Crowdsourcing Solar Farms Using Satellite Images

satellite image of solar farm in desert has been crowdsourcing solar roofs, solar parking, and solar farms for several years.  We do this manually by zooming down to appropriate levels to view images.  Solar farms, solar roofs, and solar parking structures are easy to find as long as satellite images are updated.  We ask our users to put a yellow pin on our map and submit a link to a news article about the solar location.  

Crowdsourcing solar farms using satellite images is an innovative approach that leverages the power of collective intelligence to identify and map solar installations around the world. Here's how it works:

Satellite Imagery: High-resolution satellite images are collected from various sources, including commercial satellite providers and publicly available satellite imagery archives. These images capture a wide area and provide detailed views of the Earth's surface.

Image Analysis: The satellite images are processed using advanced image analysis techniques, including machine learning algorithms and computer vision, to identify potential solar farm locations. These algorithms can detect patterns, shapes, and characteristics associated with solar panels and installations.

Crowdsourcing Platform: A crowdsourcing platform like or an application is created where volunteers or users can access the processed satellite images. The platform provides tools and guidelines for users to annotate or mark areas where they identify solar farms or solar panel arrays.

User Contributions: Users, often referred to as "citizen scientists," contribute their time and effort to review satellite images and mark potential solar installations on the crowdsourcing platform. They may manually draw polygons or use other interactive tools to highlight solar farm boundaries or put POI pins in the map.

Data Validation and Quality Assurance: To ensure the accuracy of the crowdsourced data, multiple users review and validate each marked solar farm. Algorithms can be implemented to compare and reconcile different user annotations, identifying areas of agreement and resolving discrepancies.

Data Integration and Analysis: Once the crowdsourced data is validated, it can be aggregated and integrated into a comprehensive database or map of solar farms. This data can be analyzed to gain insights into the distribution, capacity, and characteristics of solar installations worldwide.

Benefits of Crowdsourcing Solar Farms Using Satellite Images:

Scale and Efficiency: Crowdsourcing allows for a large number of satellite images to be analyzed simultaneously, enabling the identification of solar farms at a much larger scale and faster pace compared to manual analysis.

Cost-Effectiveness: Leveraging the power of citizen scientists reduces the costs associated with manual labor or specialized expertise required for image analysis.

Global Coverage: Crowdsourcing can engage volunteers from around the world, leading to the identification of solar farms in remote or inaccessible areas where traditional data collection methods may be challenging.

Data Accuracy and Validation: By involving multiple users in the annotation process and implementing validation mechanisms, the quality and accuracy of the identified solar farms can be improved.

Community Engagement: Crowdsourcing solar farms encourages public participation and citizen engagement in renewable energy initiatives, raising awareness about the importance of solar energy and sustainability.

While crowdsourcing solar farms using satellite images is a promising approach, it's important to note that the accuracy and reliability of the data depend on the quality of satellite imagery, the effectiveness of image analysis algorithms, and the level of user participation and validation. Therefore, ongoing monitoring, verification, and refinement of the crowdsourced data are necessary to maintain its accuracy and usefulness.

The future of these maps will depend on technology and using AI or artificial intelligence to train mapping software to find solar energy locations.  See the link to review an example provided by