Satellite Analytics datasets are derived from the processing of satellite imagery, such as optical images, synthetic aperture radar (SAR), and infrared data, to produce insights about physical world activity.

These datasets are used to assess economic activity, production levels, risk exposure, and inventory volumes in near-real time utilising sources beyond the production data released by the organisations which run these facilities.

Satellite-derived indexes have been developed to cover:

• Mining output

• Hot metal production

• Oil storage levels (e.g., Cushing)

• Inventory trends at industrial facilities

Rather than relying on traditional historical models, these datasets leverage direct measurement through remote sensing technologies, often augmented by machine learning.

Coverage

Coverage varies by application and supplier, but currently includes:

• Global mining locations, selectively monitored

• Hot metal production facilities (e.g. pig iron, zinc smelters)

• Oil storage sites, particularly those with large visible tank farms (e.g. Cushing)

• Select industrial and logistics facilities tied to key commodities

Sources

Satellite analytics datasets are built using:

• Commercial satellite imagery providers (optical, SAR, and infrared)

• Thermal/infrared analysis for detecting smelter activity

• Image processing algorithms and object recognition techniques

• Machine learning models for pattern detection, classification, and trend extraction

Methodology

The general workflow includes:

1. Remote sensing of target sites using high-resolution satellites

2. Data extraction using algorithms that detect changes in ground features, or temperature.

3. Index construction, typically benchmarked against a historical baseline

4. Time-series analysis to track production, throughput, or storage fluctuations

Additional specific methods based on use-cases:

• Production Indexes: Use thermal imaging to detect furnace heat levels at smelters

• Storage Analysis: Detect floating roof positions on storage tanks to infer fill levels

Why This Data?

Satellite analytics provides early, independent insight into commodity supply chains, especially where conventional data is unavailable or delayed.

• Used by traders anticipating supply-demand shifts before official releases

• Used by Commodity analysts building new pricing signals

• Also used Governments or NGOs monitoring unreported industrial activity

Benefits:

• Forward-looking indicators, often preceding traditional reporting

• Applicable across commodities markets (e.g. metals, energy, agriculture)

• Offers visibility into opaque production environments, where output is underreported or delayed

• Scalable and flexible, adapting to new sites or regions