Embeddings in Context: Tools for Geospatial Problem Solving

Chris and Krishna discuss their experiences working with geospatial embeddings, search and remote sensing in general, and how they think through problems.

Timestamps sustainably sourced and hand crafted.

(0:00) Intro

(2:30) Background, earth observation and existing algorithms and the embedding fallacy.

(9:49) Validation, how to solve problems, seductive embeddings, the museum of cool demos.

(19:22) What are embeddings?

(21:00) Search vs embeddings, the development of Earth Index.

(25:00) Expert embeddings: the dumbest embeddings that work.

(30:25) Computer vision embeddings/ImageNet. Experiments in pre-training, how to spend $40,000 in cloud credits.

(37:22) Should we pre-train on satellite imagery? DINO v3

(44:30) What properties should embeddings have for search? Recall vs Precision/Landcover+

(50:00) Real world consequences of mapping

(54:00) Future outlook, Krishna is jaded