Earthsight

Earthsight

Time-series, Landtrendr, Bayesian Deforestation Detection and Anomalous Change Detection

1 h 8 min · 10 de mar de 2026
portada del episodio Time-series, Landtrendr, Bayesian Deforestation Detection and Anomalous Change Detection

Descripción

(00:00) Time series, Landtrendr, New Mexico, Cuba. Is medium/low-resolution imagery underrated? (15:00) Bayesian Deforestation Detection, operation-oriented algorithms and platforms (27:00) Landtrendr in the West Bank, CCDC (35:00) Anomalous Change Detection

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Geospatial Failures, Problem Selection, Being an Analyst/Scientist is Hard

(00:00) Experimental failures. Krishna won't let Chris forget about his failed SimCLR experiments. (04:30) Geographic generalization discussion. Husky vs Malamute vs Wellpad. (11:32) Chris fails to map coconut palm because it's hard. Read the paper carefully. (15:30) Krishna maps oil slicks, but it's hard. (20:30) Cognitive debt, problem selection and failure modes in geospatial. (31:00) The sales cycle, promises, inflated expectations. Limitations in geospatial. (34:00) Problem selection in journalism. Are we running out of ideas? (37:00) Turning a failure into a success. Cover crop mapping is hard. (45:00) Turning failure into a success: predicting sugarcane yield is hard. (49:00) Is building tooling easier than solving modeling problems? (53:00) Vertical seems better than horizontal. Solutions are multi-modal.

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