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Skeye Research Areas Overview

Our Research

We are constantly growing and evolving our work, exploring new directions in environmental monitoring, agriculture, mining and raw materials, fertilizers, urban planning, critical infrastructure, and change detection.

Research Areas

Agriculture

Crop Identification

We are researching AI-powered crop identification using satellite imagery, aerial data, and machine learning to classify different crop types across agricultural areas. This work aims to support better monitoring of land use, crop distribution, and agricultural activity over time.

By identifying crops more accurately and consistently, this research can help authorities, farmers, and organisations improve planning, reporting, and decision-making in the agricultural sector.

Aerial view of patchwork agricultural fields in shades of green and brown, divided by tree lines and paths, showing distinct crop parcels and land use.
Aerial imagery of distinct crop parcels the kind of field-level variation our models learn to classify.

Land Use · Urban Planning

Vacant and Derelict Land

We are exploring methods to detect and monitor vacant and derelict land using geospatial data, satellite imagery, and AI-based analysis. This research focuses on identifying unused, abandoned, or underutilised land areas that may require further inspection, rehabilitation, or policy action.

The goal is to provide clearer visibility over land condition and usage, helping support urban planning, environmental management, and sustainable land development.

Aerial view of an urban area with a vacant, overgrown parcel of land outlined in red, surrounded by terraced housing, roads, and parked cars.
A vacant parcel outlined against surrounding housing the kind of underused land this research aims to detect and monitor.

Agronomy · Soil Health

Soil Health & Agronomy

We are researching how satellite imagery, vegetation indices, agronomic data, and soil health information can support better fertilisation planning and crop management. This includes analysing crop health, vegetation patterns, soil conditions, and field variability to better understand where agricultural inputs may be needed and how management practices can improve overall crop productivity and soil performance.

This research aims to support more efficient farming practices, reduce unnecessary fertiliser use, and improve decision-making for healthier and more productive crops.

Aerial view of farmland overlaid with a colour-coded vegetation index heatmap, ranging from red and orange through yellow and green to blue, showing variation in crop health across fields.
A vegetation-index heatmap over fields highlighting variability in crop health that guides where inputs are needed.

Environment · Land Degradation

Desertification

We are investigating the use of satellite imagery and environmental indicators to monitor desertification and land degradation. This research focuses on detecting changes in vegetation cover, soil exposure, and land condition over time.

By identifying early signs of degradation, this work can help support mitigation strategies, environmental monitoring, and long-term land preservation efforts.

Side-by-side satellite comparison of the same region at two points in time: the left image shows dry, bare, brown terrain while the right shows greener vegetation cover and water, illustrating change in land condition.
The same region at two points in time the kind of change in vegetation and soil exposure this research tracks to detect land degradation.

Oil & Gas · Critical Infrastructure

Critical Infrastructure Monitoring

We are exploring methods to monitor pipelines, storage facilities, and other critical infrastructure using satellite imagery, geospatial data, and AI-based analysis. This research focuses on detecting changes, disturbances, and potentially risky activity across large infrastructure corridors and rights-of-way.

The goal is to provide earlier and clearer visibility of activities such as third-party construction, excavation, encroachment, land-use changes, and vegetation disturbance. Detected changes can be reviewed through reports showing where and when an event occurred, helping teams identify areas that may require further inspection or investigation.

Satellite-based monitoring can also complement technologies such as drone inspections and Distributed Acoustic Sensing (DAS). Skeye provides wide-area coverage to identify and prioritize areas of interest, while other technologies can be used to investigate higher-risk locations in greater detail.

Satellite view of an oil and gas processing facility with storage tanks, pipelines and access roads crossing the surrounding desert.
A satellite view of an oil & gas facility and the pipelines and rights-of-way crossing the land around it the kind of infrastructure and third-party activity this research monitors for change.

Agriculture · Soil Management

Tillage Detection & Regenerative Agriculture

We are researching the use of high-resolution satellite imagery, radar data, and machine learning to detect soil tillage and monitor bare soil practices across agricultural land. This work focuses on identifying instances of soil disturbance, tillage intensity, and ground cover retention to support sourcing from farms that practice regenerative agriculture.

By reliably distinguishing between tilled fields and minimal-till or no-till practices, this research aims to help supply chain managers, buyers, and agricultural organisations verify sustainable soil management, support carbon farming initiatives, and ensure compliance with regenerative sourcing standards.

Aerial view of adjacent farm fields: a bare, brown tilled field sits directly beside a green, actively growing field, with other cropped and harvested parcels around them.
A satellite view showing bare, tilled soil contrasted with cover-cropped fields highlighting the field-level surface differences this research learns to detect.