Connect AI ideas with a clear Earth-observation question. Explore how to prepare inputs, define useful outputs, compare approaches, and build a review process that keeps predictions connected to the observations supporting them.
Begin with a specific task, such as a defined land-cover classification or a candidate change for human review. NASA’s Prithvi overview describes a geospatial foundation model prepared for downstream Earth-science applications. Use such examples to understand the possibilities, then decide what evidence your own task needs before choosing its model or interface.
Specify what a result represents and who will use it. A class label, candidate region, and reconstructed image deserve different explanations. Write a short definition that a reviewer can apply consistently to a new example.
Preserve source identifiers, observation dates, band meanings, and preparation choices. Inspect the input presented to the model. Keep uncertain or missing areas visible in the workflow so a prediction does not quietly inherit unexplained assumptions.
Google Earth Engine’s classification guide includes independent validation. Reserve examples that test the intended use, inspect mistakes individually, and define what counts as useful before comparing model options or choosing a display threshold.
Choose one study area and one result type. Create several examples of a useful answer and record how a reviewer would distinguish an incorrect or ambiguous result.
Evaluate a simple approach alongside any more complex candidate. Use the same examples and explain the tradeoffs in review effort, maintenance, and the kinds of mistakes each makes.
Place source observations, predictions, and a concise method explanation together. Use the API explorer for interface concepts and the full article to plan the separate analytical workflow.
Avoid displaying an arbitrary score as a guaranteed probability. Read the model’s output documentation and explain the value in that context. Keep observed inputs, generated estimates, and reviewer conclusions visually distinguishable so the evidence remains inspectable.
Maps become useful when their source, scale and meaning travel with the image.
A basemap, a satellite image and a model output can share a screen while answering very different questions.
Carry the observation time, coordinate reference and source through every layer of your project.
Map photography from the supplied theme, via Unsplash.
Compare available approaches on the task you actually want to support. Choose using evaluated results, preparation effort, and operating requirements. Model size alone does not answer whether an output is useful.
This page provides a learning path and related browser examples. A working analysis deployment needs its own selected data, model, evaluation, and computation. The full guide explains how to plan those components.
Build a grounded AI imagery workflow by choosing a precise task, preparing suitable observations, evaluating independent examples, and presenting predictions with evidence and context.
Read the guideA practical guide to discovering satellite imagery, reading STAC records, checking coordinate systems, and carrying source context through analysis, visualization, and export.
Read the guideA practical starting point for satellite APIs: understand the data families, ask better provider questions, and turn a promising sample response into a useful application.
Read the guide
