Unlock the Power of Satellite Data, Remote Sensing, and Artificial Intelligence with TerraEye

TerraEye revolutionizes mineral exploration and environmental monitoring by integrating advanced artificial intelligence (AI) into its platform. By transforming vast amounts of raw satellite data into actionable insights, TerraEye enables faster, more accurate, and cost-effective decision-making processes.

Decoding AI in Geology

At TerraEye, we believe that AI should be a transparent tool, not a black box. Our AI solutions are designed to help uncover insights that might have been missed, providing clarity rather than confusion. We collaborate closely with expert exploration geologists who contribute their knowledge to train and refine our algorithms. This partnership ensures that our AI is grounded in real-world expertise, enhancing its effectiveness and reliability.

AI Clustering for Geological Analysis

One of the major AI applications in TerraEye’s platform is its use of AI-based clustering algorithms, such as K-means, for lithological mapping. This process involves applying machine learning techniques to multispectral and hyperspectral satellite data to classify geological structures based on their spectral characteristics. Initially, Principal Component Analysis (PCA) is used to reduce the dimensionality of the data, focusing only on the most relevant features, which significantly enhances the efficiency of data analysis. By combining PCA with K-means clustering, the system can effectively identify different geological formations and lithologies. This tool is particularly valuable in early-stage exploration, where regional geological mapping is crucial for forming a comprehensive understanding of the area and planning further exploration campaigns​​.


Lineament Detection

TerraEye also employs AI in the detection of lineaments—linear features on the Earth's surface that may indicate geological structures such as faults or fractures. These are often associated with mineral deposits, making them important in mineral exploration. TerraEye uses the Canny edge detection algorithm, which is highly effective for identifying such linear features from satellite images, particularly from Digital Elevation Models (DEM). This AI-driven approach allows for the precise identification of lineaments, which can serve as pathways for hydrothermal fluids that lead to mineralization. This enhances the ability of geologists to target areas with high mineral potential in their exploration efforts​​.


AI Rock Outcrop Detection with PlanetScope Data

In addition to lineament detection, TerraEye leverages AI to identify rock outcrops using high-resolution PlanetScope satellite imagery with a spatial resolution of 3 meters by 3 meters. Our advanced algorithms analyze this detailed imagery to detect exposed rock formations that are critical for mineral exploration. The high-resolution data allows for precise mapping of outcrops, even in areas with complex terrain or dense vegetation where traditional methods might overlook them. This capability enhances geological understanding by providing accurate information on surface geology, enabling geologists to pinpoint potential mineralization zones more effectively and efficiently.



False Color Composites (FCC) and AI

Another AI-enhanced feature within TerraEye’s platform is its use of False Color Composites (FCC). FCC images leverage spectral bands that are beyond the range of human vision to highlight geological features and mineralization zones. These images provide unique insights into rock types and surface features that are invisible in standard satellite imagery. TerraEye integrates AI with FCC analysis, allowing the platform to automatically interpret these images and identify mineral-rich areas with high precision. This process accelerates mineral discovery, helping to distinguish between different lithologies and quickly identify promising exploration targets​​.

Spectral Indices and AI

Spectral indices are key to TerraEye’s satellite-based mineral exploration capabilities. Spectral indices use specific wavelength combinations to highlight the presence of certain minerals or alteration zones, which are crucial in identifying potential mineral deposits. TerraEye’s platform uses AI to process these spectral indices and combine them with other data layers, such as FCC images and lineament maps, to deliver detailed and accurate mineral maps. The algorithms enable rapid analysis of vast areas, identifying regions with potential for further exploration by analyzing mineral spectral signatures. This capability ensures faster and more cost-efficient exploration compared to traditional methods​​.


