Posts by Anita Graser

It’s been a while since my post on geo and the AI hype in 2019. Back then, I didn’t use the term “GeoAI”, even though it has certainly been around for a while (including, e.g., with dedicated SIGSPATIAL workshops since 2017).

GeoAI isn’t one single thing. It’s an umbrella term, including: “AI for Geo” (using AI methods in Geo, e.g. deep learning for object recognition in remote sensing images) and “Geo for AI” (integrating geographic concepts into AI models, e.g. by building spatially explicit models). [Zhang 2020] [Li et al. 2024]

Today’s post is a collection of key GeoAI developments I’m aware of. If I missed anything you are excited about, please let me know here in the comments or over on Mastodon.

Background

A week ago, I had the pleasure to attend a “Specialist Meeting” on GeoAI here in Vienna, meeting over 40 researchers from around the world, from Master students to professor emeritus. Huge props to Jano (Prof. Krzysztof Janowicz) and his team at Uni Wien for bringing this awesome group of people together.

The elephant in the room: LLMs

Unsurprisingly, LLMs and the claims they make about geography are a mayor issue due to the mistakes they make and the biases behind them. An infamous example is AI’s issue with understanding topology:

Even if recent versions of ChatGPT (such as GTP 4o) do a better job with this specific example, this doesn’t make their answers reliable. So between the trustworthiness, reproducibility, explainability, and sustainability issues … LLMs have a long way to go. And it’s not clear whether they are going in the right direction right now.

Geospatial foundation models

Prithvi, a model developed by NASA, IBM, et al. in 2023, is one of the first geospatial foundation models. Like much of GeoAI, Prithvi deals with remote sensing data. Specifically, it is trained on Landsat and Sentinel-2 (HLS) imagery, with applications in flood mapping and wildfire prediction. And maybe best of all: the model is open-source and publicly available.

Spatiotemporal machine learning model specifications

In the general AI community, model cards have become a common way to share information about models. However, identifying the right model for spatiotemporal tasks is hard since there are no standardized descriptions in existing model catalogs (e.g. Hugging Face, DLHub or MLFlow). To address this issue, [Charette-Migneault et al. 2024] have proposed the Machine Learning Model (MLM) extension for the SpatioTemporal Asset Catalogs (STAC). But, yet again, this development is targeting models trained with remote sensing imagery.

Similarly, the OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) and its ISO equivalent are limited to EO as well …

Spatial knowledge graphs

For those among us working mostly with vector data, the KnowWhereGraph is an interesting development. It’s the first geo-enriched knowledge graph [Janowicz et al. 2022] that helps answer geospatial questions by integrating a variety of spatial datasets through hierarchical grids, standard region boundaries and appropriate ontology and knowledge graph schema development. However, so far, the KnowWhereGraph is mostly limited to the United States.

Explainable AI (XAI) and geo

While answers from knowledge graphs are intrinsically explainable, many other (Geo)AI solutions are built on AI approaches that result in black box models.

Graph neural networks (GNNs) have become very popular in GeoAI (including in urban analytics and mobility [Jalali et al. 2023] [Liu et al. 2024]) but their black box nature limits their practical usefulness in domains where transparency and trustworthiness are crucial. To offer insights into how model predictions are made, [Liu et al. 2024] propose a spatially explicit GeoAI-based method that combines a graph convolutional network and a graph-based XAI method, called GNNExplainer to explore the correlation between urban objects.

Reproducibility et al.

The AI hype in geo is still going strong. Journals are being flooded with paper submissions and good reviewers are hard to come by. In many geo-related venues, it is still acceptable to present an AI paper without making code or model available. (We recently discussed this issue for mobility AI specifically [Graser et al. 2024].)

I’m convinced we can and should do better: quality over quantity, moving steadily, building and fixing things.

