I recently published a practical guide to PDAL pipelines in Python for point cloud workflows.

It covers:

- PDAL pipeline structure
- running pipelines from Python
- returning results as NumPy arrays
- clipping and merging point clouds
- creating a GeoTIFF DEM from a LAZ file

The focus was on practical examples for real processing tasks.

https://medium.com/@anton.biatov/pdal-in-python-a-practical-guide-to-point-cloud-pipelines-1f7e2bc3e2ea

Also interested in what tools people use for point cloud work in production or research.

#PDAL #LiDAR #Python #GIS #geospatial

I wrote a detailed post on LinkedIn about the technical data analysis pipeline I use at my solar energy startup. And I took ChatGPT's advice about a provocative headline at the beginning of the post. Now I think that was a mistake. Because, judging by the statistics, my readers are mostly engineers, not marketers.

#SolarStartup #solarenergy #Geospatial #TechStartup #RemoteSensing #EarthObservation #DataScience #ClimateTech #BuildInPublic

Last 2 weeks building my solo B2B solar startup in Portugal:
- Built a list of 80 potential clients
- Sent 30+ cold emails
- Got 1 reply (pricing request)
- Posted 4 times on LinkedIn
- Started connecting in Linkedin
- Spent hours learning outreach
- Started building an interactive 3D map with demo data
Feeling pretty exhausted and noticing some self-sabotage creeping in.

If you’ve built a B2B startup solo — how did you get your first customers? What worked for you?

#buildinpublic #solopreneur

I've worked in ecology and nature conservation for over 20 years.

And I've been thinking a lot about how much the energy transition still relies on assumptions.

Recently, high-quality open LiDAR datasets have become available across Europe.

Solar potential is often perceived as a promise.

But it's physics. Geometry. Shadows. Radiation intensity.

It can be measured.

So I started building a tool for modeling solar potential using opendata.

#renewableenergy #BuildInPublic #geospatial #EUtech

We’ve upgraded the Sun Potential 3D building solar report with new high-resolution maps for every analyzed property.

What’s new:
- LiDAR-based pseudo-orthophoto generated from point cloud data
- Digital Surface Model
- Annual POA heatmap for the building surroundings
- POA over Effective Roof Area map

Updated demo reports are available in English and Portuguese for all pricing plans on our website https://sun-potential-3d.com

#solarenergy #renewables #EUtech #BuildInPublic #geospatial

I’m building a small solar analysis project focused on Portugal 🇵🇹

Many rooftop PV estimates rely on simplified geometry. But open government LiDAR (25 cm resolution) allows precise modelling of roof shape and shading.

I combine:
• public LiDAR data
• digital surface models
• 8,760 hourly shading simulations
• PVGIS weather data

Goal: replace assumptions with measurable rooftop data.

#SolarEnergy #GIS #OpenData #RenewableEnergy #Portugal #BuildInPublic

Zonal Statistics in Python with Earth Engine and Colab (New guide)

Inside:
• Template for processing multiple rasters at once
• Unified reducer (mean/median/std/min/max in one pass)
• Convert to GeoDataFrame
• Export to GPKG/CSV + upload to Google Drive
• Visual maps (relief, NDVI, soil temperature)

🎁 Bonus: ready-to-use Colab notebook at the end.

Read more: https://medium.com/@anton.biatov/zonal-statistics-in-python-using-earth-engine-and-google-colab-7101d1d42ffd

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#Geospatial #GIS #EarthEngine #Python #Geemap #ZonalStatistics #NDVI #DataScience

Zonal statistics in Google Earth Engine in a single pass: how to combine terrain and NDVI into a single stack, calculate mean/median/standard deviation/min/max for thousands of polygons, and immediately visualize and export the results. Assembling the package + ready-made script.

Watch the full tutorial here:
https://medium.com/@anton.biatov/how-to-combine-topography-and-vegetation-a-practical-guide-to-zonal-statistics-in-google-earth-d8167615937b

#GIS #EarthEngine #RemoteSensing #NDVI #DataScience #geospatial #EarthObservation #gischat