fb892ea9f2
Comments, docstrings, logs, CLI help, map UI, legends, PDF sheet, scripts, compose files and AGENTS.md are now English. Data keys stay unchanged (relief_oriente, densite_sol, visualisations/, API JSON keys, link params). Wrong comments and help defaults found along the way are corrected. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
161 lines
8.5 KiB
Markdown
161 lines
8.5 KiB
Markdown
# Ground classification — options, benchmark and references
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> **Status (up to date)**: the pipeline default is `--ground-classification ign`
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> with `--ign-classes sol` (IGN vendor ground class 2), extracted directly with
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> laspy (`_extract_ign_ground` in `dtm.py`), with PDAL as fallback. `auto`,
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> `smrf` and `csf` remain selectable via `--ground-classification`. Map-triggered
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> generation always uses `ign` (no classification/reconciliation setting is
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> exposed in the map UI).
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Synthesis note for choosing/improving the ground-detection algorithm.
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Context: IGN LiDAR HD tiles (Lambert 93), areas of strong relief / rock outcrops /
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dense forest where the ground is under-classified and the DTM shows large holes.
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Reference tile: `LHD_FXX_0999_6778_PTS_LAMB93_IGN69`
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- 65,047,919 points, ~1 km², resolutions 0.5 m and 0.2 m.
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- Class breakdown (vendor pre-classification):
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class 2 (ground) **25.79%**, class 5 (high vegetation) **63.2%**,
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class 3 (low vegetation) 7.85%, class 4 (medium vegetation) 2.66%,
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class 1 0.47%, class 6 0.01%. No points in class 0.
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- Holes in the existing DTM (before correction): **43.5%** at 0.5 m,
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**44.1%** at 0.2 m (tile extent, header bounds).
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## Measured benchmark (PDAL, 1 km²)
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| Method | Time | Ground points | Ground surface* | Holes* |
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|---|---|---|---|---|
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| **IGN** (vendor pre-classification) | **9.4 s** | 25.8% | 84.6% | 15.4% |
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| **SMRF** | 326.3 s | 36.1% | 90.4% | 9.6% |
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| **CSF** | 355.2 s | 17.6% | 46.9% | 53.1% |
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\* "Ground surface" computed over the point extent (point cloud bounding box),
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at 0.5 m. Final DTM hole percentages (header bounds, larger) are higher: see
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the reference tile above.
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## A. Geometric filters (current PDAL stack)
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- **IGN** (vendor pre-classification, class 2): the fastest (~9 s). Reliable
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where the vendor has confidence; holes under dense forest / steep relief.
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No parameter to tune.
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- **SMRF** — Pingel, Clarke & McBride 2013, *ISPRS J. Photogramm. Remote
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Sens.* 77:21-30. **Raster-based** filter (operates on a DSM, not on points),
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hence faster than point-based filters; **minimizes type I errors**
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(ground omission) → well suited when ground is scarce (forest). Best
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coverage of the three here (90.4%) but ~5.4 min/tile.
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- **CSF** — Zhang et al. 2016, *Remote Sensing* 8(6):501. Inverted cloth
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draped over the point cloud; simple, accurate, but **the cloth no longer
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touches the ground on steep/hilly terrain** → poor classification. Slowest
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here and worst on this tile. Best reserved for urban areas.
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- **PTD/PTIN** (Progressive TIN Densification) — Axelsson 2000, ISPRS
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Congress. **Literature's winner**: most robust on complex terrain +
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forest (Moudrý et al. 2020, *Measurement* 150:107047; Cai et al. 2019,
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*Remote Sensing* 11(9):1037) and the fastest (lidR benchmark: PTD ~20 s
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vs CSF ~156 s vs PMF ~1800 s). **NOT available in the PDAL version
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bundled in this image** (`filters.ground` / TIN missing) — would need to
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be added to use it (or via lidR / a custom implementation).
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## B. Fast hybrid (original plan, only partly kept)
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> Only step 1 below is in use today. Step 2 (lowest-return floor) was
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> dropped and step 3 (`_interpolate_holes`) is no longer called by the DTM
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> builder: see "Synthesis / decision" for the current gap handling.
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**PTD / Wack & Wimmer** principle (Wack & Wimmer 2002, *ISPRS Archives*
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XXXIV/3A:293-296: DTM from lowest return, excluding the lowest 1% per cell
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to discard outliers):
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1. **Base = IGN pre-classification** (class 2, ~9 s, reliable and official).
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2. **Measured gap filling**: for each cell with no ground point, take the
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**robust lowest return** (min of the 99% of points in the cell) → adds
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*measured* ground where the vendor failed (rock outcrops, clearings,
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forest floor).
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3. **Topographic inpainting** of the remaining gaps (terrain-aware
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interpolation, `dtm.py:_interpolate_holes`, still present as a helper
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but not called by `create_dtm_fast`).
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Expected at the time: **continuous** DTM (0% holes), robust in
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forest/relief, **~10-15 s/tile** instead of 326-355 s. No GPU dependency,
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no training.
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## C. AI / ML models (supervised — require labels)
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Caveat (Qin et al. 2023, *ISPRS J. Photogramm. Remote Sens.*
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202:246-261): **everything is supervised**; the main risk is
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**generalization** — a model trained on one region degrades elsewhere.
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No fully unsupervised DL filter published to date.
