Invented by Joshua Adler, Sourcewater Inc

The market for image processing of aerial images for energy infrastructure analysis by pre-processing the image selection is rapidly growing. With the increasing demand for renewable energy sources and the need to optimize the efficiency of existing energy infrastructure, the use of aerial images and advanced image processing techniques has become essential. Aerial images provide a bird’s eye view of energy infrastructure, allowing for a comprehensive analysis of power plants, wind farms, solar installations, and transmission lines. These images capture detailed information about the layout, condition, and performance of energy infrastructure, enabling operators and analysts to make informed decisions and improve operational efficiency. However, the sheer volume of aerial images can be overwhelming, making it challenging to extract meaningful insights. This is where pre-processing techniques come into play. Pre-processing involves selecting and filtering images based on specific criteria, such as image quality, resolution, and relevance to the analysis objectives. The market for image processing of aerial images for energy infrastructure analysis is driven by several factors. Firstly, the increasing adoption of renewable energy sources, such as solar and wind, has led to a significant expansion of energy infrastructure. Aerial images provide a cost-effective and efficient way to monitor and analyze these installations, ensuring optimal performance and maintenance. Secondly, advancements in image processing algorithms and technologies have made it easier to extract valuable information from aerial images. Machine learning and artificial intelligence techniques can automatically detect and classify objects, identify anomalies, and predict potential issues in energy infrastructure. This automation saves time and resources, allowing operators to focus on critical tasks and make data-driven decisions. Furthermore, the growing emphasis on sustainability and environmental impact assessment has increased the demand for accurate and detailed analysis of energy infrastructure. Aerial images provide a comprehensive view of the entire energy landscape, enabling stakeholders to assess the environmental impact, identify potential risks, and plan for mitigation measures. The market for image processing of aerial images for energy infrastructure analysis is also driven by government regulations and policies. Many countries have set ambitious targets for renewable energy generation and require regular monitoring and reporting of energy infrastructure performance. Aerial images and advanced image processing techniques play a crucial role in meeting these requirements and ensuring compliance. In terms of market players, there are several companies specializing in image processing for energy infrastructure analysis. These companies offer a range of services, including image selection, pre-processing, feature extraction, and data visualization. They work closely with energy operators, environmental consultants, and government agencies to provide accurate and actionable insights. In conclusion, the market for image processing of aerial images for energy infrastructure analysis by pre-processing the image selection is experiencing significant growth. The increasing adoption of renewable energy sources, advancements in image processing technologies, and the need for sustainable energy solutions are driving this market. As the energy sector continues to evolve, the demand for accurate and detailed analysis of energy infrastructure will only increase, making image processing a critical tool for energy operators and stakeholders.

The Sourcewater Inc invention works as follows

A computer-implemented technique for selecting aerial photos for image processing in order to identify Energy Infrastructure features (EIs) is presented. Image processing is performed on aerial images captured at different times of a global terrain to identify differences in the terrain content. The selected aerial images are then subjected to further image processing based on the differences identified in terrain content. “The selected images are imaged-processed via an EI feature identification type to identify EI elements within the images.

Background for Image processing of aerial images for energy infrastructure analysis by pre-processing the image selection

Infrastructure of different types is needed to harvest energy, whether it be from hydrocarbons, solar radiation, wind, or hydroelectric resources. The Energy Infrastructure Features (hereafter referred to as ‘Energy Infrastructure Features’) are the components that make up an infrastructure for a particular energy resource. Energy Infrastructure Features (also abbreviated as EI features) are often located close to one another within the same location or geographical region. For example, an oilfield site, a solar power station, a wind farm or a hydroelectric station. “EI features” is often abbreviated as?EI features?)

As an example, hydraulic fracture, or fracking is a specific process for the hydrocarbon industry in which a liquid hydraulic such as water, gel, or other fluid is injected under pressure into shale to create cracks or to expand them to allow the extraction of underground natural gas or oil. The use of this technique is growing rapidly.

Water is often used to recover, produce or release water as part of an operation. This water can be the return of injected water, or underground water released by the fracturing. The amount of returned water is often large and can, for instance, exceed the amount of oil extracted from the well.

The fracturing method requires large quantities of water to be sourced at the beginning of a project. It also demands that water is treated and recycled during or after the project. Transporting water from the source to the site or between sites can be expensive and reduce the margin of profit available during production. These costs can be reduced by selecting options for water treatment, disposal, and source that are located near the fracturing area or utilizing efficient infrastructure like pipeline networks.

In order to support this need for efficient management of water in the energy sector, tools that facilitate a dynamic, online platform can be used to enable buyers and sellers to exchange information about water availability or demand, as well as a variety of attributes, such as quantity, location, quality, and type.

This platform could be extended to include not only water resources associated with oilfield development and exploration, but also other services, infrastructure, and related resources.

The platform can be extended to other energy sources, including solar, wind, and hydroelectric. The platform could, for example, be used to inform users about the progress of a solar power station, windfarm or hydroelectric site. This would allow them to plan their commercial activities and take advantage of upcoming opportunities.

Accordingly, it is necessary to identify energy infrastructure features in a timely, efficient and reliable manner, and to determine the status of energy infrastructure sites.

A method for selecting aerial photos for image processing is described to identify Energy Infrastructure features (EI). The method involves retrieving a plurality first of aerial photos spanning a part of the global terrain. This first plurality is associated with an initial time of image capture. The method can include retrieving a secondary plurality aerial images covering the portion of global territory, the second number of aerials images being associated with a subsequent time of image capture. The method can identify one or multiple differences in terrain content based on a comparison between image content of at least one of the first plurality aerial images, and image content of at least one of the second plurality aerial images. The method can identify one or multiple geographical locations where the differences in terrain have been identified. It may then select aerial images from a first or second set of images or third group of images depending on these locations. The set of aerial photos is subjected to an EI feature recognition algorithm to identify at lease one EI characteristic.

The following detailed description will reveal other aspects and benefits of the invention, when taken together with the accompanying illustrations, which demonstrate the principles of invention by way of examples.

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