Introduction

This course covers agentic coding workflows specializing in geospatial data science. You will get hands-on experience setting up your agent and understand how to use Skills and MCP servers to augment domain-specific capabilities. We cover best practices for using coding agents to rapidly solve complex problems - grounded in established science and human-in-the-loop validation. We cover hands-on examples using Claude Code - including crime hotspot mapping, route optimization, and object detection from aerial imagery. You will also learn how to use agents to manage a personal knowledge base and create a website that automatically updates with new information.

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Installation and Preparation

Sign-up for GitHub

Visit GitHub.com and create a free account. If you already have an account, skip this step.

Install Git and GitHub CLI

Follow our Git and GitHub CLI Installation Guide to install and configure Git and GitHub CLI.

Install Conda and Setup an Environment

We will use conda to install the required Python packages and manage local development environments.

Follow our step-by-step Conda Installation Guide to install Miniconda for your operating system. Once you have a working conda installation, follow the steps below.

  1. (Windows users), search for Anaconda Powershell Prompt and launch it. (Mac/Linux users): Launch a Terminal window. Run the following commands to create a fresh environment and activate it.
conda create --name claude_code_workshop -y
conda activate claude_code_workshop
  1. Now your environment is ready. We will install the required packages from conda-forge.
conda install -c conda-forge pandas geopandas matplotlib jupyterlab rioxarray gdal rio-cogeo

Your local development environment is now ready.

Install Visual Studio Code

We recommend Visual Studio Code (VS Code) editor for this workshop.

Follow our step-by-step Visual Studio Code Installation Guide to install and configure VS Code on your system. Make sure to complete the steps to set VS Code as your default editor.

Install Claude Code

Follow our step-by-step Claude Code Installation Guide to install and configure Claude Code on your system.

Install Obsidian

Obsidian is a free app for creating and managing notes. We will be using Obsidian for setting up your personal knowledge base using Claude. Visit Obsidian Download page and install Obsidian for your platform.

Sign-up for OpenRouteService API

OpenRouteService (ORS) provides a free API for routing, distance matrix, geocoding, route optimization etc. using OpenStreetMap data. To obtain an API key, visit the HeiGIT Sign Up page and create an account. Once your account is activated, you can visit the Dashboard and copy the long string displayed under Basic Key.

Get the Data Package

The code examples and reference materials used in this workshop are supplied to you in the agentic_coding_geospatial.zip file. Unzip this file to a directory - preferably to the <home folder>/Desktop/agentic_coding_geospatial/ folder.

Download agentic_coding_geospatial.zip.

Note: Certification and Support are only available for participants in our paid instructor-led classes.

1. Setting up Your Agent

A good practice is to always setup a CLAUDE.md file for your project. This file contains instructions and rules to be followed when working inside the project directory. If you already have a directory with some code or have cloned a Git repository, Claude Code can automatically create the CLAUDE.md file using the /init command. Here we are starting with a blank project, so we will create a new CLAUDE.md file with instructions.

  1. On your computer, launch Visual Studio Code. Click Open Folder… and select the <home folder>/Desktop/agentic_coding_geospatial/ where you have unzipped the data package.

  1. We will create a new file at the root of this folder. Select New File….

  1. Enter the name of the file exactly as CLAUDE.md. Note that the filename is case sensitive and needs to have the .md extension.

  1. Paste the following content and save the file.
# CLAUDE.md

## Geospatial Stack Preferences

- Prefer Python-based approaches
- Use existing packages instead of building solutions from scratch
- My preference for geospatial packages are below
  - Pandas for tabular data
  - GeoPandas for vector geospatial data
  - XArray ecosystem (rioxarray, xarray-spatial etc) for raster geospatial data
  - Scikit-learn for Machine Learning
  - GeoAI (geoai-py) for Deep Learning
- Web Apps
  - Streamlit for data driven apps
  - Leaflet for interactive mapping apps
  - Self contained HTML for small apps

## Other Preferences

- Write code that is simple to understand and explain
- Always install python packages in a conda environment. Never install anything in the base environment. Ask the user for confirmation on their preferred conda environment before installing anything.
- Do not use emojis

  1. Now we will start Claude Code. (Windows users), search for Anaconda Powershell Prompt and launch it. (Mac/Linux users): Launch a Terminal window. Use the cd command to change the current working directory to the project directory.

