At ruumi we are building grassland and grazing management tools to give farmers actionable insights on how to manage their land sustainably and reduce their carbon emissions.
We are hiring for various roles
1. Engineers for web apps; React, TypeScript, Express, Postgres
2. Engineers for iOS and Android; Swift, SwiftUI, Jetpack, Jetpack Compose
3. Product owner for our apps and tools for farmers and carbon emissions
In the style of "Scalability! At What COST?" I've started a side-project bringing succinct data structures (rank/select [2], elias-fano coding) and modern instruction sets (popcount, pdep) to graphs.
For some reason the article doesn't mention the field of graph compression and I think it shows: for example most if not all popular open source routing engines don't do anything regarding graph compression. It's time to change that!
Reach out to me (preferably on the Github project) if you want try some ideas :)
We built TerminusDB[1], our OSS knowledge graph, using succinct data structures. As we have a immutable delta encoding approach to allow domain teams to build and curate data products that they then share, this has been a very fruitful combination. We wrote a technical white paper all about it! [2]
At ruumi we help farmers with rotational grazing: a managed grazing technique that regenerates soil, increases biodiversity, minimizes agro-chemicals, sequesters carbon, and improves profitability at the same time.
We are bringing multi-spectral and radar satellite imagery, geo-spatial tech, and machine learning to regenerative agriculture. We tell farmers actionable insights for example: when and where to move their animals to maximize soil regeneration and carbon sequestration.
We are looking for someone to work with us on our applications, with a strong focus on iOS.
At ruumi we help farmers with rotational grazing: a managed grazing technique that regenerates soil, increases biodiversity, minimizes agro-chemicals, and improves profitability at the same time.
We are bringing multi-spectral and radar satellite imagery, geo-spatial tech, and machine learning to regenerative agriculture. We tell farmers actionable insights for example: when and where to move their animals to maximize soil regeneration.
We are looking for someone to work with us on web and mobile applications. For the frontend we are using Typescript, React and Redux; for the backend we are using Typescript and Express.js.
At ruumi we help farmers with rotational grazing: a managed grazing technique that regenerates soil, increases biodiversity, minimizes agro-chemicals, and improves profitability at the same time.
We are bringing multi-spectral and radar satellite imagery, geo-spatial tech, and machine learning to regenerative agriculture. We tell farmers actionable insights for example: when and where to move their animals to maximize soil regeneration.
We are looking for someone to work with us on web and mobile applications. For the frontend we are using Typescript, React and Redux; for the backend we are using Typescript and Express.js.
ruumi | Berlin, Remote | Full-time | Frontend & Mobile Engineer | http://jobs.ruumi.io
We are looking for someone to work with us on web and mobile applications. For the frontend we are using Typescript, React and Redux; for the backend we are using Typescript and Express.js.
At ruumi we help farmers with rotational grazing: a managed grazing technique that regenerates soil, increases biodiversity, minimizes agro-chemicals, and improves profitability at the same time.
We are bringing multi-spectral and radar satellite imagery, geo-spatial tech, and machine learning to regenerative agriculture. We tell farmers actionable insights for example when and where to move their animals to maximize soil regeneration.
ruumi | Berlin, Remote | Full-time | Frontend & Mobile Engineer | http://jobs.ruumi.io
We are looking for someone to work with us on web and mobile applications. For the frontend we are using Typescript, React and Redux; for the backend we are using Typescript and Express.js.
At ruumi we help farmers with rotational grazing: a managed grazing technique that regenerates soil, increases biodiversity, minimizes agro-chemicals, and improves profitability at the same time.
We are bringing multi-spectral and radar satellite imagery, geo-spatial tech, and machine learning to regenerative agriculture. We tell farmers actionable insights for example when and where to move their animals to maximize soil regeneration.
We are building AV-enabled routing and mapping capabilities to help ride-hailers and car manufacturers around the world shape the future of autonomous mobility. Together with Daimler's autonomous and vehicle teams our goal is not simply to get our customers from A to B but to get them there with the quality and safety they have come to expect from our brand.
We are hiring for:
* Backend - Node.js, Swagger/OpenAPI, k8s; to work on our services and the underlying infrastructure
* Routing & Maps - Python, C++, Rust; to work on graphs, shortest path algorithms, machine maps / lane-level maps
* Machine Learning - PyTorch, TensorFlow; to work on feature extraction, graph embeddings, traffic models, and demand prediction
Some example projects our engineers have worked on before:
Bonus points if you have worked professionally with geospatial data and software before and/or have contributed to OpenStreetMap, be it code or map edits.
Write me a note at daniel(dot)hofmann(at)daimler(dot)com telling me about what projects you've worked on that fit what we're looking for. Include a robot emoji in the subject to confirm you are not a robot ;) You can also find us and talk to us engineers at local meetups such as Geo Berlin.
We are running it internally on our aerial imagery from the Mapbox Maps API. The zoom level even there depends on the feature you want to extract, for example z18 seems to work well for parking lots.
There is not a single feature this model is most suited for: you can add arbitrary features (e.g. tennis courts, swimming pools) in pre-processing and train your model. Then the imagery quality depends on your feature, for example it will be hard to impossible to spot swimming pools in Landsat imagery.
We haven't released pre-trained models yet. Mostly for two reasons: 1/ The PyTorch checkpoints depend on the specific Python model class. Even if you refactor only e.g. a MaxPool layer into a direct functional.max_pool function call, loading old checkpoints will no longer work. We have an ONNX model exporter now (rs export) which allows for self-contained and portable protobuf model and weight files. This workflow needs some more time and careful evaluation, though. 2/ The models for Tanzania I was working on in my spare time I can open up for sure. If there is community interest maybe we can come up with a publicly available model catalogue hosting ONNX models and metadata where folks can easily upload and download models. For our internal models and the data we extract we are thinking through a broader strategy since a lot of time and resources are going into creating and cleaning datasets, doing hard-negative mining, running multiple training iterations and so on. They're also bound to the Mapbox aerial imagery on specific zoom levels.
The model architecture is kept simple on purpose. It used to be an encoder-decoder U-Net'ish architecture which we trained from scratch. Recently (https://github.com/mapbox/robosat/pull/46) I switched out the encoder to a pre-trained ResNet, as proposed by Alexander Buslaev. It's a mix of the papers listed in the docstring at the top with a focus on simplicity and maintainability:
Internally we were also exploring a multi-class PSPNet but decided not to move forward with it right now: the RoboSat model is currently a binary model (feature vs. background) which makes a few things easier in practice, such as efficiently storing results which is needed when scaling it up e.g. to all of North America.
Personally I always try pre-trained models very very useful.
If I'm working in a new domain (which this is to me) then I prefer to get the workflow right (files in the right directories etc) before changing the NN architecture. It's a pretty big time investment to train a NN just to try it.
At ruumi we are building grassland and grazing management tools to give farmers actionable insights on how to manage their land sustainably and reduce their carbon emissions.
We are hiring for various roles
1. Engineers for web apps; React, TypeScript, Express, Postgres
2. Engineers for iOS and Android; Swift, SwiftUI, Jetpack, Jetpack Compose
3. Product owner for our apps and tools for farmers and carbon emissions
To apply see https://robofarm.jobs.personio.com or mail us at hello@ruumi.io and include your favorite animal emoji in the subject.
Learn more
- https://www.youtube.com/watch?v=fSEtiixgRJI
- https://www.imdb.com/title/tt8618654/
- https://eng.ruumi.io/post/seeing-through-clouds.html
- https://eng.ruumi.io/post/planet-scale-radar.html