cr458

Earthsight

Science EN ↓ 9 episodes

Chris and Krishna share big thoughts about the geospatial industry. Chris is a data scientist currently working in weather, but has previously worked at Los Alamos National Lab, The Earth Genome, Gro Intelligence and Demeter Labs. At those places he used earth observation data to solve problems such as crop mapping, yield prediction, change detection etc... Krishna is a data journalist who uses satellite imagery for news coverage. Previously he worked at Descartes Labs, Impact Observatory, The Earth Genome, Ceres Imaging and more. Krishna is an adjunct professor at The Cooper Union.

Author

cr458

Episodes

Trust, but Verify: Climate Risk Scores 14.06.2026 55:42

(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...

Live from New York: Are Geospatial Platforms Dead? 03.05.2026 43:14

(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'...

Time-series, Landtrendr, Bayesian Deforestation Detection and Anomalous Change Detection 10.03.2026 1:08:30

(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

Spatial Intelligence, The Ideal Geospatial Journal, Weather Data and Opus 4.5 29.01.2026 1:08:57

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...

Krishna talks high-resolution imagery, Chris asks questions 06.12.2025 51:41

(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...

Geospatial Failures, Problem Selection, Being an Analyst/Scientist is Hard 08.11.2025 59:10

(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...

Case Studies in Validating Maps: Oil Palm and Land Cover 24.10.2025 1:05:39

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...

Geospatial Platforms, Career Paths, Questions from Ep 1. 26.09.2025 1:03:43

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...

Embeddings in Context: Tools for Geospatial Problem Solving 09.09.2025 59:44

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...