- monatlichErscheinungsrhythmus
- 1 h 00typische Länge
- 14. Juniletzte Folge
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.
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Trust, but Verify: Climate Risk Scores
(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
Live from New York: Are Geospatial Platforms Dead?
(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:
Time-series, Landtrendr, Bayesian Deforestation Detection and Anomalous Change Detection
(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) Anomalou
Spatial Intelligence, The Ideal Geospatial Journal, Weather Data and Opus 4.5
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 o
Krishna talks high-resolution imagery, Chris asks questions
(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)
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 t
Case Studies in Validating Maps: Oil Palm and Land Cover
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
Geospatial Platforms, Career Paths, Questions from Ep 1.
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
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, eart
