Earthsight

(00:00) Chris Struggles to Define Climate Risk Scores, Arbitrary Scores (13:30) Incentive Structures: Information Asymmetry and Product Quality, Comparison with Other Scoring Industries (23:50) Practical Examples: Zillow Real Estate Scores (29:00) Uncertainty and Verification (35:00) Climate Opinion Scores (41:00) Geocoding (50:00) Parametric Insurance Carbon Plan Open Climate Risk: https://carbon...

(00:00) Sick New Jingle (00:20) Double IPA (01:20) The Gang Struggles to Define a Platform: Metadata, Storage, Compute, APIs (11:20) The Value of a Platform (16:00) What If, What If, What If We Replaced Geospatial Platforms with Claude (24:00) Custom Use Case Development vs Platforms (27:00) Money Money Money, Chris' time as a traveling Google Cloud Salesman, Analytics Companies, Skytruth'...

(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

No pre-prepared discussion subject this time, we just winged it. (00:00) Imagine useful geospatial models, it's easy if you try. (08:00) Academic papers, life at the paper mill, distilled .pub for geospatial. (24:30) Weather data vs earth observation data consumption. Differences in value and monetization, difficulty in forecasting products. (49:30) Opus 4.5, Claude code, AI tools and the deat...

(00:00) Differences between medium-res and high-res worlds: data sizes, off-nadir imaging, co-registration issues (28:42) High-res SAR/Umbra data detour: building damage estimation in Jamaica, matching imagery up to vector datasets (34:33) Embeddings mentioned (37:10) Differences in seasonality, illumination and histogram matching: foundation models mentioned, discussions around Krishna's mode...

(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...

Chris and Krishna talk about validation as a practical part of developing geospatial analytics, focussing on their personal experiences mapping oil palm and land cover at scale. A central theme in throughout this conversation is the use of auxiliary geospatial products, science and human intuition to validate maps. (00:00) Introduction, what is validation? (02:30) Chris talks about mapping oil pal...

In this episode Chris and Krishna talk a little about their career paths, reply to some questions from listeners about Episode 1 concerning embeddings, and profess their undying love for Google Earth Engine. Throughout this conversation, a common theme explored is the nature of the abstractions implemented in Google Earth Engine, vs the open-source ecosystem. If anyone from Google sees this: we wo...

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. (19:2...