Windows

cd Desktop\agentic_coding_geospatial

Mac/Linux

cd Desktop/agentic_coding_geospatial

  1. Once you are inside the project folder, launch Claude Code by entering the following command.
claude

  1. The first time you start Claude Code inside a directory, there will be a security notification. You can choose Yes, I trust this folder. At the prompt, you can invoke several commands for configuration. You can type /help to see all available commands. Let’s run a few commands. First enable Plan Mode by entering the /plan command. Next choose the model using the /model command. The default model choice is good for now, and you can change the model at any time.

Now we are ready to use Claude Code for our first data analysis task.

2. Geospatial Data Analysis (Mapping Crime Hotspots)

We will now use Claude Code to do some data exploration, cleaning and analysis. This will give you a feel of how agents work and how to guide them. We will take the Police.uk data on street-level crime for the City of London Police and identify crime hotspots.

2.1 Exploratory Data Analysis (EDA)

  1. Enter the following prompt into Claude Code and press Enter. Make sure the Plan mode is on. This will ensure Claude Code will first make an implementation plan and wait for your approval before diving into coding.
I would like to do an exploratory data analysis of crime data in the folder london_crime_2024. 

* Give me a summary of the rows, columns and any missing data. 
* Flag any data quality issues. 
* Create charts showing different types of crime and monthly patterns.

  1. As the project-level CLAUSE.md states, all the work needs to be done in a conda virtual environment. Claude will prompt you to choose an environment. Choose the claude_code_workshop environment created for this class and press Enter.

  1. For the Output format, choose Python script + saved PNGs.

  1. Review the answers and select Submit answers. Press Enter.

Depending on your operating system, Claude Code version, and selected model, you may see a slightly different set of questions.

  1. Now Claude Code will go through the data and come up with a plan for building the required script for exploratory data analysis. You can review the plan and edit it as per your requirements. Use the Ctrl+G shortcut to open an editor with the plan and make any necessary edits. Once you are satisfied, confirm the execution by selecting Yes, and use auto mode.

  1. Claude Code will write the Python scripts and run it using the installed packages in the conda environment.

  1. Once done, it will prints the summary of its findings.

  1. The analysis will also produce some charts in the output/ subfolder.

2.2 Data Cleaning and Spatial Analysis

Now that we have a good understanding of the data structure, we can ask Claude to help us with analyzing the data. In this section, we will go through the workflow of identifying hotspots off thefts. Starting with data cleaning, we will iteratively improve our analysis and learn how we can use existing references (research papers, manuals etc.) to ground the behavior of the model.

  1. Back in Claude Code, switch to the plan mode. You can use Shift+Tab till you see plan mode on. Enter the following prompt. Claude Code will ask you clarifying questions. Select All 7 theft-family categories for Theft scope.
I want to analyze thefts data. Suggest which categories should be merged.

  1. For the merge goal, select Align to official ONS groupings.

  1. Select Submit answers and press Enter.

  1. Review the plan and make any adjustments if required. Select Yes, and use auto mode to proceed.

  1. Review the summary and enter the following prompt to implement them.
Filter to data to only for the 7 theft-family categories and 
write a single merged CSV with those records.
Remove missing data and unnecessary fields.

  1. Claude will produce a merge script and the output CSV file.

  1. This is the cleaned version of our original dataset. We can use this for mapping hotspots. Switch to the plan mode (Shift+Tab) and enter the following prompt.
I want to map the theft hotspots. 
Suggest what would be the best technique for the analysis.

  1. Answer the clarifying questions and select the Kernel Density Estimation (KDE) as the technique. Select Submit answers and press Enter.

  1. Once the analysis is planned, enter the following prompt to build the script.
Create a static visualiztion using matplotlib using gaussian_kde.

