Building LiDAR-based solar screening for Portugal 🇵🇹 and beyond
Replacing assumptions with data.
sun-potential-3d.com
My personal profile: https://www.linkedin.com/in/anton-biatov/
#solarenergy #geospatial #EUtech #BuildInPublic
Open data is powerful.
But open data without practical tools remains abstract.
LiDAR point clouds, DSMs, irradiance models —
these are not just datasets.
They are ways to reduce blind decisions.
Less wasted materials.
Fewer unnecessary site visits.
More realistic expectations.
Perhaps the next step of the energy transition
is not just installing panels —
but improving how we decide where to install them.
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.
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
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.
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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
