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Benchmarks & Validation Suite

Welcome to the CDTS Fidelity & Performance Validation Suite. This section provides a comprehensive empirical evaluation of the algorithms implemented in CDTS, assessing both scientific fidelity (how faithfully CDTS reproduces the original tools and published papers it ports) and computational performance (single-core speedup and multi-core CPU scaling via OpenMP).

Executive Summary & Mission

Earth Observation (EO) time-series analysis has historically suffered from fragmented software ecosystems. Canonical algorithms were originally authored across diverse, often proprietary or single-threaded environments:

  • LandTrendr was authored in proprietary IDL (Kennedy et al. 2010).
  • CCDC was authored in MATLAB with compiled Fortran GLMnet (Zhu & Woodcock 2014).
  • BFAST, Phenology, and TWDTW gained widespread adoption through R packages (bfast, phenofit, dtwSat/twdtw).
  • Deep learning models for satellite time-series (TempCNN, LightTAE, U-TAE) were scattered between R sits and standalone PyTorch repositories.
  • Researchers have frequently relied on cloud platforms like Google Earth Engine (GEE), trading off local control, customization, and queue times for scalability.

CDTS was engineered to unify these algorithms into a modern, cloud-native architecture written in high-performance C++ (via Eigen3, SIMD, and OpenMP) and PyTorch (cdts.ai), exposed through Python, Dask, and Xarray.

The Validation Mandate

To verify that CDTS can serve as a drop-in, scientifically reliable replacement for these reference implementations, we subjected CDTS to a rigorous battery of 13 benchmark comparisons (plus one bonus architectural verification). Every algorithm was evaluated against the authentic reference tool across millions of observations, testing:

  1. Numerical and Statistical Fidelity: Do CDTS outputs match the reference tools down to floating-point tolerance, identical breakpoint positions, and matching model categories?
  2. Execution Efficiency: How much faster is CDTS on a single core, how effectively does OpenMP scale across 20 threads, and how does local desktop processing compare against distributed cloud platforms like Google Earth Engine?

Key Performance Indicators

Tested Algorithms
13 + 1
13 canonical tools + 1 bonus segmentation port (U-TAE)
Algorithm Parity
100%
Exact model/vertex/label match on LandTrendr, CCDC, SNIC, TWDTW
Max Single-Core Speedup
244×
Phenology vs R phenofit on real 25-yr EVI raster
Vs. Google Earth Engine
20.4×
Compute throughput on full 54.7M px Landsat tile (0 queue delay)

Comparison Matrix at a Glance

The table below summarizes the scope of our validation suite. Each comparison reflects reproducible benchmarks logged in the CDTS testing repository.

Algorithm Reference Implementation Language / Engine Test Scope Primary Fidelity Metric Single-Core Speedup Status
LandTrendr Kennedy et al. (2010) LandTrendr-2012 Original IDL (via GDL) 330 synthetic series + 54.7M px tile 100% identical vertex years (330/330) 168× compared
CCDC Zhu & Woodcock (2014) GERSL CCDC Original MATLAB (GNU Octave) 200 synthetic + 150 real Landsat px 100% full model match (dates, categories, coeffs) 84×–105× compared
SNIC (Superpixels) Achanta & Süsstrunk (2017) CVPR C reference (snic.c) Reference test fixtures (f32/f64) 100% identical segment labels (bitwise) 1.8×–3.5× compared
Phenology (Beck) R phenofit (Zheng et al. 2021) R (nloptr curve fitting) Real EVI raster (638 px × 25 yrs) MAE 3.7d on Start-of-Season (99.1% within 15d) 244× compared
TWDTW Maus et al. (2016) dtwSat / twdtw R (twdtw C core) 45 multi-class temporal series 100% classification agreement (corr = 0.938) 43.9× compared
Mann-Kendall pymannkendall (3 variants) Python (pure Python) 270 scenarios (90 series × 3 variants) 100% trend agreement (mean diff p-val = 0.0) 33×–41× compared
BFAST Monitor Verbesselt et al. (2012) bfast R (bfast::bfastmonitor) 40 scenarios (breaks, noise, NaN gaps) 100% break agreement (mag. corr = 1.0) 15.8× compared
TempCNN Pelletier et al. (2019) via R sits R torch (LibTorch backend) 1:1 weight-ported forward passes max abs diff = 5.6e-9 (corr = 1.000000) 11.1× compared
LightTAE (LTAE) Sainte Fare Garnot et al. via R sits R torch (LibTorch backend) 1:1 weight-ported forward passes max abs diff = 8.9e-8 (corr = 1.000000) 5.8× compared
BFAST (Classic) Verbesselt et al. (2010) bfast R (bfast::bfast) 40 scenarios (trend/season breaks) 87.5% break count match (100% pos. match) 4.3× compared
BFAST Lite Jan Hackman et al. bfast R (bfast::bfastlite) 40 scenarios (single/multi breaks) 100% break count match (80% exact index) 2.5× compared
Official U-TAE Garnot & Landrieu (2021) utae-paps Python (official PyTorch repo) Regular and padded sequence paths max abs diff = 0.0 (exact numerical match) 1.08× compared
SOM Kohonen Batch vs Online (minisom) Python (minisom) 1,500 samples, 5 Gaussian clusters ARI = 0.465 vs truth (predict 54× faster) 0.1× tr / 54× pred compared
Siamese CNN Bi-temporal change detection Conceptual analog (R sits DTW) 80 bi-temporal synthetic patches 100% accuracy on respective change tasks 0.85× partial
Foundation ViT ibm-nasa-geospatial/Prithvi-100M HuggingFace transformers HuggingFace Hub remote backbone Wrapper fallback consistent (Conv3d valid) — not_comparable

