RoboSat: feature extraction from aerial and satellite imagery (opens in new tab)

Would you be open to chatting over email? I'm working on a noncommercial software project with a lot of geospatial data and I think you (and more generally folks working at Mapbox) could provide useful technical insight. My email is in my profile if you're up for it :)

Congratulation to you and the team. I have just one question in the world of tensorflowjs how do I run this project on a browser I was hoping to use this project to introduce high students to data science ?

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:

https://github.com/mapbox/robosat/blob/1e687552fe9b254a14d55...

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.

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.

Sentinel 2 data could be interesting for you. With its high refresh rate it can be used for change detection. The multi-band data is especially useful for adding more input channels to the model e.g. for water detection based on NDWI: https://apps.sentinel-hub.com/eo-browser/#lat=52.51747&lng=1...

I can also recommend http://openaerialmap.org for playing around with openly available drone imagery. I did this last week as a side-project on my evenings for drone imagery provided by http://www.zanzibarmapping.com. Here is a step-by-step guide for giving it a try on the Tanzania drone imagery: https://www.openstreetmap.org/user/daniel-j-h/diary/44321

Sure, the NOAA has extensive and freely available satellite imagery data: https://www.ncdc.noaa.gov/data-access/satellite-data/satelli...

NASA also releases satellite imagery for the public.

Planet.com