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  • Writer's pictureAndy Bohnhoff

Utilizing AI and GIS in the Energy Industry

The intersection of Artificial Intelligence (AI) and Geographic Information Systems (GIS) has ushered in a new era of possibilities, transforming the way businesses and industries harness spatial data. In this article, we will explore how Platte River Analytics, an Esri business partner, is leveraging AI and Machine Learning (ML) to revolutionize the GIS landscape, with a specific focus on their groundbreaking applications in the solar and oil and gas sectors.


GIS with Esri ArcGIS AI ML
Platte River utilized an AI tool in ArcGIS Pro to detect open parking lots in Golden, CO

Solar Site Selection using AI in GIS

Platte River Analytics has recently harnessed the power of AI to assist solar clients in identifying optimal locations for solar installations in major cities. By employing Esri's Segment Anything Model (SAM), which utilizes both Grounding DINO and Segment Anything Model, the process is streamlined through free-form text prompts. Grounding DINO acts as an open-set object detector, finding objects based on text prompts, while the Segment Anything Model is employed to segment objects within a specified region of interest represented by bounding boxes or points.


In practical terms, this means that solar clients can input text descriptions such as "parking lots" or "solar panels on rooftops" and the AI model will extract features, generate masks, and convert them into GIS features. The resulting information helps solar companies efficiently identify suitable locations for solar panels, considering factors like available open space and sunlight exposure.


To see more about this project, which was published in the winter edition of Esri's ArcNews, click on this link.


GIS Solar Site Selection in Esri ArcGIS Pro
Solar Site Selection using GIS

Oil and Gas Industry Revolution: Aerial Site Inventory

Platte River Analytics is currently evaluating the application of Esri's Segment Anything Model in the oil and gas industry, with a focus on detecting well pads and equipment. This innovative approach involves using free-form text prompts to initiate the model, enabling the extraction of features within specified regions of interest.


Aerial imagery and AI models allow for real-time or periodic monitoring of oil and gas sites without the need for physical presence. Drones equipped with high-resolution cameras or satellite imagery can capture detailed visuals of the entire site, providing up-to-date information on infrastructure, equipment, and changes in the landscape.


A couple other useful applications within the oil and gas industry are change detection and infrastructure monitoring. Aerial imagery enables the detection of changes or anomalies in the infrastructure over time. This can be crucial for identifying potential issues, such as equipment malfunctions, leaks, or unauthorized activities, allowing for swift corrective action. Aerial imagery also provides a bird's-eye view of access roads, well pads, and other infrastructure elements. Monitoring these features helps assess road conditions, identify maintenance needs, and plan for infrastructure improvements.


Machine Learning models and aerial imagery can help energy companies provide up to date information on infrastructure, equipment, and changes in the landscape

By leveraging aerial imagery and machine learning technologies, the oil and gas industry can enhance operational efficiency, reduce costs, and improve overall safety and environmental compliance. Automation of inventory-related tasks further frees up workers for more strategic and value-added activities while minimizing the risks associated with manual data collection in the field.


GIS in Esri ArcGIS Pro using ML and AI
Using ML and GIS to detect Oil and Gas Pads

 

Platte River Analytics is an Esri Business Partner
Platte River Analytics is an Esri Business Partner

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