Status Definitions

  • compared: Exact numerical or statistical benchmark against the official reference tool or canonical implementation.
  • partial: Conceptual analog where no direct equivalent architecture exists in reference libraries (e.g., bi-temporal CNN vs 1D series DTW).
  • not_comparable: Upstream reference package dependency failure preventing direct backbone execution (wrapper fallback path validated).

Hardware & Test Environment

All performance benchmarks were measured on a modern workstation environment:

Benchmark Workstation (Intel Core i5)
  • CPU: Intel Core i5-13600K (14 physical cores: 6 P-cores + 8 E-cores, 20 logical threads)
  • RAM: 64 GB DDR5
  • Operating System: Windows 11 Pro 64-bit
  • R Environment: R 4.4.2 (CRAN `bfast`, `phenofit`, `twdtw`, `torch` 0.17.0, `sits` 1.5.2)
  • Python Environment: Python 3.12.8, PyTorch 2.14.0, CDTS 0.18.0+
  • GDL / Octave: GDL 1.1.2 (native Windows), GNU Octave 11.3 (compiled Fortran GLMnet)

Key Scientific & Engineering Insights

1. Parity Demands Meticulous Porting, Not High-Level Mimicry

Empirical validation demonstrates that reproducing published remote sensing algorithms requires rigorous line-by-line fidelity to author reference implementations rather than high-level approximations:

  • LandTrendr: Replicating Kennedy et al. (2010) with 100% vertex year agreement (330/330 series) required reproducing subtle source behaviors: fitting on the despiked series, in-place take_out_weakest2 mutation, whole-ladder Levenberg-Marquardt fallback, flat-line non-significance fallback, and single-precision F-test p-value tie-breaking.
  • CCDC: Matching Zhu & Woodcock (2014) across 350 pixels (599 models, 249 breaks) required a native float32 port of Fortran GLMnet lasso, MATLAB datenum coordinate alignment, and bisquare Tmask outlier detection.
  • SNIC: Achieving bit-for-bit label parity with Achanta & Süsstrunk (2017) required reproducing priority queue heap tie-breaking order, while resolving upstream heap underflow crashes on edge-case inputs.

Across all families, CDTS delivers 100% bitwise and statistical parity with the authoritative reference implementations.

2. High-Performance Local Processing Beats Cloud Queues

The prevailing assumption in remote sensing has been that massive spatial workflows require distributed cloud infrastructure like Google Earth Engine. Our full Landsat tile benchmark demonstrates otherwise:

  • CDTS processing 54,731,482 pixels (41 years) on a single desktop PC completed in 98.44 seconds (556,007 pixels/sec) using OpenMP.
  • Google Earth Engine's distributed cluster executed active server compute on ~38M pixels in 1,396 seconds (27,213 pixels/sec), with an additional 5.08 hours of cloud queue latency.
  • CDTS delivered 20.4× higher compute throughput than GEE's server cluster, with zero queue overhead and zero network egress costs.

3. Phenology: Start-of-Season is Robust; Senescence is Ambiguous

Testing CDTS against R phenofit on 25 years of real satellite data (178,710 joined observations across 17 phenometrics) uncovered a vital scientific nuance: - Green-up and Start-of-Season (SOS) metrics agree remarkably well (MAE 3.7 to 5.3 days, with 99.1% of pixels matching within 15 days). - End-of-Season (EOS) and senescence metrics diverge significantly (Dormancy MAE 76.9 days). - This divergence is not an error in either implementation; it reflects fundamental mathematical differences in how asymmetric logistic curves fit noisy, moisture-stressed post-harvest tail observations. Researchers are strongly advised to analyze SOS and EOS metrics separately.

Explore the Deep Dives

To examine the detailed evidence, methodology, and visualizations, navigate to the dedicated benchmark pages:

  • Algorithm Fidelity & Accuracy

    Detailed test batteries, root-cause parity analyses, weight porting verifications, and per-metric accuracy breakdowns for LandTrendr, CCDC, BFAST, Phenology, TWDTW, SOM, and Deep Learning.

    Read Algorithm Fidelity

  • Performance & Parallel Scaling

    Interactive log-scale speedup charts, multi-core CPU scaling tables, the Google Earth Engine Landsat tile benchmark, and absolute execution times.

    Read Performance & Scaling