References

• Charette-Migneault, F., Avery, R., Pondi, B., Omojola, J., Vaccari, S., Membari, P., … & Sundwall, J. (2024). Machine Learning Model Specification for Cataloging Spatio-Temporal Models. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data (pp. 36-39).
• Graser, A., Jalali, A., Lampert, J., Weißenfeld, A., & Janowicz, K. (2024). MobilityDL: a review of deep learning from trajectory data. GeoInformatica, 1-33.
• Jakubik, J., Roy, S., Phillips, C., Fraccaro, P., Godwin, D., Zadrozny, B., … & Edwards, B. (2023) Foundation models for generalist geospatial artificial intelligence. arXiv preprint arXiv:2310.18660.
• Jalali, A., Graser, A., & Heistracher, C. (2023). Towards eXplainable AI for Mobility Data Science. arXiv preprint arXiv:2307.08461.
• Janowicz, K., Hitzler, P., Li, W., Rehberger, D., Schildhauer, M., Zhu, R., … & Currier, K. (2022). Know, Know Where, KnowWhereGraph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence. AI Magazine, 43(1), 30-39.
• Li, W., Arundel, S., Gao, S., Goodchild, M., Hu, Y., Wang, S., & Zipf, A. (2024). GeoAI for Science and the Science of GeoAI. Journal of Spatial Information Science, (29), 1-17.
• Liu, P., Zhang, Y., & Biljecki, F. (2024). Explainable spatially explicit geospatial artificial intelligence in urban analytics. Environment and Planning B: Urban Analytics and City Science, 51(5), 1104-1123.
• OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard (2023)
• Zhang, L. (2020). Explainable Machine Learning for Activity Modeling in GeoAI. Doctoral dissertation, George Mason University.

After the initial ChatGPT hype in 2023 (when we saw the first LLM-backed QGIS plugins, e.g. QChatGPT and QGPT Agent), there has been a notable slump in new development. As far as I can tell, none of the early plugins are actively maintained anymore. They were nice tech demos but with limited utility.

However, in the last month, I saw two new approaches for combining LLMs with QGIS that I want to share in this post:

IntelliGeo plugin: generating PyQGIS scripts or graphical models

At the QGIS User Conference in Bratislava, I had the pleasure to attend the “Large Language Models and GIS” workshop presented by Gustavo Garcia and Zehao Lu from the the University of Twente. There, they presented the IntelliGeo Plugin which enables the automatic generation of PyQGIS scripts and graphical models.

The workshop was packed. After we installed all dependencies and the plugin, it was exciting to test the graphical model generation capabilities. During the workshop, we used OpenAI’s API but the readme also mentions support for Cohere.

I was surprised to learn that even simple graphical models are actually pretty large files. This makes it very challenging to generate and/or modify models because they take up a big part of the LLM’s context window. Therefore, I expect that the PyQGIS script generation will be easier to achieve. But, of course, model generation would be even more impressive and useful since models are easier to edit for most users than code.

ChatGeoAI: chat with PyQGIS

ChatGeoAI is an approach presented in Mansourian, A.; Oucheikh, R. (2024). ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models. ISPRS Int. J. Geo-Inf., 13, 348.

It uses a fine-tuned Llama 2 model in combination with spaCy for entity recognition and WorldKG ontology to write PyQGIS code that can perform a variety of different geospatial analysis tasks on OpenStreetMap data.

The paper is very interesting, describing the LLM fine-tuning, integration with QGIS, and evaluation of the generated code using different metrics. However, as far as I can tell, the tool is not publicly available and, therefore, cannot be tested.

Are you aware of more examples that integrate QGIS with LLMs? Please share them in the comments below. I’d love to hear about them.

Today marks the release of Trajectools 2.3 which brings a new set of algorithms, including trajectory generalizing, cleaning, and smoothing.

To give you a quick impression of what some of these algorithms would be useful for, this post introduces a trajectory preprocessing workflow that is quite general-purpose and can be adapted to many different datasets.

We start out with the Geolife sample dataset which you can find in the Trajectools plugin directory’s sample_data subdirectory. This small dataset includes 5908 points forming 5 trajectories, based on the trajectory_id field:

We first split our trajectories by observation gaps to ensure that there are no large gaps in our trajectories. Let’s make at cut at 15 minutes:

This splits the original 5 trajectories into 11 trajectories:

When we zoom, for example, to the two trajectories in the north western corner, we can see that the trajectories are pretty noisy and there’s even a spike / outlier at the western end:

If we label the points with the corresponding speeds, we can see how unrealistic they are: over 300 km/h!

