Thinking a little more about moderately large compute resources and their container (see previous post), I revised my analysis to look and see if we can fit these 10 NUCs plus switch and outlets into a carry-on sized case. It turns out, at first blush, it seems feasible:
Posts Tagged ‘opendronemap’
Posted by smathermather on March 28, 2017
Posted by smathermather on March 27, 2017
This is a theoretical post. Imagine for a moment that OpenDroneMap can scale to the compute resources that you have in an elastic and sane way (we are short weeks away from the first work on this), and so, if you are a typical person in the high-speed internet world, you might be thinking, “Great! Let’s throw this up on the cloud!”
But imagine for a moment you are in a network limited environment. Do you process on a local laptop? Do you port around a desktop? The folks in the humanitarian space think about this a lot — depending on the project, one could spend weeks or months in network limited environments.
Folks at American Red Cross (ARC) have been thinking about this a lot. What has resulted, in order to aid in mapping e.g. rural areas in West Africa is Portable OpenStreetMap, or POSM, a tool for doing all the OpenStreetMap stuff, but totally and temporarily offline.
The software for this is critical, but I’ve been increasingly interested in the hardware side of things. OpenDroneMap, even with it’s upcoming processing, memory, and scaling improvements will still require more compute resources than, say OpenMapKit and Field Papers. I’ve been contemplating that once the improvements are in place, what kind of compute center could you haul in the field with you?
I’m not just thinking humanitarian and development use cases either — what can we do to make processing drone imagery in the field faster? Can we make it fast enough to get results before leaving the field? Can we modify our flight planning based on the stream of data being processed and adapt while we are there? Our real costs for flying are often finding staff and weather windows that are good, and sometimes we miss opportunities in the delay between imagery capture and processing. How can we close that loop faster?
On the hardware side of the house, the folks at ARC are using Intel NUC kits. For ODM, as I understand it, they go a step up in processing power from their specs to something with an i7. So, I got to thinking — can we put together a bunch of these, running on a generator, and not break the bank on weight (keep it under 50 lbs)? It turns out, maybe we can. For a round $10,000, you might assemble 10 of these 4-core NUCs with a network switch, stuff it into a Pelican Air 1605 case, with 320 GB RAM, and 2.5 TB of storage. More storage can be added if necessary.
Any thoughts? Anyone deployed serious compute resources to the field for drone image processing? I’d love to hear what you think.
Posted by smathermather on March 26, 2017
Just saw this great blog post by my friend Mr. Yu at Korea National Park on using OpenDroneMap. If you need it in English, google seems to translate it rather well:
Posted by smathermather on March 8, 2017
OpenDroneMap has really evolved since I first put together a concept project presented at FOSS4G Portland in 2014, and hacked with my first users (Michele M. Tobias & Alex Mandel). At this stage, we have a really nicely functioning tool that can take drone images and output high-quality geographic products. The project has 45 contributors, hundreds of users, and a really great community (special shout-out to Piero Toffanin and Dakota Benjamin without whom the project would be nowhere near as viable, active, or wonderful). Recent improvements can be roughly categorized into data quality improvements and usability improvements. Data quality improvements were aided by the inclusion better point cloud creation from OpenSfM and better texturing from mvs-texturing. Usability improvements have largely been in the development of WebODM as a great to use and easy-to-deploy front end for OpenDroneMap.
With momentum behind these two directions — improved usability and improved data output, it’s time to think a little about how we scale OpenDroneMap. It works great for individual flights (up to a few hundred images at a time), but a promise of open source projects is scalability. Regularly we get questions from the community about how they can run ODM on larger and larger datasets in a sustainable and elastic way. To answer these questions, let me outline where we are going.
When I stated that scalability is one of the promises of open source software. I mostly meant scaling up: if I need more computing resources with an open source project, I don’t have to purchase more software licenses, I just need to rent or buy more computing resources. But an important element to scalability is the per unit use of computing resources as well. If we are not efficient and thoughtful about how we use things on the small scale, then we are not maximizing our scaled up resources. Are we efficient in memory usage; is our matching algorithm as accurate as possible for the level of accuracy thus being efficient with the processor resources I have; etc.? I think of this as improving OpenDroneMap’s ability to efficiently digest data.
Incremental toolchain optimizations are thus part of this near future for OpenDroneMap (and by consequence OpenSfM, the underlying computer vision tools for OpenDroneMap), focusing on memory and processor resources. The additional benefit here is that small projects and small computing resources also benefit. For humanitarian and development contexts where compute and network resources are limiting, these incremental improvements are critical. Projects like American Red Cross’ Portable OpenStreetMap (POSM) will benefit from these improvements, as will anyone in the humanitarian and development communities that need efficient processing of drone imagery offline.