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**Point-based (3D):**
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| Model | Year | Architecture | Accuracy | Speed (~/km², GPU) |
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|---|---|---|---|---|
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| KPConv / RandLA-Net (Qin, OpenGF) | 2021 | KPConv / RandLA-Net | 97.8% OA, DTM RMSE 0.20 m, ground IoU 95% | 0.5-2.5 min |
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| PFCN (Jin, *IEEE JSTARS* 13:3958) | 2020 | point-FCN | Te 1.73%, Kappa 93.9% | ~1/3 the cost of PointNet++ |
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| Terrain-Net (Li, *Remote Sensing* 14(22):5798) | 2022 | KPConv + self-attention | OA 98%, mIoU 0.933 | parameter-free at transfer |
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| MSVC (Štroner, *Remote Sensing* 17(4):615) | 2025 | 9x9x9 voxel DNN | beats CSF on F-score | — |
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**Rasterized (directly output the DTM — closest to our need):**
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| Model | Year | Architecture | Result |
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| Precursor (Rizaldy, *ISPRS Annals* IV-2:231) | 2018 | 2D FCN | Te 5.22%, 78x faster |
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| DeepTerRa / ALS2DTM (Lê, *IEEE JSTARS* 15:2778) | 2022 | GAN pix2pix (U-Net) | DTM RMSE < 1 m, filter + interpolation in one pass |
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| DSM2DTM (Bittner, *ISPRS Annals* X-1/W1-2023:925) | 2023 | U-Net (EfficientNet) | non-ground mask + per-pixel ground height |
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**Training datasets**: OpenGF (Qin et al., CVPRW 2021,
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arXiv:2101.09641 — 47.7 km², 542 M pts); ALS2DTM (Lê et al. 2022,
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arXiv:2206.03778 — 52 km², 1.66 billion pts, urban/forest/mountain).
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**Costs / obstacles for our case**: (1) labels → to be generated as
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pseudo-labels (high-quality SMRF/PTD output on a representative sample of
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our tiles) or pre-training on OpenGF/ALS2DTM; (2) generalization across the
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diverse terrain of LiDAR HD (plain/forest/mountain/urban); (3)
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infrastructure: checkpoint + GPU inference path in the Docker image.
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**Best fit if going the AI route**: a **rasterized U-Net (DSM2DTM-style)** —
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rasterize the point cloud into multi-channel grids (elevation, slope,
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curvature, density, return statistics), output a ground mask + ground
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height. 2D = very fast and trivial to deploy on GPU, merges filtering +
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interpolation. KPConv/RandLA-Net is more accurate in pure 3D but heavier to
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deploy.
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## Synthesis / decision
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- "Fast" hard constraint + low maintenance → **fast hybrid (B)**
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(~10-40 s/tile, zero training, zero GPU). ← **chosen approach, base
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IMPLEMENTED** (step 1 only, see below)
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- Base = IGN pre-classification (fast: ~5 s with the direct laspy
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extraction, vs ~13.5 s through PDAL). `auto` prefers it as soon as
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≥ 20% of points are classified as ground (threshold lowered from 30%
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to 20%).
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- **This gap-filling note is superseded**: gap filling in the DTM is no
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longer a distance-based `fillnodata` pass over small holes. It is now
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a morphological closing bounded to the point envelope
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(`_fill_small_gaps` in `dtm.py`): the closing radius follows the local
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point spacing (1.5 × the spacing measured over 5 m, staged at
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1/1.5/2/3 m), nothing is extended beyond measured pixels, and islands
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under 1 m² are removed. Large holes (dense forest, steep relief where
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ground is under-classified) still remain as nodata (dark grey in the
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oriented relief, hatched in the PDF export).
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Deliberately no floor at the lowest return: under dense canopy that
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return is vegetation, which would print trees into the DTM.
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- Maximum quality in hard cases (steep + dense), ~1-2 min/tile + GPU +
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training accepted → **rasterized U-Net (C)**. (not implemented)
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- Best geometric filter available in PDAL → **SMRF (A)** (best coverage
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90.4% but 5.4 min/tile). Selectable via `--ground-classification smrf`.
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- Literature's absolute "winner" (fast + robust) → **PTD/PTIN
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(A)**: to be integrated (not in the current PDAL stack).
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## References
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- Axelsson (2000), PTIN/PTD, ISPRS Congress.
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- Pingel, Clarke & McBride (2013), SMRF, ISPRS J. P&RS 77:21-30.
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- Zhang et al. (2016), CSF, Remote Sensing 8(6):501.
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- Wack & Wimmer (2002), lowest-return DTM, ISPRS Archives XXXIV/3A.
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- Moudrý et al. (2020), CSF/PTIN/PMF/SMRF comparison, Measurement 150:107047.
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- Cai et al. (2019), CS+PTD, Remote Sensing 11(9):1037.
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- Qin et al. (2021), OpenGF, CVPR Workshops (arXiv:2101.09641).
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- Qin et al. (2023), dataset + evaluation + survey, ISPRS J. P&RS 202:246-261.
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- Lê et al. (2022), DeepTerRa/ALS2DTM, IEEE JSTARS 15:2778 (arXiv:2206.03778).
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- Bittner et al. (2023), DSM2DTM, ISPRS Annals X-1/W1-2023:925.
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- lidR book (PTD/CSF/PMF comparison): https://r-lidar.github.io/lidRbook/gnd.html
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