  1. Claude Code will produce a script and a map with hotspot visualization.

  1. While this is a good starting point, we can improve it by providing well-known and high quality reference material to guide the implementation. We have provided an extract of a research report Mapping Crime: Understanding Hot Spots in your data package that contains useful tips and best practices for crime mapping. We can improve our analysis by using this reference. Enter the plan mode (Shift+Tab) and write the following prompt.
I have some reference material for crime mapping in documents/hotspots.pdf. 
Incorporate suggestions from that and give me an updated plan.

  1. Select the option to use the Quartic kernel and deny option to extend the analysis beyond the scope of the original request.

  1. Once the plan is ready, review it. We can be explicit about the desired output, so you can select Tell Claude what to change.

  1. Ask for the following change.
Save the updated script and chart with the _v2 suffix.

  1. Claude Code will build the required script and produce the new hotspot visualization.

  1. The new map is much more interpretable and based in scientifically validated technique. Let’s extract the hotspots. Enter the following prompt.
Use the criteria "Greater than 5x mean" to extract hotspots and convert then to polygons.
Save the results as a GeoJSON london_theft_hotspots.geojson.

  1. The raster hotspots will be vectorized using the given threshold and saved as a GeoJSON file.

  1. We can visualize and explore the results in GeoLibre. Open https://web.geolibre.app/ in a browser. Drag and drop the GeoJSON file and verify the results.

2.3 Monitoring Usage

It’s a good practice to keep an eye on your token usage and costs. You can use the following command to know the utilization in the current session and how much quota is left in your plan.

/usage

3 Using Web APIs (Route Optimization)

In this section, we will learn how to use Claude Code to build Jupyter notebooks for data analysis and interactively guide the agent to reach the desired solution and validate the results. We will be implementing a real-world scenario of optimizing delivery routes based on a route optimization algorithm.

In your data package, you will find the following files in the data/route_optimization/ folder.

We will use Claude Code to build a Jupyter Notebook that builds an end-to-end workflow that loads the data, finds optimal delivery routes and writes PDF manifests with delivery schedule for each store.

3.1 Using Claude Code in VS Code

Let’s install the Claude Code for VS Code extension.

  1. Click the Extensions button on the left-hand panel. Search for Claude and select the Claude Code for VS Code by Anthropic extension.

  1. Click Install.

  1. Once installed, it can be invoked from the Toggle Chat button and selecting the Claude Code tab.

3.2 Building Notebooks

Now that we have Claude Code running inside VS Code, we can prompt it to build a Jupyter notebook for us.

  1. Ensure the workshop folder agentic_coding_geospatial is open in VS Code. Enter the following prompt.
I want to build a notebook to find optimal delivery routes. 
Create a new notebook scripts/route_optimization.ipynb
Read the .geojson files in data/route_optimization/ folder and
display the data on an interactive map.

  1. This is a simple and straightforward task. Our project level CLAUDE.md already has preferences listed for how to setup the environment and what packages to use. Claude Code will start creating the notebook and will pause periodically for confirmation. You can choose Yes to proceed.

  1. Once the task finishes, a new notebook route_optimization.ipynb will be created in the scripts/ folder as instructed.

  1. Locate the notebook in the Explorer tab and double-click to open it. The notebook will have the datasets visualized on an interactive map.

3.3 Interactive Data Analysis

Now, let’s build the core of our data analysis to find the optimal delivery routes. This is a complex problem that requires a lot more context and direction. We will use the Plan mode and guide Claude Code towards building the code that solves the problem.

  1. Make sure the notebook route_optimization.ipynb is open. Open Claude Code from the Toggle Chat button.

  1. Switch to the Plan mode.

  1. Enter the following prompt.
I want to use OpenRouteService API. 
Give me options on different approaches to plan for the most optimal delivery routes from each store. 

Here are the constrants:

- A single trip will consist of 10 deliveries at maximum.
- The delivery will originate at a grocery store and end at the same delivery store.
- All stores are capable of fulfilling the order but can do a maximum of 5 delivery trips each day.

