Feature detection exploration in Lidar DEMs via differential decomp
Details
- External ID
- 46449892
- Source
- HN
- Company
- —
- Product
- Feature detection exploration in Lidar DEMs via differential decomp
- Website domain
- github.com
- Launched
- Jan. 1, 2026
- Cohort
- —
- Upvotes
- 8
- Upvotes percentile
- 0.41699604743083
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
I'm not a geospatial expert — I work in AI/ML. This started when I was exploring LiDAR data with agentic assitince and noticed that different signal decomposition methods revealed different terrain features.The core idea: if you systematically combine decomposition methods (Gaussian, bilateral, wavelet, morphological, etc.) with different upsampling techniques, each combination has characteristic "failure modes" that selectively preserve or eliminate certain features. The differences between outputs become feature-specific filters.The framework tests 25 decomposition × 19 upsampling methods across parameter ranges — about 40,000 combinations total. The visualization grid makes it easy to compare which methods work for what.Built in Cursor with Opus 4.5, NumPy, SciPy, scikit-image, PyWavelets, and OpenCV. Apache 2.0 licensed.I'd appreciate feedback from anyone who actually works with elevation data. What am I missing? What's obvious to practitioners that I wouldn't know?
Enrichment
- Theme
- scientific computing and research algorithms
- Vertical
- Horizontal
- Function
- Analytics & BI
- Audience
- Developer
- AI stance
- Not AI
- Project type
- Hobby / open-source project
- Normalized one-liner
- lidar feature detection and analysis
- Manually corrected
- False
Could you build this?
Partial While basic image filters can be generated quickly, developing a novel differential signal decomposition algorithm for geospatial LiDAR DEMs requires solid numerical and geospatial domain expertise.
What it would actually take: Requires a scientific Python pipeline using GDAL/Rasterio, SciPy, and OpenCV or specialized geospatial libraries (like WhiteboxTools). The hard part is tuning mathematical filtering combinations (Gaussian, bilateral, multi-scale wavelets) across noisy irregular point-cloud derived heightmaps to extract subtle micro-topographic archaeology or geological features without amplifying sensor artifacts.
Discussion
No comments on this launch.
Competitors
Other products that read as similar to this one — 63 launches clear the similarity bar, closest 8 shown.
Attention rank: #38 of 64 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 52 days after the earliest competitor.
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Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a analytics & bi tool for Legal yet.