Let’s remove outliers over 50 km/h:

Better but not perfect:

Let’s smooth the trajectories to get rid of more of the jittering.

(You’ll need to pip/mamba install the optional stonesoup library to get access to this algorithm.)

Depending on the noise values we chose, we get more or less smoothing:

Let’s zoom out to see the whole trajectory again:

Feel free to pan around and check how our preprocessing affected the other trajectories, for example:

Earlier this year, I shared my experience using ChatGPT’s Data Analyst web interface for analyzing spatiotemporal data in the post “ChatGPT Data Analyst vs. Movement Data”. The Data Analyst web interface, while user-friendly, is not equipped to handle all types of spatial data tasks, particularly those involving more complex or large-scale datasets. Additionally, because the code is executed on a remote server, we’re limited to the libraries and tools available in that environment. I’ve often encountered situations where the Data Analyst simply doesn’t have access to the necessary libraries in its Python environment, which can be frustrating if you need specific GIS functionality.

Today, we’ll therefore start to explore alternatives to ChatGPT’s Data Analyst Web Interface, specifically, the OpenAI Assistant API. Later, I plan to dive deeper into even more flexible approaches, like Langchain’s Pandas DataFrame Agents. We’ll explore these options using spatial analysis workflow, such as:

1. Loading a zipped shapefile and investigate its content
2. Finding the three largest cities in the dataset
3. Selecting all cities in a region, e.g. in Scandinavia from the dataset
4. Creating static and interactive maps

To try the code below, you’ll need an OpenAI account with a few dollars on it. While gpt-3.5-turbo is quite cheap, using gpt-4o with the Assistant API can get costly fast.

OpenAI Assistant API

The OpenAI Assistant API allows us to create a custom data analysis environment where we can interact with our spatial datasets programmatically. To write the following code, I used the assistant quickstart and related docs (yes, shockingly, ChatGPT wasn’t very helpful for writing this code).

Like with Data Analyst, we need to upload the zipped shapefile to the server to make it available to the assistant. Then we can proceed to ask it questions and task it to perform analytics and create maps.

from openai import OpenAI

client = OpenAI()

file = client.files.create(
  file=open("H:/ne_110m_populated_places_simple.zip", "rb"),
  purpose='assistants'
)

Then we can hand the file over to the assistant:

assistant = client.beta.assistants.create(
  name="GIS Analyst",
  instructions="You are a personal GIS data analyst. Write and rund code to answer geospatial analysis questions",
  tools=[{"type": "code_interpreter"}],
  model="gpt-3.5-turbo",  # or "gpt-4o"
  tool_resources={
    "code_interpreter": {
      "file_ids": [file.id]
    }
  }  
)

Then we can start asking questions and giving our assistant tasks:

thread = client.beta.threads.create()

message = client.beta.threads.messages.create(
  thread_id=thread.id,
  role="user",
  content="Please load the zipped shapefile and describe the content"
)

assistant > The shapefile has been successfully loaded as a GeoDataFrame with the following details:

- The GeoDataFrame has 243 entries.
- It contains a total of 38 columns with various data types including integer, float, and object types.
- One of the columns is a geometry column representing the spatial information.

If you have any specific analysis or visualizations you would like to perform on this data, feel free to let me know!

The docs have a nice example for how to stream the answers back, e.g. into a Jupyter notebook. I’ve used it to create a small convenience function:

def ask_another_question(question):
    message = client.beta.threads.messages.create(
        thread_id=thread.id,
        role="user",
        content=question
    )
    with client.beta.threads.runs.stream(
        thread_id=thread.id,
        assistant_id=assistant.id,
        event_handler=EventHandler(),
    ) as stream:
        stream.until_done()

ask_another_question("Please list the three largest cities")

assistant > code_interpreter

# Sort the GeoDataFrame by the population in descending order
largest_cities = gdf.sort_values(by='pop_max', ascending=False).head(3)[['name', 'pop_max']]
largest_cities
assistant > The three largest cities in the dataset, along with their maximum population numbers, are as follows:

