Identify Solar Panel Locations with Computer Vision

James Gallagher's blog post discusses using Roboflow Workflows to create a computer vision application that identifies and classifies solar panels in aerial imagery. The system begins by detecting solar panels using a fine-tuned model for aerial images, applies a detection offset for environmental context, and visualizes predictions through bounding box visualizations. The process employs a multimodal classification model, such as GPT-4 with Vision, to determine if solar panels are on the ground or a roof, returning results in a structured JSON format. This low-code application can be tested and deployed on various platforms, including Roboflow Cloud and edge devices, facilitating the development of complex computer vision applications without extensive coding.