Use Cases — AIMU Local-first GIS + AI Workbench

Model environmental variables from field measurements, spatial layers, and tabular predictors without moving sensitive datasets to cloud tools.

Problem: Research teams often split data prep, GIS handling, and model training across separate tools.
Data: Rasters, shapefiles, CSV/Excel tables, monitoring data, derived predictors.
Workflow: Import layers, prepare predictors, train classical ML or DL models, compare metrics, and save the full run.
Expected result: A repeatable model package with documented inputs, settings, and evaluation evidence.
Best for: Environmental researchers, hydrology/geoscience teams, agriculture and land-analysis projects.

Use rasters, derived indices, and tabular predictors in one local project. Compare classical ML and DL approaches without uploading data.

Problem: Imagery-derived workflows become fragile when preprocessing, training, and evaluation are scattered.
Data: GeoTIFF rasters, extracted bands/indices, vector samples, training tables.
Workflow: Prepare inputs, run model experiments, inspect metrics, and preserve each configuration for review.
Expected result: A clearer comparison between candidate models for imagery-derived predictors.
Best for: Remote-sensing researchers, labs evaluating imagery workflows, GIS analysts.

Build repeatable prediction workflows for new locations or updated datasets using saved preprocessing, model, and evaluation settings.

Problem: Teams need to rerun models on new geographic areas while keeping methods traceable.
Data: Spatial layers, training samples, new-area predictors, tabular measurements.
Workflow: Train, evaluate, save the configuration, then reuse the project structure on updated inputs.
Expected result: A prediction workflow that can be rerun and audited when data or geography changes.
Best for: Applied researchers, technical consultants, engineering teams.

Give students and lab members a consistent local GIS + AI workflow instead of a fragile chain of notebooks, GIS exports, and cloud accounts.

Problem: Teaching and supervision become harder when every student builds a different toolchain.
Data: Course datasets, thesis datasets, lab project files, local Windows workstations.
Workflow: Standardize import, preprocessing, model comparison, and saved project handoff.
Expected result: A shared workflow that is easier to teach, supervise, and reproduce across users.
Best for: Universities, research labs, supervisors, student cohorts.

Compare Random Forest, SVM, CNN, LSTM, GRU, and Time-Series Transformer experiments using saved metrics and project state.

Problem: Model choices are difficult to justify when runs and preprocessing steps are not saved together.
Data: Tabular, spatial, time-series, or signal-oriented datasets prepared for local experiments.
Workflow: Run alternative models, review metrics, tune candidates, and keep the evidence in one project.
Expected result: A defensible shortlist of models with comparable metrics and saved settings.
Best for: Researchers preparing reports, papers, internal comparisons, or technical validation.

Evaluate GIS + AI workflows on Windows machines where datasets cannot be uploaded to third-party services.

Problem: Some research, client, or institutional datasets cannot leave local infrastructure.
Data: Sensitive project datasets, field surveys, restricted GIS layers, local lab archives.
Workflow: Run preprocessing, training, evaluation, and exports locally, with online access limited to controlled-access/licensing needs.
Expected result: A local evaluation path for restricted datasets without cloud training or data upload.
Best for: Labs, universities, public-sector teams, and consultants handling restricted data.

Use cases that make AIMU concrete.