There must be some cool application for this but I can't think of what. I guess computing shadows and things like that but we often already have 3d buildings (though maybe not for rural areas like this does).
OpenStreetMap often has building outlines, but not building height. This would be a nice way to augment that data for visualisations (remember: OSM doesn't take auto-generated bot updates, so don't submit that to the primary source).
Similar to the flood analysis others have mentioned, this can be used to create databases of buildings with the number of stories for each, which is important for understanding how each building will respond to various catastrophes (earthquakes, strong winds, etc.) in addition to various non-catastrophe administrative tasks. The other post about finding the depth of oil in oil tanks is actually super interesting to me because the amount of oil in the tank is a huge determinant of how it will respond to seismic ground motions. I had no idea the top sinks with the oil level and am skeptical that it does on all of the tanks but it's cool nonetheless.
Measuring the depth of floods. There’s a commercial product being sold to insurance companies doing this right now for quick and dirty impact assessments.
Interesting, surprised they are using optical data for this instead of synthetic aperture radar. SAR (and in particular interferometric SAR, although that requires short repeat cycle) shines in this area, and a lot of the data is free.
ESA provides worldwide 20m x 5m radar imagery from Sentinel-1 free online. Revisit in the mid-latitudes is generally a few times per week, with an exact repeat cycle every 12 days. Once Sentinel-1C is fully operational, it'll be half that.
Trying to find emergency landing spots for planes from any position and speed? I'm not sure if planes' computers already (continuously) provide this to pilots: "here are the top 5 landing spots in this and that contingency"
Might be good info to plan safer routes ahead of time too
> I'm not sure if planes' computers already (continuously) provide this to pilots: "here are the top 5 landing spots in this and that contingency"
No they don't. For airliners it doesn't really matter. The only place they can set down safely is an airport. Which are already listed in their systems and flight plan (alternates)
For the smaller stuff it depends on the pilot, a common electronic flight system like the Garmin G1000 doesn't have sensors to actually make that determination.
Yeah but the determination of safety is pretty difficult to do and it's extremely rare for this to happen safely. Take for example the Gimli Glider. That was an actual airport though defunct and from a distance it looked fine but in the end it turned out there was a race going on. It was only luck that people managed to get out of the way in time.
Could an automated system make a better determination than a skilled pilot? And is the scenario frequent enough to warrant the big cost of cameras etc (keeping in mind they must be stabilized and with huge aperture to function at night). I doubt it.
The "miracle on the Hudson" was not called a miracle for nothing. Usually it ends like a few months ago at Washington Reagan.
And a freeway is never a safe place to land an airliner of course. The traffic makes it so. Even if there's very little, there's lampposts, barriers etc. If an airline pilot ever steers towards one they're really going for the least terrible option. Small planes fare better of course but again they won't have such tech for decades.
Measuring tree "depth" (ie canopy height) is a critical tool for conservation biology to monitor the world's forests. We already do this using remotely sensed data correlated against ground truth, which relies on specific optical reflectance characteristics associated with plant biology. But this technique is more general and works only on the spatial structure of the image itself, meaning this could potentially lead to more ubiquitous forest monitoring.
Urban heat island analysis. The physical volumes of buildings is an essential input parameter into calculating the estimated impact of the built environment and possible interventions (e.g. greening, reducing traffic) against local temperature rises. It is notoriously difficult to obtain that data at fine spatial resolution. This would be a game changer. True to a lesser degree for air pollution modelling as well, building volume is a significant input for land use regression models.
In a few recent bridge collapses and such I've seen they've used past satellite data to see how there were signs months or years in advanced.
Was also some similar evidence regarding three gorges dam, and how it's not doing so great. Ie estimated height of surrounding area over time to indicate problematic movement, or something like that.
