrove, dense creek-cut forest, and agricultural plots — chosen because each terrain breaks a different assumption. Every engine change re-validates against all of them.
Hillshade review from above — and from below. Underside relief exposes classification artifacts that hide in a top-down view. If it isn't clean from both sides, it doesn't ship.
Candidate changes must reproduce the locked production output byte-for-byte wherever they claim no effect. Drift of a single byte fails the gate.
The per-cell uncertainty raster is calibrated against surveyed checkpoints, so "how good is the DEM here?" gets a numeric answer — not just a color ramp.
Every processed site can be compared against the USGS national elevation model in one click — an outside reference we don't control.
Accuracy statements follow ASPRS Positional Accuracy Standards (Ed. 1 2014 and Ed. 2 2023 conventions), with FGDC metadata on exports. Built to support a licensed surveyor's review — never to replace it.
Ellipsoidal heights natively; NAVD88 orthometric via GEOID18 with rigorous per-cell undulation — not a single site-wide constant. Vertical adjustment from your control points with the full before/after disclosure in the report.
State Plane coordinate systems with county-correct zone selection (Florida's zone boundaries follow counties, not latitude — we resolve them the way a surveyor would), US survey feet handled exactly.
DXF (3D breakline-ready polylines, points, TIN faces) and LandXML 1.2 surfaces that rebuild correctly in Civil 3D-class software — northing/easting order and all the ingest foot-guns handled.
Every output carries the exact engine and module versions that produced it. Two years from now, you can state precisely how a deliverable was made — and regenerate it.
Most tools stop at a classified cloud. PRISM·lidarcloud carries each job through the steps a survey practice bills time for — CAD interchange, datum bookkeeping, accuracy statistics, and the documentation a reviewing professional expects — in the same one-click run.
DXF and LandXML surfaces in State Plane coordinates with NAVD88 (GEOID18) heights, ready for Civil 3D-class software — scale factor and full datum lineage stamped on every file, so nothing arrives ambiguous.
Accuracy statistics following ASPRS Positional Accuracy Standards, computed against your surveyed control points — plus an independent cross-check against the USGS 3DEP reference surface. Residuals disclosed, not summarized away.
Re-fly a site and the engine co-registers the epochs and quantifies what moved — erosion and deposition volumes with a printed minimum level of detection — turning a repeat flight into a monitoring deliverable.
An AI-assisted report formatted for a licensed professional's review workflow: methodology, datum lineage, residuals, and limitations stated up front, with a plain-language interpretation your client can read.
Where the licensed professional comes in: every PRISM deliverable is clearly stamped DRAFT — not a sealed survey. Outputs are intended for review and adoption by your firm's — or your client's — licensed surveyor or geomatics professional. PRISM does not provide licensed surveying services.
Run your hardest site through it yourself — free trial, full engine, next to your current tool's output and your checkpoints if you have them. Keep whichever is better.
Start your free trial Launch PRISM·lidarcloudStart in the cloud, or hand us the hard ones.
SaaS — upload and process in the browser, priced per area.
We process your backlog — or your hardest sites — for you.
Run your hardest site through it yourself — free trial, full engine, no card. Put our deliverables next to your current tool's. Keep whichever is better.
Prefer it done for you? Pro Services — or write to us about anything.
Where PRISM·lidarcloud is heading — capabilities in active development, building on the same one-click run you use today.
Individual-tree segmentation and tree-level 3D structural metrics, derived from the same one-click run that produces your terrain and surface models.
Automated tree-level and stand-level assessment of storm impact and forest-health change between flights — extending the change detection that ships today.
Additional ASPRS feature classes in the delivered point cloud, widening the semantic detail available straight out of the automated run.
The team
Built by the team behind a decade of airborne-LiDAR research from the University of Florida, Florida Agricultural and Mechanical University, and the United States Forest Service.
University of Florida
Associate Professor of forest ecology and geomatics in the School of Forest, Fisheries, and Geomatics Sciences. He co-directs the Spatial Ecology and Conservation (SPEC) Lab and the GatorEye Unmanned Flying Laboratory, the drone-borne LiDAR program at the heart of this work. His research spans spatial scales from the globe to the leaf, carrying him from the Amazon to Hawaii and back to the New England forests he grew up in.
University of Florida
Research faculty in the Center for Latin American Studies and co-director of the Spatial Ecology and Conservation (SPEC) Lab, where she also co-directs the GatorEye Unmanned Flying Laboratory. Her work centers on the human side of landscape change — how people and ecosystems shape each other across the tropics. She is core faculty in UF's Tropical Conservation and Development program.
USDA Forest Service · Florida A&M University
Biological scientist and Gulf restoration program specialist with the USDA Forest Service, working with federal, state, and private partners to plan and carry out ecosystem and hydrologic restoration across the Gulf Coast. He is co-founder and co-director of FAMU's Center for Spatial Ecology and Restoration, a USDA Forest Service–Florida A&M partnership. His expertise runs through remote sensing, LiDAR, and ecological modeling of southern forests.
USDA Forest Service · Florida A&M University
GIS program manager for the USDA Forest Service's National Forests in Florida and co-director of FAMU's Center for Spatial Ecology and Restoration. He brings more than two decades of hands-on geospatial experience to applied LiDAR and forest-mapping work across Florida's public lands.
The PRISM app family builds on more than a decade of proprietary GatorEye algorithmic development by Broadbent & Almeyda Zambrano.
The underlying technology was developed at the University of Florida; commercialization proceeds through UF’s technology-licensing process (UF Innovate | Tech Licensing).