You have Two Ears and One Mouth, so Listening is More Important
Full disclosure, some of this is AI generated. Some of these items are outside my area and experience, so in order to make this complete and useful, I used ChatGPT to help me round out the lists to 20 each, and help me add some color and details to make up for my lack of knowledge.
I feed it some of the questions from Fintech folks to better inform the results, and if you want the prompts DM me and I’ll send them to you.
When Fintech Says... Geospatial Hears...
“Real-time data” → Best available revisit rate, which is daily at best for most locations, not milliseconds. May also mean “delivered shortly after satellite pass” rather than “as events occur.”
“High accuracy required” → Depends entirely on spatial resolution, sensor calibration, atmospheric conditions, and validation methodology. 95% accuracy on satellite imagery is often exceptional; 99.9% is rarely achievable.
“We need this to scale” → Can theoretically process global coverage, but computational costs scale with area analyzed. “Scaling” means bigger cloud bills and longer processing times, not just adding servers.
“Can we A/B test this?” → Requires controlled experiments with satellite data, which means waiting for multiple revisit cycles and dealing with confounding factors like weather and seasonality. Timeline: months, not days.
“What’s the API rate limit?” → Satellite data delivery is constrained by acquisition schedules and processing capacity, not API throttling. You can’t just “call the API more frequently” to get fresher data.
“Show me the dashboard” → Expecting interactive web map interfaces with multiple spatial data layers, zoom capabilities, and temporal sliders. Not your standard BI dashboard with charts and tables.
“We need 99.9% uptime” → Satellite operations face orbital mechanics, weather, sensor health, and geopolitical factors. SLAs exist but must account for force majeure events that don’t affect typical cloud services.
“Can we backtest this signal?” → Requires historical satellite archive, which may cost money to access and process. Backtesting spans geography and time, creating exponentially larger datasets than financial time series.
“Add it to our ML pipeline” → Satellite data requires completely different preprocessing (radiometric correction, georeferencing, cloud masking) than structured financial data. It’s a parallel pipeline, not an additional feature column.
“What’s the false positive rate?” → In geospatial analysis, this depends on classification thresholds, ground truth availability, and spatial autocorrelation effects. It’s not a single number but varies by location and conditions.
“We need explainable AI” → Satellite-derived insights often come from complex spectral analysis and change detection algorithms. Explanations involve physics and remote sensing principles, not simple feature importance.
“What’s the latency?” → Encompasses satellite revisit time, downlink delay, processing time, and delivery. End-to-end latency is measured in hours or days, not milliseconds. Each step has physical constraints.
“Can this trigger automated decisions?” → Satellite signals are probabilistic indicators that usually require human judgment and contextual knowledge. Rarely clean enough for fully automated decisioning without risk.
“How do we version this data?” → Satellite imagery doesn’t version like code or databases. Instead, you have different processing levels, sensor calibrations, and atmospheric correction versions that affect comparability.
“What’s our data retention policy?” → Satellite imagery archives are valuable long-term assets. Geospatial professionals expect to keep data indefinitely for time series analysis, not delete it after 90 days.
“Can we cache this?” → Yes, but satellite imagery files are gigabytes each. Caching strategies differ entirely from caching API responses. Storage costs dominate bandwidth costs.
“Let’s run a quick experiment” → Quick in geospatial means weeks to acquire sufficient imagery, process it, and extract meaningful signals. The physics of satellite orbits prevents rapid iteration.
“We need audit trails” → Geospatial processing involves dozens of transformation steps with parameters that affect outputs. Audit trails must capture processing lineage, not just who accessed what when.
“What’s the cost per transaction?” → Satellite data costs are per area analyzed, per time period, per resolution level. There’s no “per transaction” pricing model. Pricing is spatial and temporal, not event-based.
“Can we personalize this?” → Satellite data provides insights about locations, not individuals. “Personalization” means customizing geographic areas of interest, not tailoring to user behavior patterns.
When Geospatial Says... Fintech Hears...
“Analysis-ready data” → Processed and calibrated enough to use in ML models, equivalent to cleaned and normalized data in your data warehouse. Not raw sensor readings, but not fully interpreted insights either.
“We have good coverage” → Geographic area we can image regularly with acceptable revisit frequency. Think of it like “market coverage” - we serve these regions with this service level.
“Resolution constraints apply” → Minimum feature size we can detect acts as a hard threshold for use cases. Like minimum transaction sizes - below this threshold, we can’t reliably operate.
“Subject to cloud cover” → Data availability varies by weather, meaning gaps in your time series. Imagine if your payment processor randomly went offline 30% of days in some regions.
“Processing pipeline required” → ETL process but for massive raster datasets requiring specialized geospatial libraries, not standard data engineering tools. Different infrastructure stack needed.
“Ground truth validation” → Like A/B testing but requires expensive field verification. You’re physically sending people to locations to verify what satellites detected. High cost, limited samples.
“Temporal analysis needed” → Analyzing change over time requires multiple satellite passes with consistent processing. Like cohort analysis but spatially distributed and weather-dependent.
“Spectral signature indicates” → Material or condition identified by its electromagnetic reflection pattern. Similar to how you identify fraud patterns, but in physics rather than behavior.
“Atmospheric correction applied” → Data quality normalization that’s essential for comparing images from different dates. Like currency normalization or inflation adjustment for financial time series.
“Different projections used” → Geographic coordinate systems that affect measurements and calculations. Choose wrong, and areas and distances are wrong. Like mixing currencies without conversion.
“Quality flags present” → Metadata indicating reliability issues like cloud contamination or sensor problems. Similar to transaction status codes, but affecting entire scenes or regions.
“Derived product available” → Feature-engineered indicator calculated from raw spectral bands, analogous to deriving “velocity” or “lifetime value” from transaction data.
“Change detection performed” → Algorithm identifying differences between time periods, similar to anomaly detection but spatial. Requires careful baseline establishment and threshold tuning.
“Spatial resolution is...” → Pixel size that determines what can be detected, directly limiting use cases. Like API precision - if you need to detect something smaller than pixel size, it’s technically impossible.
“Scene acquisition planned” → Satellite tasking scheduled for future date, similar to scheduling a batch job but dependent on orbital mechanics and weather. Can’t guarantee success.
“Archive data available” → Historical imagery exists going back years or decades, providing training data and baselines. Like historical transaction logs but massive in size.
“Preprocessing required” → Raw satellite data needs geometric correction, calibration, and cleanup before analysis. Like raw logs needing parsing and normalization before they’re usable.
“Mosaic created” → Multiple satellite images stitched together to cover larger area seamlessly. Think of it as consolidating data from multiple shards into unified view.
“Radiometric calibration ensures” → Sensor measurements are standardized to physical units allowing comparison across sensors and time. Like ensuring all monetary values are in same currency and inflation-adjusted.
“Spatial index required” → Database optimization for geographic queries, equivalent to indexing transaction tables by timestamp or user ID. Without it, spatial queries are impossibly slow at scale.