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

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 (24:00) Custom Use Case Development vs Platforms (27:00) Money Money Money, Chris' time as a traveling Google Cloud Salesman, Analytics Companies, Skytruth's Sentinel-1 Pipeline, Honestly This Conversation Kind of Meanders a Bit. (34:00) Ask Not What Platforms Can Do For You

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) Anomalous Change Detection

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 observation data consumption. Differences in value and monetization, difficulty in forecasting products. (49:30) Opus 4.5, Claude code, AI tools and the death of meaning and effort at work.

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) Embeddings mentioned (37:10) Differences in seasonality, illumination and histogram matching: foundation models mentioned, discussions around Krishna's model performance over Gaza

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.