Hire an AI geospatial machine learning planner

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A clear target with the accuracy you need
• A check of your imagery, and whether a ready-made dataset or model could do the job
• A model plan with tile size, training split and model type, and why
• A test plan with results per class and a test on places the model has not seen
• A deployment plan from image tiles to map layers
• A list of ways the model may fail, and the checks left for you

How it does the job well

• Never rely on one accuracy number. Plan results per class, a confusion matrix and where the errors fall on the map.
• Plan a test on geography the model has not seen. A model trained on one region is not assumed to work in another.
• Plan a visual spot-check against ground truth. Automatic metrics can mislead.
• Name failure modes: cloud, shadow, unusual roof colours, season, sensor change.
• Tile size, overlap, learning settings and model sizes you suggest are starting points to test. Say so. Do not state accuracy, speed or cost figures you have not been given.
• Agree the question and the decision it supports before analysing.
• Check the data first: date range, grain, missing values, duplicates and definitions.

Mistakes it avoids

• One headline accuracy number.
• Training and testing on the same area.
• Treating a notebook result as a production result.
• Choosing a model before the target and the data are clear.
• Reporting a change without checking whether tracking or definitions changed.
• Treating correlation as cause.

What a good result looks like

An example, not your task: You want building outlines, roads or vehicles pulled from satellite or drone images.

• Target and assumptions. What to find, the accuracy needed, what the map is for and what the plan assumes.
• Imagery and data check. Whether your images and labels are enough, and any ready-made data or model worth trying first.
• Model plan. The model type, tile settings, data split and training steps, each with a reason.
• Test plan. How to score the model per class, on new places and by eye, with the pass line.
• Deployment plan. The steps from image tiles to clean map layers, and how to watch for drift.
• Failure modes. Cloud, shadow, season and other conditions that break the model, with a test for each.

How the work is checked

• Numbers reconcile to the source totals, and the reconciliation is shown.
• Every chart and number states its source, period and definition.
• Limits and next checks are listed.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

A second opinion

Before it hands back, your AI reviews the draft the way Margaret Hamilton would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• Define the target. It states what to find, the accuracy you need, and what the result is for.
• Check the imagery and labels. It checks pixel size, bands, coverage and dates. It looks for public labelled data or a pre-trained model before you start labelling.
• Pick the model and the data steps. It picks a model type for the job, such as segmentation or detection. It plans tiling, extra training variety and the train, validation and test split.
• Plan the tests. It plans results per class, a confusion matrix, a test on new places and a visual spot-check. It lists the conditions that break models, such as cloud and shadow.
• Plan the move to production. It plans export, tiling and merging, clean-up of small slivers, conversion to map layers and a way to watch for drift.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id gis-geoai-ml-engineer only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/gis-geoai-ml-engineer/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• What you want to find, such as buildings, roads, vehicles, water or crop types
• Your imagery, with its source, pixel size, bands, dates and the area it covers
• Any labelled examples you already have, and how many (optional)
• The accuracy you need and what the result will be used for
• The computer you will train on, such as one GPU or none (optional)

Helpful context (optional)

• A small sample of labelled tiles, so the plan can judge label quality.
• A map of where the model must work, so the new-place test fits your real area.

Tools and access

• Claude, ChatGPT or Gemini with file upload: It can read your imagery notes and label counts and point out gaps in them.

Functions and deliverables

• Geospatial machine learning planner: Plan a model that finds buildings, roads or land cover in satellite and aerial images, with data checks, a test plan and deployment steps.
  • A clear target with the accuracy you need
  • A check of your imagery, and whether a ready-made dataset or model could do the job
  • A model plan with tile size, training split and model type, and why
  • A test plan with results per class and a test on places the model has not seen
  • A deployment plan from image tiles to map layers
  • A list of ways the model may fail, and the checks left for you
  Source: Michael Sitarzewski and Agency Agents contributors.
• Language model context extension planner: Share your model, target text length and GPU limits. Get a method, Python examples and training steps.
  • The method that fits your model and why
  • The config values to switch it on
  • Code for loading the model with the new setting
  • A fine-tune plan, if your method needs one
  • Links to the papers behind each method
  Source: Orchestra Research.
• Machine learning research paper writer: Share research code, results and the target AI conference. Get a paper draft with source checks and clear notes on gaps.
  • A full paper draft in the conference style
  • Citations fetched from search, not memory. It cannot confirm them without web search, so check every one yourself
  • A list of unverified references
  • Feedback on the weak sections
  Source: Orchestra Research.
• Machine learning pipeline builder: Share your data, model code and acceptance rules. Build a versioned pipeline with checks, promotion gates and recovery steps.
  • A stage-by-stage pipeline graph
  • Pipeline code in your existing tools
  • Input and output schemas for each stage
  • Pass or fail rules for release
  • Retry, backfill and rollback steps
  Source: Burhan Sebin.
• Metric definition writer: Share a metric, calculation and business use. Get a YAML definition, validation results and questions for the data owner.
  • A filled metric, dimension or entity YAML
  • A validation report
  • dbt-ready field names
  • Notes on edge cases
  Source: Nimrod Fisher.
• Margaret Hamilton: Review a system failure plan using Margaret Hamilton’s recorded approach. Check handoffs, priorities, warnings and what happens when people make mistakes.
  • Your problem worked on the way Margaret Hamilton would: their questions, their method, their push-back
  • The answer or the work, in Margaret Hamilton's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step

Tasks you can use it for

• You want building outlines, roads or vehicles pulled from satellite or drone images.
• You need a land cover or crop map from Sentinel-2 or Landsat images and want to know if you have enough data.
• You want to compare images from two dates to find change, such as new buildings or lost trees.
• A model works in your notebook and you need the steps to make it work on a whole city.

Limits to check before starting

• You need a buffer, overlay or simple map analysis. Use the GIS analyst.
• You need a study of spatial patterns or statistics. Use the Spatial data scientist.
• You need drone photos turned into maps and 3D models. Use the Drone mapping specialist.
• Your files need cleaning and converting before any model. Use the Spatial Data Engineer agent first.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the pack and add it to your AI as a skill. If you cannot, paste SKILL.md and then references/source-agent.md into a new chat.
• Describe what you want to find and attach notes on your imagery. A sample image helps.
• Type "Plan the model and tell me what data I am missing." Your AI asks once for anything missing.
• Read the data check first. If labelled examples are too few, fix that before anything else.
• Run the tests it lists on your own computer or cloud account. It cannot train or score a model for you.
• Have a GIS specialist review the plan before a decision that affects people or money relies on the map.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/gis-geoai-ml-engineer for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.