1. Tokyo: 35,676,000
2. New York: 19,040,000
3. Mexico City: 19,028,000

These cities have the highest recorded maximum population numbers in the dataset. Let me know if you need any further information or analysis!

ask_another_question("extract all scandinavian cities ")

assistant > To extract all the Scandinavian cities from the dataset, we can filter the GeoDataFrame based on the countries typically considered part of Scandinavia. These countries include Norway, Sweden, Denmark, Finland, and sometimes Iceland. Let's filter the GeoDataFrame to extract the Scandinavian cities.
assistant > code_interpreter

# List of countries typically considered part of Scandinavia
scandinavian_countries = ['Norway', 'Sweden', 'Denmark', 'Finland', 'Iceland']

# Filter the GeoDataFrame for Scandinavian cities
scandinavian_cities = gdf[gdf['adm0name'].isin(scandinavian_countries)][['name', 'adm0name']]

scandinavian_cities.head()  # Displaying the first few Scandinavian cities
assistant > Here are some of the Scandinavian cities extracted from the dataset:

1. Reykjavík, Iceland
2. Oslo, Norway
3. Helsinki, Finland
4. København (Copenhagen), Denmark
5. Stockholm, Sweden

These are just a few examples of Scandinavian cities present in the dataset. If you would like the complete list or perform any specific analysis on these cities, feel free to let me know!

ask_another_question(
    "please create a world map with all cities (use gray circles) "
    "with scandinavian cities highlighted as red circles. "
)

assistant > To create a world map with all cities represented as gray circles and Scandinavian cities highlighted as red circles, we will use the GeoPandas library for mapping. Let's go ahead and create the map.
assistant > code_interpreter

import matplotlib.pyplot as plt

# Plot all cities with gray circles and Scandinavian cities with red circles
fig, ax = plt.subplots(figsize=(12, 8))

# Plot all cities in gray circles
gdf.plot(ax=ax, color='lightgrey', markersize=5)

# Plot Scandinavian cities in red circles
scandinavian_cities.plot(ax=ax, color='red', markersize=50, edgecolor='black', label='Scandinavian Cities')

# Set the title
ax.set_title('World Map with Scandinavian Cities Highlighted', fontsize=16)

# Add legend
ax.legend()

# Display the map
plt.show()
assistant > It seems that there was an error while plotting the map because the GeoDataFrame `scandinavian_cities` does not have the necessary numeric data to plot the map directly.
...
plt.show()

output >

assistant > Here is the world map with all cities represented as gray circles and Scandinavian cities highlighted as red circles. The map provides a visual representation of the locations of the Scandinavian cities in relation to the rest of the cities around the world. If you need any further assistance or modifications, feel free to let me know!

To load and show the image, we can use:

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

def show_image():
    messages = client.beta.threads.messages.list(thread_id=thread.id)

    for m in messages.data:
        if m.role == "user":
            continue
        if m.content[0].type == 'image_file':
            m.content[0].image_file.file_id
            image_data = client.files.content(messages.data[0].content[0].image_file.file_id)
            image_data_bytes = image_data.read()
            with open("./out/my-image.png", "wb") as file:
                file.write(image_data_bytes)
            image = mpimg.imread("./out/my-image.png")
            plt.imshow(image)
            plt.box(False)
            plt.xticks([])
            plt.yticks([])
            plt.show() 
            break

Asking for an interactive map in an html file works in a similar fashion.

You can see the whole analysis workflow it in action here:

This way, we can use ChatGPT to perform data analysis from the comfort of our Jupyter notebooks. However, it’s important to note that, like the Data Analyst, the code we execute with the Assistant API runs on a remote server. So, again, we are restricted to the libraries available in that server environment. This is an issue we will address next time, when we look into Langchain.

Conclusion

ChatGPT’s Data Analyst Web Interface and the OpenAI Assistant API both come with their own advantages and disadvantages.

The results can be quite random. In the Scandinavia example, every run can produce slightly different results. Sometimes the results just use different assumptions such as, e.g. Finland and Iceland being part of Scandinavia or not, other times, they can be outright wrong.