Automated Exploration Support

At present, TerraEye offers fully automated processes that are effective for the majority of cases. Our AI-powered platform automates satellite data processing, analyzing large datasets in near real-time and delivering actionable analyses in user-friendly formats. Users can begin interpreting the first results within just 10 minutes, facilitating a quick start to the analysis process. This automation frees up experts to focus more on interpretation and decision-making rather than data preparation.

We acknowledge the limitations of a fully automated approach and are committed to continuous improvement. We are developing tools that will allow users to retrain and enhance their predictions, tailoring the analyses to their specific needs and scenarios.


Introducing Geo Copilot

In the upcoming months, we will be rolling out Geo Copilot—a new feature designed to help summarize all your data and keep your projects organized. Geo Copilot will assist in managing the wealth of information generated, ensuring that insights are easily accessible and actionable, ultimately making your workflow more efficient.

Impact of AI on Cost and Efficiency

One of the most notable benefits of using AI in TerraEye’s platform is the significant reduction in exploration costs. AI accelerates the mineral exploration process by quickly identifying high-potential areas at early stages of a project. Automation ensures faster access to insights, reducing the time spent on manual data preparation and allowing more focus on interpreting results. This streamlined approach boosts efficiency and enables better resource allocation during exploration and environmental monitoring.

Enhancing Fieldwork Efficiency

The AI-driven acceleration in TerraEye’s exploration process helps identify key areas in the field where conducting on-site inspections or collecting samples can provide the most value. While AI won’t replace fieldwork, it significantly enhances its efficiency by highlighting the locations that deserve closer attention. Given that field visits come with substantial costs, it’s crucial to make the most of them. Proper preparation, guided by AI insights, ensures that time in the field is used effectively and that resources are allocated wisely. The more effort put into planning, the easier the execution becomes.

Stay Ahead with TerraEye

By integrating innovative AI solutions into your projects, you not only optimize operations but also gain a competitive edge. TerraEye’s integration of AI across multiple aspects of its platform—from lithological mapping and lineament detection to spectral analysis—enables us to offer highly accurate, fast, and cost-effective solutions for mineral exploration. The use of AI enhances the speed and precision of our services, making exploration processes more efficient and sustainable, while supporting timely decision-making.

App.terraeye.co
www.terraeye.co

Popular posts from this blog

Lineament Maps – What They Are and How They Are Created

Image
TerraEye introduces a lineament detection algorithm based on Digital Elevation Model (DEM) data. This approach allows for the identification of surface expressions of linear structures, which can be part of local geological formations significant for mineralization. As such, lineament data serves as an important input in the analysis of areas for potential mineral resources. The detection process is carried out using the Canny edge detection algorithm (Mohammadpour et al., 2020), which has proven to be effective for this purpose. In this version, the results are generated from a DEM derived from the global Copernicus GLO-30 dataset. The Canny algorithm operates in several stages: first, it filters noise to eliminate irrelevant details, then identifies and selects pixels where DEM values change sharply. From these selected pixels, lines are created, and in the final step, thresholding allows for the extraction of strong and clear edges or more subtle ones depending on the settings. The ...
Read more

Navigating TerraEye: An In-Depth Guide to Mineral Exploration Using Sentinel-2 and EMIT Data

Image
  In the complex field of mineral exploration, the ability to analyze vast amounts of satellite data accurately and efficiently is a key competitive advantage. TerraEye, a cloud-based application, provides advanced tools to process and interpret satellite data, particularly from Sentinel-2 and EMIT sensors. This blog post offers a detailed guide on how to navigate TerraEye, focusing on data processing and how to interpret the results of automated analyses. Additionally, we'll cover how to order additional datasets and request further processing of your Area of Interest (AOI) directly within the app. How to Get Started with TerraEye Getting started with TerraEye is straightforward: Sign Up: Create an account on the TerraEye website by entering your details and agreeing to the terms of service. Log In: Use your credentials to access the TerraEye dashboard. Explore Demo Sites: Familiarize yourself with TerraEye’s features by exploring the demo sites available on your dashboard. Cre...
Read more