I want to know the most optimal route to minimize the distance traveled along with a schedule of the trips.

  1. Claude Code will trigger the AskUserQuestion tool to collect more information. For the Vehicle Routing Problem (VRS) approach, select the ORS Optimization API (VROOM).

  1. We need to enter our API key. Select Other and enter the API key in the response below, replacing the <your-api-key> with your actual API key. If you do have your key handy, you can login to your HeiGIT Account and copy the long string displayed under Basic Key.
Yes, I have a key. Save the API key in a separate cell in the notebook.

ORS_API_KEY='<your-api-key>'

  1. For the Conda env, select the claude_code_workshop conda environment that we have setup. You can then click Submit answers.

  1. As we are asking for delivery schedules, you may be prompted to choose store operating hours. We keep things simple and choose Trip order per store listing.

  1. After obtaining the necessary information, Claude Code will kick-off an agent to do the planning. Approve any permissions it may need.

  1. The OpenRouteService API has limits on how many Origin-Destination pairs it can solve. If you get prompted to choose for an approach, choose Two-stage: assign + batch, then ORS Optimization per trip approach.

  1. Once the plan it ready, it will be opened in VS Code. Review the plan and suggest any changes by adding comments. You can select any text and enter comments on what you would like changed. For example, in the initial plan that Claude Code came up with - it wanted to use the openrouteservice Python package. We can instead ask it to use the API directly instead of a client library by adding a comment like below.
Instead, use the REST API directly.

  1. After adding the comment, choose Send feedback and keep planning.

  1. Once the plan is modified and you are satisfied, choose Yes, and auto-accept.

  1. Claude Code will now start implementing the plan.

  1. Once the notebook is built, it will also test it to ensure it runs without any errors. Once everything is done, it will print a summary.

  1. Open the notebook and review the results. You will see the map with the planned routes for all the delivery locations.

3.4 Validation and Export

When building with AI - you need to spend most of your time validating the results and guiding the model. Let’s learn some techniques for validation.

  1. When reviewing the initial output of the delivery route, you will notice that some of the routes are very long and does not look optimal. We can ask Claude Code to fix this using the following prompt.
I see that some stores are not assigned any deliveries even if they are quite close to an address. 
The assignment tries to assign 10 deliveries in each route - 
making some of them quite long while nearby stores end up without any deliveries.
Update the plan to address this problem.

  1. Claude Code will go through the notebook and come up with a fix. Give your approval to apply the fix.

  1. Your prompt will also force Claude to do a more thorough validation of the results.

  1. The updated routes look much better, but we still need to do a closer inspection. We can export the results in any vector data format, so we can use a GIS to view and verify it better. Enter the below prompt.
Save the routes as a single GeoJSON file in the outputs/route_optimization/
folder so I can validate the results.

  1. A new file routes.geojson will be created. Locate it in the VS Code Explorer. Right-click and select Reveal in File Explorer to open the folder on your computer.

  1. We can visualize and explore the results in GeoLibre. Open https://web.geolibre.app/ in a browser. Drag and drop routes.geojson, along with the source data Delivery_Locations.geojson and Grocery_Store_Locations.geojson and verify the results.

  1. Once we are satisfied with the output, we can generate PDF manifests with the planned routes. These files are the output from our analysis that can be handed over to the operations team for implementation. Enter the following prompt. If prompted for a PDF library, choose Install reportlab and click Submit answers.
Generate PDF manifests in the outputs/route_optimization/ folder containing
delivery order and addresses for each trip.
1 manifest per store.

  1. The code will be added to the notebook and PDFs will be generated.

  1. The PDFs now contain delivery routes for each store.

Assignment

Now you are ready to put your agentic coding skills to test by solving a spatial analysis problem from scratch.

Problem Statement

You are a data scientist for a real-estate company who wants to enhance their property listings by assigning a livability score to each of their properties to help buyers pick properties that suit their lifestyle.

Your task is to calculate such a score for all the Apartment Buildings in your city. Consider the factors that are important to home buyers. You can design the score using metrics such as:

These are some examples and you can pick the criteria that make sense to you based on the knowledge of your city. You can use OpenStreetMap to obtain the data for apartment building polygons and the required amenities.