As always, I’m interested to hear your experiences and thoughts. Have you been testing the LLM plugins for QGIS when they originally came out?

This release is the first to support GeoPandas 1.0.

Additionally, this release adds multiple new features, including:

• New explore() function adds interactive folium / leaflet maps
• New support for mfjson trajectory

For the full change log, check out the release page.

We have also revamped the documentation at https://movingpandas.readthedocs.io/ using the PyData Sphinx Theme:

On a related note: if you know what I need to change to get all Trajectory functions listed in the TOC on the right, please let me know.

If you downloaded Trajectools 2.1 and ran into troubles due to the introduced scikit-mobility and gtfs_functions dependencies, please update to Trajectools 2.2.

This new version makes it easier to set up Trajectools since MovingPandas is pip-installable on most systems nowadays and scikit-mobility and gtfs_functions are now truly optional dependencies. If you don’t install them, you simply will not see the extra algorithms they add:

If you encounter any other issues with Trajectools or have questions regarding its usage, please let me know in the Trajectools Discussions on Github.

Last week, I had the pleasure to meet some of the people behind the OGC Moving Features Standard Working group at the IEEE Mobile Data Management Conference (MDM2024). While chatting about the Moving Features (MF) support in MovingPandas, I realized that, after the MF-JSON update & tutorial with official sample post, we never published a complete tutorial on working with MF-JSON encoded data in MovingPandas.

The current MovingPandas development version (to be release as version 0.19) supports:

• Reading MF-JSON MovingPoint (single trajectory features and trajectory collections)
• Reading MF-JSON Trajectory
• Writing MovingPandas Trajectories and TrajectoryCollections to MF-JSON MovingPoint

This means that we can now go full circle: reading — writing — reading.

Reading MF-JSON

Both MF-JSON MovingPoint encoding and Trajectory encoding can be read using the MovingPandas function read_mf_json(). The complete Jupyter notebook for this tutorial is available in the project repo.

Here we read one of the official MF-JSON MovingPoint sample files:

traj = mpd.read_mf_json('data/movingfeatures.json')

Writing MF-JSON

To write MF-JSON, the Trajectory and TrajectoryCollection classes provide a to_mf_json() function:

The resulting Python dictionary in MF-JSON MovingPoint encoding can then be saved to a JSON file, and then read again:

import json
with open('mf1.json', 'w') as json_file:
    json.dump(mf_json, json_file, indent=4)

Similarly, we can read any arbitrary trajectory data set and save it to MF-JSON.

For example, here we use our usual Geolife sample:

gdf = gp.read_file('data/demodata_geolife.gpkg')
tc = mpd.TrajectoryCollection(gdf, 'trajectory_id', t='t')
mf_json = tc.to_mf_json(temporal_columns=['sequence'])

And reading again

import json
with open('mf5.json', 'w') as json_file:
    json.dump(mf_json, json_file, indent=4)
tc = mpd.read_mf_json('mf5.json', traj_id_property='trajectory_id' )

Conclusion

The implemented MF-JSON support covers the basic usage of the encodings. There are some fine details in the standard, such as the distinction of time-varying attribute with linear versus step-wise interpolation, which MovingPandas currently does not support.

If you are working with movement data, I would appreciate if you can give the improved MF-JSON support a spin and report back with your experiences.

With the release of GeoPandas 1.0 this month, we’ve been finally able to close a long-standing issue in MovingPandas by adding support for the explore function which provides interactive maps using Folium and Leaflet.

Explore() will be available in the upcoming MovingPandas 0.19 release if your Python environment includes GeoPandas >= 1.0 and Folium. Of course, if you are curious, you can already test this new functionality using the current development version.

This enables users to access interactive trajectory plots even in environments where it is not possible to install geoviews / hvplot (the previously only option for interactive plots in MovingPandas).

I really like the legend for the speed color gradient, but unfortunately, the legend labels are not readable on the dark background map since they lack the semi-transparent white background that has been applied to the scale bar and credits label.

Speaking of reading / interpreting the plots …

You’ve probably seen the claims that AI will help make tools more accessible. Clearly AI can interpret and describe photos, but can it also interpret MovingPandas plots?

