Here you will find links to Odyssey Geospatial's blog posts, open source software, and R&D projects.
Positional encoding is a key technology enabling Geospatial AI and ML. The idea is that one can create vector encodings for shapes -- Points, LineStrings, and Polygons -- in a way that captures their key geometric properties. Those vectors then serve as input to ML and AI models, making the whole range of modern data processing technology applicable for vector-format geospatial data. This is a series of blog posts focusing on a method called Multi-Point Proximity encoding.
This post introduces the idea of Multi-Point Proximity encodings, and shows that they capture key geometric properties of the objects that they encode. Link
This post goes into more detail on the properties of MPP encodings: they are continuous, they exhibit desirable types of invariance, and they capture information that can be used for decoding. Link
This post shows that one can estimate pairwise spatial relationsbhips from MPP encodings: containment, overlap, and so on. Link
An example of applying geometric encodings in a realistic example use case: estimation of earthwquake rates based on the local distribution of geological features. This builds a neural nmetwork whose inputs are entirely derived from vector-mode geiospatial data. Link
These are posts that present interesting ideas, novel techniques, and potentially useful insights for general geospatial and ML applications.
When predicting compass directions using a ML model, a possible problem is the discontinuity of values at the point where direction angles "wrap around". This post describes a simple technique that eliminates any potential problems in this case. Link
When trying to estimate the rate of occurrence of rare events, we can put our models on a solid theoretical foundation by framing the analysis as maximum likelihood estimation. This post talks about the issues and how to address them. Link
These are installable python packages useful for various Geospatial / AI / ML tasks
On the subject of encodings and embeddings of geospatial objects
Presents the method of Multi-Point Proximity encoding, and shows that it is capapbel of capturing important geometric properties. Link
Research notebooks used in R&D on geospatial encodings
This a github repo with a series of notebooks illustrating how geospatial encodings can capture pairwise spatial relationships.
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