Required output

Tips

4. Using Skills

Skills are a set of instructions that teach an AI agent how to perform specific, multi-step workflows. Skills in Claude Code are defined by a SKILL.md containing natural-language instructions for the agent along with specific examples and patterns. You can also publish your skill on GitHub and make it available for others to download and use with their own agent.

4.1 Installing a Skill

We will install the Humanizer skill that improves the text generated by AI models and makes them sound more natural.

  1. Start Claude Code in the Terminal. (Windows users), search for Anaconda Powershell Prompt and launch it. (Mac/Linux users): Launch a Terminal window. Use the cd command to change the current working directory to the project directory. Once you are inside the project folder, launch Claude Code by entering the following command.
claude

  1. There are multiple ways to install skills. The npx skills command provides an easy to to install skills for a wide range of coding agents. However this requires installation of Node.js on your system. For Claude Code, you can install Skills as plugins. Run the following command to add the GitHub repository.
/plugin marketplace add blader/humanizer

  1. Next run the following command to install the humanizer skills as a plugin.
/plugin install humanizer@humanizer

  1. When prompted, select Install for you (user scope).

  1. The plugin will be installed and activated.

4.2 Using a Skill

Once installed, Skills are triggered automatically when your prompt matches the task handled by the skill. We will use the Humanizer skills to review and rewrite a paragraph.

  1. Enter the following prompt into Claude Code. Claude Code will use the selected model and generate a text summary.
Give me a 100-word summary of this article
https://cloudnativegeo.org/blog/2026/02/the-technical-debt-of-earth-embedding-products/

  1. We will now invoke the humanizer skill by asking Claude Code to review and update the text. Enter the following prompt and press Enter.
Review the summary and remove signs of AI writing

  1. The humanizer skill will be used to rewrite the text by removing common AI-writing patterns.

4.3 Creating a Skill

The real power of Skills lies in automating custom workflows. We will learn how to create a new skill for your custom workflow and use it. We will create a new skill named create-cog for converting raster data into a Cloud Optimized GeoTIFF (COG) format.

  1. Enter the following prompt. Create a new skill from the following text: and paste the content below.
---
name: create-cog
description: Convert a raster to a Cloud-Optimized GeoTIFF (COG)
---

Use this skill when you need to convert any geospatial raster data to a
Cloud-Optimized GeoTIFF.

## Workflow

1. Activate the conda environment that has GDAL. Never use the base environment.
2. Check the GDAL version, since the conversion command differs by version.
3. Check if the input is already a valid COG. If it is, stop and report
   that. Do not re-convert.
4. Convert, using the compression settings and naming convention below.
5. Validate the output and report the result.

## Required Tools

* `gdal` Conda package. Install using `conda install -c conda-forge gdal`
* `rio-cogeo` Python package for validation. Install using `pip install
  rio-cogeo`

Both must come from a conda environment, not base. Check what is available
before installing:

    conda env list
    conda activate <env>
    gdal --version

If neither is installed, ask the user which environment to install into
before proceeding.

## Validating COGs

    rio cogeo validate <file>

Always validate the input before converting and the output after converting.

## Converting Raster to a COG

Check `gdal --version` first, then use the matching command.

GDAL version >= 3.11 (the `gdal` subcommand interface)

    gdal raster convert -f COG <input file> <output file>

GDAL version < 3.11 (the legacy utility)

    gdal_translate -of COG <input file> <output file>

Note the flag difference. The new `gdal raster convert` takes `-f` (or
`--format`). It does NOT accept `-of`, which is the legacy `gdal_translate`
flag, and fails with "Option 'o' is not a boolean option."

Creation options are passed with `--co KEY=VALUE` on the new interface,
`-co KEY=VALUE` on the legacy one.

If the output file already exists, the command errors out. Add
`--overwrite` only when you intend to replace it.

