Easy monitoring example¶

Notebook contributors: Ronald Tabernig, William Albert, July 2025

This notebook demonstrates the data preparation. The dataset contains 6 scans of a rockfall area in Trier, Germany captured hourly in the night from 25 August 2024 to 26 August 2024. We've prepared the needed functionality in the helper functions.

To make this work with your own list of point clouds, you simply need to adjust the url and paths when downloading. And make sure the name and timestamp of each file is matching the format defined in the configuration.

Imports¶

In [1]:

import sys
sys.path.insert(0, "../src")
from fourdgeo.helpers.getting_started import *

import sys sys.path.insert(0, "../src") from fourdgeo.helpers.getting_started import *

Get the data¶

In [2]:

# Load the rockfall data or use your own data by giving the path to the data folder variable
data_folder = download_example_data({
    "data_url": "https://zenodo.org/api/records/16153369/files/rockfall_trier.zip/content",
    "data_hash": "77b343183c86cbc3f72e0edbe362cc3219d41e00fcf4389ab650a09a13b1b1ec",
    "file_name": "rockfall_trier.zip",
    "data_folder": "data/rockfall_trier",
})

# Load the rockfall data or use your own data by giving the path to the data folder variable data_folder = download_example_data({ "data_url": "https://zenodo.org/api/records/16153369/files/rockfall_trier.zip/content", "data_hash": "77b343183c86cbc3f72e0edbe362cc3219d41e00fcf4389ab650a09a13b1b1ec", "file_name": "rockfall_trier.zip", "data_folder": "data/rockfall_trier", })

Projections¶

Prepare the configuration file¶

We use a configuration dictionary that contains general project settings like the output_folder and the relevant settings for the point cloud projection. For the projection, parameters like the camera_position and the resolution_cm are essential.

In [3]:

configuration = get_example_configuration()

configuration = get_example_configuration()

Generate and populate the data model with the input point clouds¶

The final json file is now finished and stored.

In [ ]:

list_background_projections = convert_point_cloud_time_series_to_datamodel(data_folder, configuration)

list_background_projections = convert_point_cloud_time_series_to_datamodel(data_folder, configuration)

Visualise the data in the dashboard¶

To see your results in the actual dashboard, we need to host the created json file and images to make them accessbile. As a quick and easy solution, we can use the http.server python library. Running the following cell will host your newly converted data and make it available for a dashboard to read it.

For example, this dashboard reads that data and visualises your images! (This link only works if you didn't change the port number in the configuration)

If you want to create your own dashboard, simply set the data source to http://localhost:PORT_NUMBER/out/getting_started/data_model.json.

In [ ]:

host_data(configuration)

host_data(configuration)

Putting it all together¶

Heres every step in one so you can copy and paste it into your own environment! Remember to also copy the helper functions.

In [ ]:

import sys
sys.path.insert(0, "../src")
from fourdgeo.helpers.getting_started import *

data_folder = download_example_data()

configuration = get_example_configuration()

list_background_projections = convert_point_cloud_time_series_to_datamodel(data_folder, configuration)

host_data(configuration)

import sys sys.path.insert(0, "../src") from fourdgeo.helpers.getting_started import * data_folder = download_example_data() configuration = get_example_configuration() list_background_projections = convert_point_cloud_time_series_to_datamodel(data_folder, configuration) host_data(configuration)