ChatGPT 4o interpretations of MovingPandas plots

Not bad.

And what happens if we ask it to interpret the animated GIF from the beginning of the blog post?

So it looks like ChatGPT extracts 12 frames and analyzes them to answer our question:

Its guesses are not completely off but it made up the facts such as that the view shows “how traffic speeds vary over time”.

The problem remains that models such as ChatGPT rather make up interpretations than concede when they do not have enough information to make a reliable statement.

Today, I took ChatGPT’s Data Analyst for a spin. You’ve probably seen the fancy advertising videos: just drop in a dataset and AI does all the analysis for you?! Let’s see …

Of course, I’m not going to use some lame movie database or flower petals data. Instead, let’s go all in and test with a movement dataset.

You don’t get a second chance to make a first impression, they say. — Well, Data Analyst, you didn’t impress on the first try. How hard can it be to guess the delimiter and act accordingly?

Anyway, let’s help it a little:

That looks much better. It makes an effort to guess what the columns could mean and successfully identifies the spatiotemporal information.

Now for some spatial analysis. On first try, it didn’t want to calculate the length of the trajectories in geographic terms, but we can make it to:

It will also show the code used to get to the results:

And indeed, these are close enough to the results computed using MovingPandas:

“What about plots?” I hear you ask.

For a first try, not bad at all:

Let’s see if we can push it further:

Looks like poor Data Analyst ended up in geospatial library dependency hell

It’s interesting to watch it try find a solution.

Alas, no background map appears:

Not giving up yet :)

Woah, what happened here? It claims it created an interactive map in an HTML file.

And indeed it did:

This has been a very interesting experiment for me with many highs and lows. The whole process is a bit hit and miss. But when it does work, it’s fun.

I wasn’t sure what to expect with regards to Data Analyst’s spatial data processing capabilities. Looks like there are enough examples in its training data to find solutions for the basic trajectory analysis problems I asked it solve today, eventually, at least.

What’s the conclusion? Most AI marketing videos are severely overselling the capabilities of these tools. However, that doesn’t mean that they are completely useless, either. I’m looking forward to seeing the age of smaller open source models specifically trained for geospatial analysis to finally make it unnecessary for humans to memorize data analysis library syntax.

Today marks the 2.1 release of Trajectools for QGIS. This release adds multiple new algorithms and improvements. Since some improvements involve upstream MovingPandas functionality, I recommend to also update MovingPandas while you’re at it.

If you have installed QGIS and MovingPandas via conda / mamba, you can simply:

conda activate qgis
mamba install movingpandas=0.18

Afterwards, you can check that the library was correctly installed using:

import movingpandas as mpd
mpd.show_versions()

Trajectools 2.1

The new Trajectools algorithms are:

• Trajectory overlay — Intersect trajectories with polygon layer
• Privacy — Home work attack (requires scikit-mobility)
  • This algorithm determines how easy it is to identify an individual in a dataset. In a home and work attack the adversary knows the coordinates of the two locations most frequently visited by an individual.
• GTFS — Extract segments (requires gtfs_functions)
• GTFS — Extract shapes (requires gtfs_functions)
  • These algorithms extract public transport routes (GTFS shapes) and route segments between stops (GTFS segments) from GTFS ZIP files using gtfs_functions.Feed.shapes and .segments, respectively.

Furthermore, we have fixed issue with previously ignored minimum trajectory length settings.

Scikit-mobility and gtfs_functions are optional dependencies. You do not need to install them, if you do not want to use the corresponding algorithms. In any case, they can be installed using mamba and pip:

mamba install scikit-mobility
pip install gtfs_functions

MovingPandas 0.18

This release adds multiple new features, including

• Method chaining support for add_speed(), add_direction(), and other functions
• New TrajectoryCollection.get_trajectories(obj_id) function
• New trajectory splitter based on heading angle
• New TrajectoryCollection.intersection(feature) function
• New plotting function hvplot_pts()
• Faster TrajectoryCollection operations through multi-threading
• Added moving object weights support to trajectory aggregator

For the full change log, check out the release page.