## Compression

The COG driver already applies LZW compression by default, so the output is
compressed even if you pass nothing. But the default uses no predictor,
which wastes a lot of space on continuous data. Set the compression and
predictor explicitly. Use DEFLATE as the default, since it is readable by
every GeoTIFF client.

    gdal raster convert -f COG --co COMPRESS=DEFLATE --co PREDICTOR=YES <input> <output>

Only exception - If the input data is JPEG compressed - typically used in
aerial/drone imagery), retain the same with COMPRESS=JPEG

## File Naming Convention

Append the text `_cog` to the converted filename. If the input is
`data.tif`, the output should be named `data_cog.tif`

## Remote Files

If the given file is a remote file, do not download it. Use GDAL Virtual
File Systems by prefixing the URL with `/vsicurl/`

    rio cogeo validate /vsicurl/https://example.com/data.tif
    gdal raster convert -f COG /vsicurl/https://example.com/data.tif data_cog.tif

A remote input gives no obvious output location. Write to the current
working directory unless a location is specified by the user.

In Windows Powershell, set the following environment variable `MSYS_NO_PATHCONV=1` 
to avoid errors when using paths starting with `/vsicurl`

  1. If prompted, install the skill as a project-level skill. This will be installed in the .claude directory in the current folder.

  1. Run the following command to reload the skills so the new skill is available.
/reload-skills

  1. Let’s test the skill. You have a regular GeoTIFF file in your data package. Let’s ask Claude Code to convert it. Enter the prompt below.
Convert the file data/chirps/chirps-v3.0.2025.tif to a Cloud Optimized GeoTIFF

  1. The skill will be invoked as a new file will be generated as per the instructions in the skill.

  1. Let’s test the skill with a remote file. Enter the following prompt.
Convert the file https://data.chc.ucsb.edu/products/CHIRPS/v3.0/annual/global/tifs/chirps-v3.0.2024.tif to a COG

  1. As specified in the skill, Claude Code will use the /vsicurl virtual file system to access the file instead of downloading it.

  1. Skills can also be invoked using commands. Let’s test the skill to ensure it follows the validation instructions. Enter the following prompt.
/create-cog Convert this file https://storage.googleapis.com/spatialthoughts-public-data/ntl/viirs/viirs_ntl_2021_global.tif

  1. As our input is already a COG, the skill will correctly stop and not convert the file.

4.4 Useful Skills

  • Skills Leaderboard: Discover, install and manage skills using the skills CLI.
  • Geospatial Skills for Agents: A catalog of installable geospatial tools and capabilities designed for coding agents, covering raster/vector processing, satellite data, and interactive visualization.

5. Building a Personal Knowledge Website

5.1 Understanding the AI Second Brain

Coming soon

5.2 Setting up your personal knowledge base

Coming soon

5.3 Creating and publishing your website

Coming soon

6. Using GeoAI (Object Detection)

6.1 Configuring an MCP Server

MCP (Model Context Protocol) is an open-source standard for connecting agents to external systems. MCP servers are similar to APIs - but are designed specifically for use by AI Agents. MCP servers allow your agents to access specialized data sources and applications. We will setup and configure a connection to the Google Colab MCP server which will allow Claude Code to create, modify and run Google Colab notebooks.

  1. Start Claude Code in the Terminal. (Windows users), search for Anaconda Powershell Prompt and launch it. (Mac/Linux users): Launch a Terminal window. Use the cd command to change the current working directory to the project directory.
claude

  1. Enter the prompt below to setup the MCP server. In the world of agentic coding, you can point your agent to install instructions like these and it will configure itself instead of you doing this yourself.
Setup an MCP server according to the instructions at
  https://github.com/googlecolab/colab-mcp

  1. You can select the workshop Conda environment when prompted to select an environment for installing dependencies.

  1. Claude Code will install the dependency and update the local configuration file .claude.json with the required information. Restart Claude Code for the changes to take effect. You can exit the session using Ctrl + C and start it again by typing claude.

  1. Once the session is restarted, enter the following command.
/mcp

  1. If everything went fine, you should see the colab-mcp server connected.

6.2 Building Notebooks in the Cloud

Now that we have configured the MCP server, we can prompt Claude Code to create cloud-hosted notebooks using Google Colab.

