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We have done little work on spatial oracles, instead focusing to date on the data storage and spatial contracts layers of the protocol stack. Our early thinking suggests that making a full suite of spatial analytics algorithms (raster and vector) available at the oracle layer would be useful for on-demand processing of geospatial data.
For example, one concept protocol we have designed is a parametric insurance system. With this, we trustlessly insure physical assets in space - initially conceived of as static areas or volumes like land parcels or administrative jurisdictions (maritime, terrestrial, airspace etc). Upon purchasing a policy, agents would register their land parcel in an Astral verifiable spatial data registry, possibly represented using a GeoDID identifying a polygon or polyhedron. Additional information like the policy duration, indemnity process and, crucially, insured parameter and data source, would be specified upon policy creation. See this relatively simple example deployed by traditional insurers.
Asset monitoring could be configured in a few ways. In the example above, periodic checks to the parameterized data feed could be made, and a payout could be triggered automatically if the parameter threshold is exceeded. Alternatively, the insurance contract could be reactive, requiring a policy holder to submit a claim transaction. In this event, the contract would trigger the oracle to fetch both the land parcel information and the relevant parameterized external information. To enable a scalable, fully decentralized system, we suspect the most efficient architecture will require an oracle or some Layer 2 consensus network to apply a spatial analysis algorithm to these inputs to determine if the claim is valid. (This differs to many existing DeFi insurance protocols - these often rely on some entity - a trusted individual or DAO committee - to assess the evidence off chain and submit an attestation to settle a claim or trigger automatic indemnity - see IBISA and certain review strategies employed by Protekt's Claims Manager.
This functionality was also required to detect the amount of time devices spent in policy zones in Hyperaware, and to supply NOx levels to the sustainability-linked bond dApp we prototyped during the KERNEL Genesis Block.
What is unique about this compared to other oracle systems is that our focus is narrowly on spatial data, that is, information that contains some spatial, or location, dimension. We could argue that all data is spatial data, but here specifically we are looking at data representing physical space - geospatial data, and data positioned within other spatial reference systems.
Needless to say, much research into these oracle capabilities - including privacy-preserving techniques - for bringing spatial insights on chain in an efficient way is warranted, as it seems this is an unavoidable layer of the Astral stack.