Tag: gis

Oslandia is the main partner of OPENGIS.ch around QField. We are proud today to forward the announcement of the new QField release 3.4 “Ebo”.

Main highlights

A new geofencing framework has landed, enabling users to configure QField behaviors in relation to geofenced areas and user positioning. Geofenced areas are defined at the project-level and shaped by polygons from a chosen vector layer. The three available geofencing behaviours in this new release are:

• Alert user when inside an area polygon;
• Alert user when outside all defined area polygons and
• Inform the user when entering and leaving an area polygons.

In addition to being alerted or informed, users can also prevent digitizing of features when being alerted by the first or second behaviour. The configuration of this functionality is done in QGIS using QFieldSync.

Pro tip: geofencing settings are embedded within projects, which means it is easy to deploy these constraints to a team of field workers through QFieldCloud. Thanks Terrex Seismic for sponsoring this functionality.

QField now offers users access to a brand new processing toolbox containing over a dozen algorithms for manipulating digitized geometries directly in the field. As with many parts of QField, this feature relies on QGIS’ core library, namely its processing framework and the numerous, well-maintained algorithms it comes with.

The algorithms exposed in QField unlock many useful functionalities for refining geometries, including orthogonalization, smoothing, buffering, rotation, affine transformation, etc. As users configure algorithms’ parameters, a grey preview of the output will be visible as an overlay on top of the map canvas.

To reach the processing toolbox in QField, select one or more features by long-pressing on them in the features list, open the 3-dot menu and click on the process selected feature(s) action. Are you excited about this one? Send your thanks to the National Land Survey of Finland, who’s support made this a reality.

QField’s camera has gained support for customized ratio and resolution of photos, as well as the ability to stamp details – date and time as well as location details – onto captured photos. In fact, QField’s own camera has received so much attention in the last few releases that it was decided to make it the default one. On supported platforms, users can switch to their OS camera by disabling the native camera option found at the bottom of the QField settings’ general tab.

Wait, there’s more

There are plenty more improvements packed into this release from project variables editing using a revamped variables editor through to integration of QField documentation help in the search bar and the ability to search cloud project lists. Read the full 3.4 changelog to know more, and enjoy the release!

Contact us !

A question concerning QField ? Interested in QField deployment ? Do not hesitate to contact Oslandia to discuss your project !

QField 3.4 is out, and it won’t disappoint. It has tons of new features that continue to push the limits of what users can do in the field.

Main highlights

A new geofencing framework has landed, enabling users to configure QField behaviors in relation to geofenced areas and user positioning. Geofenced areas are defined at the project-level and shaped by polygons from a chosen vector layer. The three available geofencing behaviours in this new release are:

• Alert user when inside an area polygon;
• Alert user when outside all defined area polygons and
• Inform the user when entering and leaving an area polygons.

In addition to being alerted or informed, users can also prevent digitizing of features when being alerted by the first or second behaviour. The configuration of this functionality is done in QGIS using QFieldSync.

Pro tip: geofencing settings are embedded within projects, which means it is easy to deploy these constraints to a team of field workers through QFieldCloud. Thanks Terrex Seismic for sponsoring this functionality.

QField now offers users access to a brand new processing toolbox containing over a dozen algorithms for manipulating digitized geometries directly in the field. As with many parts of QField, this feature relies on QGIS’ core library, namely its processing framework and the numerous, well-maintained algorithms it comes with.

The algorithms exposed in QField unlock many useful functionalities for refining geometries, including orthogonalization, smoothing, buffering, rotation, affine transformation, etc. As users configure algorithms’ parameters, a grey preview of the output will be visible as an overlay on top of the map canvas.

To reach the processing toolbox in QField, select one or more features by long-pressing on them in the features list, open the 3-dot menu and click on the process selected feature(s) action. Are you excited about this one? Send your thanks to the National Land Survey of Finland, who’s support made this a reality.

QField’s camera has gained support for customized ratio and resolution of photos, as well as the ability to stamp details – date and time as well as location details – onto captured photos. In fact, QField’s own camera has received so much attention in the last few releases that we have decided to make it the default one. On supported platforms, users can switch to their OS camera by disabling the native camera option found at the bottom of the QField settings’ general tab.

Wait, there’s more

There are plenty more improvements packed into this release from project variables editing using a revamped variables editor through to integration of QField documentation help in the search bar and the ability to search cloud project lists. Read the full 3.4 changelog to know more, and enjoy the release!

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.

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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?

The GRASS GIS 8.4.0 release provides more than 520 improvements and fixes with respect to the release 8.3.2.

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Today, we celebrate a true geospatial legend: GRASS GIS!

The post Happy 41st birthday, GRASS GIS! appeared first on Markus Neteler Consulting.

The GRASS GIS 8.4.0RC1 release provides more than 515 improvements and fixes with respect to the release 8.3.2. Please support us in testing this release candidate.

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