Remotely Detect Encroachment

Intro

UP42 enables large-scale encroachment detection

UP42 enables encroachment to be detected and insights to be gathered on the rate of development, size of encroachment, and projected costs and risks. We offer near-real-time Earth observation data, off-the-shelf algorithms, and powerful infrastructure to detect and mitigate encroachment for your areas of interest.

Overview

Archive or on-demand satellite imagery

Either task a satellite for specific date ranges or use the latest archive imagery, filtered by date, cloud cover, and area of your customers' infrastructure.

Change detection processing blocks

Access change detection and land use algorithms to give your customers the insights they need to monitor and prevent encroachment on their infrastructure.

Trigger regular processing via API

Run your encroachment detection analytics workflows on a regular basis or when new image data becomes available through the powerful UP42 API.

Benefits

Benefits for you

Save costs by purchasing only the data and processing the algorithm covering exactly your customer's AOI.
Save time by utilizing our APIs to automate your analytics workflows fully.
Avoid costly and time-consuming setup by building on our tried-and-tested infrastructure.
Supplement archive data with the latest data, utilizing our bespoke tasking service.

Benefits for your customers

Get timely insights to proactively respond to encroachment, avoiding costly outages or property damage.
Easily detect different types of encroachment, from settlements to vegetation.
Ensure data, and therefore insights are as near-real-time as possible.

Build your solution with UP42

Encroachment detection for:

Land Use Management

Identify the encroachment of unwanted vegetation, water bodies, and settlements on farmland and pastures.

Infrastructure & Utilities

Monitor vegetative encroachment on transport infrastructure and settlement encroachment on high-value assets.

The technical details

How can you use UP42 to build encroachment detection solutions?

Building blocks

Choose your data and analytics

Create a workflow using data and analytics from vast options available on the UP42 marketplace. Or make UP42 your own but bringing in your own data and algorithms to run on our scalable infrastructure. Choose from a range of satellite data sources, weather data, aerial data, and more to feed pre-processing and machine-learning algorithms that are perfect for detecting encroachment.

Processing

Apply algorithms to your data

Once you've built your workflow, trigger your analysis with full transparency into progress and pricing. Jobs can be triggered manually or automatically through the API to automatically run analysis when new archive data is available for your customer's area of interest and requirements, such as cloud cover.

Insights

Download your output or call via API

Access your output as a file download within the UP42 console or call the UP42 API to integrate or embed your output into your own solution. Outputs of the change detection and land cover algorithms can be downloaded as a GeoTIFF to alert your customers to changes in land use or reduction in the area surrounding critical infrastructure.

API

Automate, embed, and scale your analytics

Through the UP42, and supported by the Python SDK, it is easier than ever to automate your analysis based on specific events, triggers, or customers' requests. You can easily call intermediate or final outputs via API to integrate them into your application. Furthermore, for larger AOIs, you can seamlessly scale your encroachment detection by parallelizing analysis.

Access our Python SDK

import up42
up42.authenticate(project_id="12345", project_api_key="12345")

project=up42.initialize_project()
workflow = project.create_workflow(name="Encroachment", use_existing=True)
input_tasks=['oneatlas-pleiades-aoiclipped','tiling','change-detection']
workflow.add_workflow_tasks(input_tasks=input_tasks)

parameter = workflow.construct_parameters(
    geometry=[13.46485, 52.497329, 13.465558, 52.497969],
    geometry_operation="bbox",
    scene_ids=[
        "DS_PHR1B_202004281031350_FR1_PX_E013N52_0513_01239",
        "DS_PHR1A_202005061019188_FR1_PX_E013N52_0513_01183"])
parameter["tiling:1"].update({"match_extents":True})

job = workflow.run_job(input_parameters=parameter, track_status=True)
job.download_results()

Use Case