Lists (7)
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gearslaboratory / RVS
Forked from rlank/RVSRangeland Vegetation Simulator
Sensor aware ground points classification and derived products e.g., CHM, DEM, DSM, Terrain derivatives at user defined grid resolution in fully automated fashion
O5080 - QUIC-Fire GUI ment to run QUIC-Fire and other applications
Source code for the LANDFIRE Total Fuel Change Tool
LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning
Estimate voxelized leaf area density metrics from lidar for wildfire risk modeling, treatment effect monitoring, hydrology analysis, wildlife habitat suitability, and more.
One line code to get any remote sensing foundation model embeddings for any place and any time
Files and code for the National Scale Volume Biomass (NSVB) models
Utilities to support lidar (airborne, mobile, terrestial) applications at the landscape-, forest-, and tree-scale to facilitate ecologcial data collection, manipulation, analysis, modelling, and vi…
A 0.6-meter resolution canopy height model for the contiguous United States using NAIP imagery.
A Self-contained Latex Book/Note Writing Tutorial.
Output management tools for LANL DUET program
The #1 AI Harness for Building Resumes, PDFs, Cover Letters & more, locally with 100+ LLMs support.
Extract a tree list, CHM, DTM, and more from .las|.laz point cloud data
IDE support and convenience utilities for PDAL (Point Data Abstraction Library) and USGS 3DEP.
A summary of related works about flow matching, stochastic interpolants
A PyTorch library for implementing flow matching algorithms, featuring continuous and discrete flow matching implementations. It includes practical examples for both text and image modalities.
Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality
One-step image-to-image with Stable Diffusion turbo: sketch2image, day2night, and more
Another interactive 3D fluid simulation in WebGPU
Creates an analysis ready sentinel-1 SAR image collection in Google Earth Engine by applying additional border noise correction, speckle filtering and radiometric terrain normalization.
FastAPI Best Practices and Conventions we used at our startup
Python bindings to the Behave fire modelling framework