To this end, three approaches are being considered for incremental improvements. Matching speed could be improved by the use of Cascade Hashing matching or Bag of Words based method.Memory improvements could come via improved correspondence graph data structures and possibly SLAM-like pose-graph methods for global adjustment of camera positions in order to avoid global bundle adjustment.
In addition to incremental improvements, for massive datasets we need an approach to splitting up our dataset into manageable chunks. If incremental improvements help us better and more quickly process datasets, the large-scale pipeline is the teeth of this approach — we need to cut and chew up our large datasets into smaller chunks to digest.
If for a given node I can process 1000 images efficiently, but I have 80,000 images, I need a process that splits my dataset into 80 manageable chunks and processes through them sequentially or in parallel until done. Maybe I have 9000 images? Then I need it split into 9 chunks.
Eventually, I want to synthesize the outputs back into a single dataset. Ideally I split the dataset with some overlap as follows:
Problems with splitting SfM datasets
We do run into some very real problems with splitting our datasets into chunks for processing. There are a variety of issues, but the most stark is consistency issues from the resultant products. Quite often our X, Y, and Z values won’t match in the final reconstructions. This becomes critical when performing, e.g. hydrologic analyses on resultant Digital Terrain Models.
What Anna describes and solves is the problem of matching LiDAR and drone data and assumes that the problems between the datasets are sufficiently small that smoothing the transition between the datasets is adequate. Unfortunately, when we process drone imagery in chunks, we can get translation, rotation, skewing, and a range of other differences that often cannot be accounted for when we’re processing the digital terrain model at the end.
What follows is a small video of a dataset split and processed in two chunks. Notice offsets, rotations, and other issues of mismatch in the X and Y dimensions, and especially Z.
When we see these differences in the resultant digital terrain model, the problem can be quite stark:
To address these issues we require both the approach that Anna proposes that fixes for and smooths out small differences, and a deeper approach specific to matching drone imagery datasets to address the larger problems.
Deeper approach to processing our bites of drone data
To ensure we are getting the most out of stitching these pieces of data back together at the end, we require using a very similar matching approach to what we use in the matching of images to each other. Our steps will be something like as follows:
- Split our images to groups
- Run reconstruction on each group
- Align and tranform those groups to each other using matching features between the groups
- For secondary products, like Digital Terrain Models, blend the outputs using an approach similar to r.patch.smooth.
I hope you enjoyed a little update on some of the upcoming features for OpenDroneMap. In addition to the above, we’ll also be wrapping in reporting and robustness improvements. More on that soon, as that is another huge piece that will help the entire community of users.
(This post CC BY-SA 4.0 licensed)
(Shout out to Pau Gargallo Piracés of Mapillary for the technical aspects of this write up. He is not responsible for any of the mistakes, generalities, and distortions in the technical aspects. Those are all mine).
Posted by smathermather on February 23, 2017
Part 10 of N… , wait. This is a lie. This post is actually about optical drone data, not LiDAR data. This is about next phase features fro OpenDroneMap — automated and semiautomation of the point clouds, creation of DTMs and other fun such stuff.
To date, we’ve only extracted Digital Surface Models from ODM — the top surface of everything in the scene. As it is useful for hydrological modeling and other purposes to have a Digital Terrain Model estimated, we’ll be including PDAL’s Progressive Morphological Filter for the sake of DEM extraction. Here’s a small preview:
The test data above is Midpines, flown by NextGen Air Transportation Center (NGAT) access to the data through collaboration with Center for Geospatial Analytics at NCSU.
Posted by smathermather on June 30, 2016
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 […]
Posted by smathermather on February 29, 2016
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 process for ODM.
- Fixed odm_orthophoto in the branch so that it produces geotiffs
- Fixed PMVS so that it is multithreaded again
- Added rerun-all and rerun-from function
- Integrated @lupas78’s additions for an xyz point cloud output
- Added benchmarking which is an important soft number for when we have code changes
- (Technically before the sprint) wrote the first test for OpenDroneMap
- Cleaned code
- I also added PDAL to the build processs
- And I inserted PDAL into the point cloud translation process.
Currently, this means we’ve dropped support for LAZ output, as I haven’t successfully built PDAL with LAZ support, but it stages the project for LAZ support through PDAL, and allows us to tap into additional PDAL functionality in the future.
It was an intensive couple of days that would have been improved with French wine, but we were in Parma (Ohio). So, a shout out to the coders in Paris at the same time, and cheers to all.
Posted by smathermather on October 27, 2015
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 prosumer price point, we are doomed to a patchwork of images that don’t align, which is useless for most purposes, like overlaying other geodata.
Ben’s right of course. If drone data is produced, analyzed, and combined in isolation, especially while prosumer and consumer grade drones don’t have verifiable ground control, the data can’t be combined with other geodata.
