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I generate these type of charts [1] focused on the daylight hours so it was a surprise to see a concave shape instead of a convex one. Awesome way to validate t

I generate these type of charts [1] focused on the daylight hours so it was a surprise to see a concave shape instead of a convex one. Awesome way to validate these computer generated charts with captured physical data. [1] https://shademap.app/@52.39941,4.88468,11.49849z,1736006487237t,0b,0p,2m!1735977063143!1736005314203 https://shademap.app/@52.39941,4.88468,11.49849z,17360064872...

brookst 2y ago
Pretty! But daylight savings time messes with the aesthetic.
- tppiotrowski 2y ago
  True. Argentina doesn't have DST and I just realized living in the northern hemisphere made me miss the fact that the sunlight hours chart is concave in the south https://shademap.app/@-50.35203,-70.98027,4.13034z,1736008239965t,0b,0p,2m!1735980137585!1736038673017 https://shademap.app/@-50.35203,-70.98027,4.13034z,173600823...
  - brookst 2y ago
    Yep. Could flip either one by starting July 1.
nozzlegear 2y ago
This is really neat. I'm curious where the data for the tree shadows comes from though. I was surprised to see that the trees in my yard and my neighbor's yard were all mapped by your service, since I live in a small town in the middle of nowhere. I read the "how it works" FAQ section, which explained that building shadows come from the map services, but it didn't mention trees.
- isoprophlex 2y ago
  I built a similar shadow mapping tool for some commercial party that wanted to accurately estimate solar panel production in The Netherlands... In my specific case I could access very accurate LIDAR heightmaps gathered from planes. This means you can ray-march the location of the sun throughout the year over the entire country to calculate exactly where and when a surface is occluded by shadows from nearby (or even faraway, sometimes) objects. The LIDAR data can be as detailed as a shadow cast by antennas, a chimney or a tree... Which is more important than you'd think, because a little bit of shadow on a single panel means that all panels daisy-chained to that panel will see an efficiency drop! (So you either don't chain them but give each panel its own inverter, or you wreck your neighbors chimney)
- tppiotrowski 2y ago
  You can train ML to recognize vegetation in satellite photos and further train it to estimate its height +/-3 meters https://github.com/facebookresearch/HighResCanopyHeight https://github.com/facebookresearch/HighResCanopyHeight

brookst 2y ago

Pretty! But daylight savings time messes with the aesthetic.

tppiotrowski 2y ago
True. Argentina doesn't have DST and I just realized living in the northern hemisphere made me miss the fact that the sunlight hours chart is concave in the south https://shademap.app/@-50.35203,-70.98027,4.13034z,1736008239965t,0b,0p,2m!1735980137585!1736038673017 https://shademap.app/@-50.35203,-70.98027,4.13034z,173600823...
- brookst 2y ago
  Yep. Could flip either one by starting July 1.

nozzlegear 2y ago

This is really neat. I'm curious where the data for the tree shadows comes from though. I was surprised to see that the trees in my yard and my neighbor's yard were all mapped by your service, since I live in a small town in the middle of nowhere. I read the "how it works" FAQ section, which explained that building shadows come from the map services, but it didn't mention trees.

isoprophlex 2y ago
I built a similar shadow mapping tool for some commercial party that wanted to accurately estimate solar panel production in The Netherlands... In my specific case I could access very accurate LIDAR heightmaps gathered from planes. This means you can ray-march the location of the sun throughout the year over the entire country to calculate exactly where and when a surface is occluded by shadows from nearby (or even faraway, sometimes) objects. The LIDAR data can be as detailed as a shadow cast by antennas, a chimney or a tree... Which is more important than you'd think, because a little bit of shadow on a single panel means that all panels daisy-chained to that panel will see an efficiency drop! (So you either don't chain them but give each panel its own inverter, or you wreck your neighbors chimney)
tppiotrowski 2y ago
You can train ML to recognize vegetation in satellite photos and further train it to estimate its height +/-3 meters https://github.com/facebookresearch/HighResCanopyHeight https://github.com/facebookresearch/HighResCanopyHeight