Problem Overview: One of the greatest challenges with OpenDroneMap (ODM) is getting great results out of sparse data. I used to describe this as getting good data out of mediocre inputs, but this isn’t a fair descriptor, and here I’ll make a public apology: just because I have the time to fly with lots of overlap most of the time doesn’t mean it should be … Continue reading Upcoming improvements to OpenDroneMap — Better everything.
I was on my way to Rwanda to do some more work with Karisoke Research Institute on Gorillas, help close out some research worked on by University of Rwanda botany student Tuyizere Jean de Deiu on how land use and land cover have changed between 1995 and present. More on that project in a later post. In the meantime, while passing through Europe, I took … Continue reading Apis helvetica var. Jacques
I finally got PDAL properly compiled with Point Cloud Library (PCL) baked in. Word to the wise — CLANG is what the makers are using to compile. The PDAL crew were kind enough to revert the commit which broke GCC support, but why swim upstream? If you are compiling PDAL yourself, use CLANG. (Side note, […]
Continue reading Scaling OpenDroneMap, necessary (and fun!) next steps
I finally got PDAL properly compiled with Point Cloud Library (PCL) baked in. Word to the wise — CLANG is what the makers are using to compile. The PDAL crew were kind enough to revert the commit which broke GCC support, but why swim upstream? If you are compiling PDAL yourself, use CLANG. (Side note, […]
Continue reading Taking Slices from ~~LiDAR~~ OpenDroneMap data: Part X
This is a post about OpenDroneMap, an opensource project I am a maintainer for. ODM is a toolchain for post-processing drone imagery to create 3D and mapping products. It’s currently in beta and under pretty heavy development. If you’re interested in contributing to the project head over here. The Problem So for most of the […] via Viewing Sparse Point Clouds from OpenDroneMap — GeoKota Continue reading Viewing Sparse Point Clouds from OpenDroneMap — GeoKota
I failed to make it to the Paris Code Sprint. It just wasn’t in the cards. But, my colleague Dakota and I sprinted anyway, with some help and feedback from the OpenDroneMap community. So, what did we do? Dakota did most of the work. He hacked away at the cmake branch of ODM, a branch set up by Edgar Riba to substantially improve the installation … Continue reading OpenDroneMap — Paris Code Sprint
Two posts precede this one, ODM — the future that awaits, and ODM — the future that awaits (part 이) Ben Discoe has a good point on the first post, specifically: As I see it, the biggest gap is not in smoother uploading or cloud processing in the cloud. The biggest gap is Ground Control Points. Until there’s a way to capture those accurately at a … Continue reading OpenDroneMap — the future that awaits (part 삼)
In my previous post, ODM — the future that awaits, I start to chart out OpenDroneMap beyond the toolchain. Here’s a bit more, in outline form. More narrative and breakdown to come. (this is the gist) Objectives: Take OpenDroneMap from simple toolchain to an online processing tool + open aerial dataset. This would be distinct from and complementary to OpenAerialMap: Explicitly engage and provide a … Continue reading OpenDroneMap — the future that awaits (part 이)
OpenDroneMap could piece together from disparate parts all the pixels for the world:
At a high resolution (spatial and temporal)
For everywhere we can fly
One drone, balloon, and kite flight at a time
And all to be pushed into common and public dataset, built on open source software commonly shared and developed. Continue reading OpenDroneMap — the future that awaits
I have been reading a bit about drone remote sensing of agriculture fields. On one hand, it’s amazing, world changing technology. On the other hand, some part of all of it is bunk. What do I mean? Well, applying techniques created for continent size analyses may not scale down well. Why? Well for one, all those clever techniques (like Normalized Difference Vegetation Index, as well as its non-normalized siblings) rely heavily on two things: 1– being on average right over a large area; 2 — painting with such a broad brush as to be difficult to confirm or refute. Continue reading Reflections on Goldilocks, Structure from Motion, near scale remote sensing, and the special problems therein
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