  1. Enter the following prompt.
Create a new colab notebook

  1. Claude Code will using the colab-mcp server to open a connection. Approve the execution requests.

  1. A new tab will open in Google Chrome and you will be prompted to connect to the Colab MCP server. Click Connect. Ensure that you are signed-in to your Google account.

  1. If this round-trip command was successful, a new notebook will be created. If you get errors, see the steps outlined in Debugging Colab MCP Connection.

  1. Next, let’s add some code and execute it. Enter the following prompt.
Add a cell to print "hello world" and run it

  1. The MCP server will add the cell and run the notebook in a cloud-hosted machine.

Debugging Colab MCP Connection

Colab MCP server can be a bit flaky and you may see errors when establishing the connection. Here are a few things you can try.

  1. Type /mcp and check whether the colab-mcp server is connected. If it is disconnected, select it and press Enter to reconnect.

  1. If you have a Google Colab tab open but the Claude Code is not able to connect to it, you can go to Tools → Command palette.

  1. Search for mcp and select Connect to a local Colab MCP server to connect that notebook to the MCP server.

6.3 Using Deep Learning Models

  1. We will now build a notebook to detect building footprints from an aerial image. Enter the following prompt.
Create a new Colab Notebook named "building_detection".

This notebook will use a pre-trained model for building footprint extraction from GeoAI and run it on the provided image. 

Structure the notebook following this example https://opengeoai.org/examples/building_footprints_africa/

The provided image can be large. So use the GeoAI package to split the input image into tiles 

Reproject the tiles to a suitable local UTM crs.

The model is trained on images around 50cm-1m resolution images. Check the resolution of the provided image and if it is higher resolution (i.e. 5cm, 10cm) - resample the tiles to 50cm resolution before running the inference. 

Save the results as a GeoJSON file in EPSG:4326 CRS.

Use this image from OpenAerialMap to test the notebook https://oin-hotosm-temp.s3.us-east-1.amazonaws.com/69493c8084a859b011c94266/0/69493c8084a859b011c94267.tif

  1. Claude Code will now build the workflow and populate the cells in the Colab notebook. Once done, we need to change the runtime to use a GPU.

  1. Open the notebook and click on the dropdown for Additional connection options and select Change runtime type.

  1. Select T4 GPU and click Save.

  1. Back in Claude Code, enter the following prompt.
Run the notebook

  1. The inference pipeline will be executed.

  1. View the notebook and you will get the detected building footprints as a polygon layer. Let’s download and view the results. Click on the Files tab from the left-hand panel in Colab.

  1. Browse to the directory where the output is saved and locate the building_footprint.geojson file.

  1. Click on the : button next to it and select Download.

  1. We can open the source imagery and the detections in GeoLibre Web for detailed QA. Visit https://web.geolibre.app/ and select Add Data → Raster Layer.

  1. Paste the URL of the image that we used for the notebook and click Load
https://oin-hotosm-temp.s3.us-east-1.amazonaws.com/69493c8084a859b011c94266/0/69493c8084a859b011c94267.tif

  1. Next, go to Add Data → Vector Layer. Click on the Drop file or click to browse panel. Browse to the downloaded GeoJSON file and select it and click Load.

  1. Check how well the model performed in detecting and extracting the buildings.

  1. Save the completed notebook by going to File → Save a copy in Drive.

If you want to catch up to this step and use a sample notebook generated in this step, click the button below.

Open In Colab

Exercise

You will notice that since the footprints were vectorized from a raster layer, they have jagged edges. We can make the output polygons much better by Regularizing the polygons. GeoAI has several algorithms for Building Regularization. Prompt Claude Code to add a step at the end to regularize the polygons and save them.

Data Credits

License

This course material is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to re-use and adapt the material but are required to give appropriate credit to the original author as below:

Agentic Coding for Geospatial course by Ujaval Gandhi www.spatialthoughts.com

© 2026 Spatial Thoughts www.spatialthoughts.com

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