The larger framework that I’m proposing here side-steps those issues in two ways:
- Combine drone data with other data from the start. Drones are a platform and a choice. Open aerial imagery, the best available, should always be used in a larger mosaic. If Landsat is the best you’ve got… Use it. If a local manned flight has better data… use it. If an existing open dataset from a photogrammetric / engineering company is available… use it. And if the drone data gets you those extra pixels… use it. But if you don’t have ground control (which you likely don’t), tie it into the larger mosaic. Use that mosaic as the consistency check.
- The above isn’t always practical. Perhaps the existing data are really old, or are too low in resolution. Maybe the campaign is so big and other data sources so poor that the above is impractical. In this case, internal consistency is key. Since OpenDroneMap now leverages OpenSfM, we have the option of doing incremental calculation of camera positions and sparse point clouds. If we have 1000 images and need to add 50, we don’t have to reprocess the first 1000.
Posted by smathermather on October 25, 2015
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 platform for drone enthusiasts to contribute imagery in return for processing services.
- Address and serve:
- Aerial imagery
- Point clouds
- Surface models
- Terrain models
- Additionally, as part of a virtuous circle, digitizing to OSM from this aerial imagery would refine the surface models and thus the final aerial imagery
- More on this later: in short digitizing OSM on this dataset would result in 3D photogrammetric breaklines which would in turn refine the quality of surface and terrain models and aerial imagery.
- Aerial basemap
- (ultimately with filter for time / season?)
- Point cloud (see e.g. http://plas.io)
- Digital surface model (similar to Open Terrain)
- Digital elevation model (in conjunction with Open Terrain)
- OpenDroneMap https://github.com/OpenDroneMap/OpenDroneMap
- Open Terrain https://github.com/openterrain/openterrain
- OpenAerialMap / OpenImageryNetwork http://openaerialmap.org
- MapKnitter http://openaerialmap.org
- Point cloud related technologies:
If ODM is to take submissions of large portions of data, data collection campaigns may come into play. Here are some back-of-the-envelope calculations for flying whole cities, albeit the medium size cities of San Francisco and Cleveland. This ignores time needed for all sort so things, including coordinating with local air traffic control. As such, this is a best case scenario set of estimates.
|Drone||Flight Height||Pixel||Overlap||Per flight||City Name||City Area||Total # of Flights||Total Flight time|
|E384||400 ft||3 cm||60%||1.5 sq mile||San Francisco||50 sq miles||33 flights||66 hours|
|E384||400 ft||5 cm||90 %||0.5 sq mile||San Francisco||50 sq miles||100 flights||200 hours|
|E384||400 ft||3 cm||60 %||0.5 sq mile||Cleveland||80 sq miles||54 flights||108 hours|
|E384||400 ft||5 cm||90 %||0.5 sq mile||Cleveland||80 sq miles||160 flights||320 hours|
|Iris+||400 ft||3 cm||60%||0.078 sq mile||San Francisco||50 sq miles||640 flights||213 hours|
|Iris+||400 ft||5 cm||90%||0.026 sq mile||San Francisco||50 sq miles||1920 flights||640 hours|
Posted by smathermather on October 24, 2015
Do you recall this 2013 post on GeoHipster?:
Later on, I confessed my secret to making accurate predictions:
In all this however, we are only touching the surface of what is possible. After all, while we have a solid start on a drone imagery processing toolchain, we still have gaps. For example, when you are done producing your imagery from ODM, how do you add it to OpenAerialMap? There’s no direct automatic work flow here; there isn’t even a guide yet.
And then once this is possible, is there a hosted instance of ODM to which I can just post my raw imagery, and the magical cloud takes care of the rest? Not yet. Not yet.
So, this is the dream. But the dream is bigger and deeper:
I remember first meeting Liz Barry of PublicLab at CrisisMappers in New York in 2014. She spoke about how targeted (artisanal?) PublicLab projects are. They aren’t trying to replace Google Maps one flight at a time, but focus on specific problems and documenting specific truths in order to empower community. She said it far more articulately and precisely, of course, with all sorts of sociological theory and terms woven into the narrative. I wish I had been recording.
Then, Liz contrasted PublicLab with OpenDroneMap. OpenDroneMap could map the world. 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.
Yes. Yes it could, Liz. Exactly what I was thinking, but trying hard to focus on the very next steps.
This future ODM vision (the “How do we map the world with ODM) relies on a lot of different communities and technologies, from PublicLab’s MapKnitter, to Humanitarian OpenStreetMap Team’s (HOT’s) OpenAerialMap / OpenImageryNetwork, to KnightFoundation / Stamen’s OpenTerrain, ++ work by Howard Butler’s team on point clouds in the browser (Greyhound, PDAL, plas.io, etc.).
Over the next while, I am going to write more about this, and the specifics of where we are now in ODM, but I wanted to let you all know, that while we fight with better point clouds, and smoother orthoimagery, the longer vision is still there. Stay tuned.