Increase visibility into activity at and between key supply chain locations by analyzing high-resolution satellite imagery.
Intro
As supply chains become increasingly complex and geographically distributed, it becomes more difficult to effectively monitor, manage, and ensure the continuity of manufacturing and distribution processes. Satellite imagery and analytics provide visibility into that supply chain, enabling you to monitor activity automatically and implement alert systems for risks or anomalies.
Overview
Access high-resolution satellite imagery
Task regular imagery of critical locations in your supply chain. Access that imagery in the UP42 console, integrated into your cloud storage, or via API in your own tools.
Monitor activity and risks in your supply chain
Run your satellite imagery through processing algorithms to detect change and count the number of objects of interest, such as trucks, storage tanks, and more.
Combine insights in your analytics tools
Integrate the outputs of your change and object detection algorithms into analytics tools, existing supply chain management software, or alert systems - all via API.
The technical details
Data
High-resolution satellite imagery
Order regularly tasked imagery at a 0.5 m resolution from Pleiades or a 1.5 m resolution from SPOT with a guaranteed maximum level of cloud coverage and incidence angle. Receive your imagery directly in your user storage in the UP42 platform or upload it into your cloud storage, where you can integrate it into an UP42 workflow with ease. Supplement your satellite imagery with real-time weather and AIS data for more holistic analysis.
Processing
Detect objects in your imagery
Identify and count the number of various objects in your imagery. For example, you can apply truck detection from Orbital Insights to your Pleiades imagery to analyze whether the number of trucks at a warehouse is sufficient to distribute a large shipment or whether that large shipment is already on route.
Risk analysis
Identify change and monitor risks
Create powerful time-series data sets by integrating your NDVI analysis's outputs into the analytics tool of your choice. For example, you could access the data set in Jupyter Notebooks and plot changes in NDVI year-on-year, taking into account seasonality. These NDVI values can be correlated to crop yield in a given country to predict crop yield for the upcoming year. These predictions can be used as one input for the prediction of agricultural commodity prices.
API & Python SDK
Automate your analysis
Build and automate analytics workflows leveraging UP42's infrastructure but working in Jupyter Notebooks or other command-line tools. With our Python SDK, you can access all the functionality of the UP42 platform within the command line, enabling UP42 to work seamlessly within your existing processes, interacting with your regular analytics workflows, tools, and libraries.
import up42
up42.authenticate(project_id="12345", project_api_key="12345")
project=up42.initialize_project()
workflow = project.create_workflow(name="Supply Chain", use_existing=True)
input_tasks=['oneatlas-pleiades-aoiclipped', 'tiling', 'orbital_pleiades_trucks', 'up42-countobjects']
workflow.add_workflow_tasks(input_tasks=input_tasks)
parameter = workflow.construct_parameters(
geometry=[13.4466, 52.495598, 13.453016, 52.499216],
geometry_operation="bbox",
scene_ids="DS_PHR1A_202005061019188_FR1_PX_E013N52_0513_01183")
parameter['tiling:1']['tile_width'] = 1232
parameter['tiling:1']['tile_height'] = 1232
job = workflow.run_job(input_parameters=parameter, track_status=True)
job.download_results()