Looks nice. To scale this to large revenue I would highly recommend considering the real estate surveying market. This market is in the process of being disrupted by geolocating UAV-based photogrammetry point clouds. It has lots of bullshit inertia owing to regulatory significance, including a fully enshrined network of "approved surveyor" types who provide far less data than UAVs for far more money, far more slowly. There are multiple paid-for services that can be offered here. One is real estate sales, just showing the site in-situ in a local map and maybe emphasizing sun and aspect. One is real estate concept development as point clouds can provide local high-res trees/buildings even across the boundaries for shade mapping. Good shade mapping can mean the difference between a development being approved or not, so has huge monetary benefit. Generating reports for this depends upon the regional requirements, eg. which season. Often they pick a random peak-summer or peak-winter day and demand top-down shade maps of those. Currently architects pay for surveys and get back crap. Shade mapping is specialist. Every single day tens of thousands of real estate surveys are made. There has to be opportunity to push in this direction, just a suggestion.
Great write-up. I've never worked with a surveyor (I rent) but I did some Googling to understand the process. I think I would need to convert point clouds to DEM which Shade Map natively understands.
I'm wondering if I should meet with local surveyors of if there's some big names in the industry you suggest I try to go after?
Also, sorry for my abysmal treatment of buildings on the map (pixelated shadows at high zoom). I'm working to improve this.
I have not spent much time interviewing users but there’s a few niches:
Outdoor enthusiasts. Searching for hikes in sun or shade. Sun exposure also correlates with snow stability so mountaineers and backcountry skiers. Also hang gliders and pilots who rely on thermals.
Real-estate. I think this is at the high end where people are looking for things beyond price to inform their buying decisions. Also: apartment hunters or vacation renters.
OSINT. Investigators attempting to place where and when a photo was taken.
Academia. Sun exposure correlated with vegetation or glacier recession.
Government. Placing of solar parking meters or garbage cans and other sensors. Identifying icy roads in winter. Solar parking installations.
These are just a few people have mentioned in passing. I’m mostly focused on making the shading algorithm fast and accurate but took a few days to add this paid tier because I got burned with a few Mapbox bills in the $100’s of dollars.
I downloaded a tileset file (.mbtiles extension) for the whole planet (latest I can find for free is here [1]). Tileset file is just a SQLite database with a table where each row contains the x/y/z coordinates of a tile and a binary blob containing the corresponding GeoJSON of features as a protobuf (commonly referred to as a vector tile).
You can start tileserver-gl-light[2] (light only serves vector tiles) pointing to the .mbtiles file (Ideally I think you slice the .mbtiles file into individual tiles and put them in something like S3 but it will cost $$$ and time) and it performs remarkably well given that it's serving an 60GB+ .mbtiles file from SSD, not RAM. I have an nginx proxy in front of tileserver-gl-light as I use the Linode for other projects.
I added a rule in Cloudflare to cache any tile request url for 8 days to reduce the load on the origin server. On average about 30-50% of tile requests come from Cloudflare's cache.
On the front end I switched from Mapbox GL JS to Maplibre JS [4]. I switched the Mapbox DEM data for Open Data on AWS [5].
If you need any clarifications, my contact info is in my profile. Hope this helps.
