less3d 0.3.2

GPU-accelerated 3D radiative transfer model for remote sensing

LESS 3.0

GPU + CPU accelerated 3D radiative transfer model for remote sensing simulation

LESS 3.0 is a complete reimplementation of the LESS model — a widely-used 3D radiative transfer framework in quantitative remote sensing research. Built from the ground up with NVIDIA OptiX ray tracing and a modern Python API, LESS 3.0 inherits the physical foundations of the original LESS while adding GPU acceleration, a redesigned scripting interface, and full physical coupling of energy balance, photosynthesis, and solar-induced chlorophyll fluorescence (SIF).

The original LESS remains available at lessrt.org. LESS 3.0 is the recommended path for new projects.

---

Key Features

• GPU + CPU dual backend — Runs on any machine. With an NVIDIA GPU, simulation is typically 10–50× faster; without one, most features still work via the Embree CPU backend.
• Clean Python API — Designed for remote sensing scientists. Build a scene in 10 lines of code.
• Digital twin architecture — Build the scene once, simulate many times. Update illumination or surface properties without rebuilding geometry.
• Multi-sensor support — Optical imager (RGB / multispectral / hyperspectral), thermal infrared, LiDAR point cloud (ALS + TLS), and SIF fluorescence imager.
• Full physical coupling — 3D radiation field → energy balance → photosynthesis → SIF, all tightly coupled to the Monte Carlo ray tracing engine.
• Interactive 3D viewer — scene.show() opens a browser-based viewer to inspect the scene and preview simulation results.

---

Installation

pip install less3d

Pre-built wheels for Windows and Linux are provided for Python 3.10–3.13. No compiler, CUDA toolkit, or additional runtime installation required.

	CPU mode	GPU mode (recommended)
Processor	Any modern x86-64	NVIDIA GPU, compute capability ≥ 5.0
VRAM	—	4 GB+ (8 GB+ recommended)
GPU driver	—	NVIDIA driver ≥ 535
OS	Windows 10/11, Linux	Windows 10/11, Linux
Python	3.10 – 3.13	3.10 – 3.13

3D energy balance solving currently requires a GPU. All other features (optical, thermal, LiDAR, SIF) run on CPU.

---

Quick Start

import less

# Create a 10 m × 10 m bare-soil scene
scene = less.Scene()
scene.size = 10.0
scene.terrain = less.Terrain(property=less.Lambertian(reflectance=0.3))
scene.illumination = less.DirectDiffuse(sun_zenith=30, sun_azimuth=150)
scene.build()

# Simulate a nadir RGB image
image = scene.simulate(less.OpticalImager(
    less.Orthographic(image_size=256),
    bands=[650, 550, 450],   # R, G, B (nm)
    quality=128,
))
image.save("output.png")

Thanks to the digital twin architecture, updating scene parameters does not require a rebuild:

# Change sun angle and re-simulate — no rebuild needed
scene.illumination = less.DirectDiffuse(sun_zenith=60, sun_azimuth=90)
image2 = scene.simulate(sensor)

---

Simulation Capabilities

Domain	What you can simulate
Optical	RGB, multispectral, and hyperspectral images; BRF / BRDF angular sampling; NDVI and vegetation indices
Thermal infrared	Per-component temperature scenes (sunlit/shaded leaf, soil, trunk); thermal radiance images
LiDAR	Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point clouds
Vegetation physiology	3D PAR radiation field, full energy balance, photosynthesis, solar-induced chlorophyll fluorescence (SIF) imaging

---

Forest Scene Example

import less
import numpy as np

scene = less.Scene()
scene.size = 30.0
scene.terrain = less.Terrain(property=less.Lambertian(reflectance=0.12))
scene.illumination = less.DirectDiffuse(sun_zenith=35, sun_azimuth=225)

# Broadleaf tree with PROSPECT leaf optical properties
birch = less.Object("birch", mesh="birch.obj")
birch.set_property("leaves", less.Prospect(cab=40, car=8, cw=0.012, cm=0.009, N=1.5))

# Random planting positions
xy = np.random.rand(50, 2) * 30
positions = np.c_[xy, np.zeros(50)]
scene.add(birch, positions=positions)
scene.build()

# Multispectral image (green, red, NIR)
image = scene.simulate(less.OpticalImager(
    less.Orthographic(image_size=512),
    bands=[550, 670, 800],
    quality=256,
))
image.save("forest.tif")   # GeoTIFF preserves physical radiance values

---

Built-in Examples and Viewer

import less

less.examples.list()                  # list all built-in scenes
less.examples.run("autumn_forest")    # launch interactive viewer
less.examples.run("maize_field")

scene.show()   # open 3D viewer in browser (Scene / Simulate / Products tabs)

---

Validation

LESS 3.0 is cross-validated against the original LESS on reference scenes:

Scene	Description	Max RRMSE
Val01	Lambertian ground	0.02%
Val02	Box canopy	0.22%
Val03	Ellipsoid forest	0.83%
Val04	Forest + diffuse sky	2.17%
Val05	PROSPECT-D leaves	0.24%
Val06	fPAR vs sun zenith angle	0.62%
Val07	Thermal IR	0.17%

Full validation reports are regenerated on each release. See tests/validation/.

---

Documentation and Tutorials

Full documentation with step-by-step tutorials:

Topics covered: coordinate system · terrain and surface properties · illumination models · optical imaging · BRDF/BRF sampling · thermal infrared · LiDAR · energy balance · photosynthesis · SIF · visualization · scene serialization

---

Citing

If you use LESS 3.0 in your research, please cite the original LESS paper:

Qi, J., Xie, D., Yin, T., Yan, G., Gastellu-Etchegorry, J.P., Li, L., Zhang, W., Mu, X., Norford, L.K. (2019). LESS: LargE-Scale remote sensing data and image Simulation framework over heterogeneous 3D scenes. Remote Sensing of Environment, 221, 695–706.

---