IOT BASED SOLAR ENERGY DETECTION WITH CRESCENT DUNES

Abstract High quality information about the quantity, power capacity, and energy generated by PV arrays, including at a high spatial resolution is desired. Surveys and utility interconnection filings are limited in their completeness and spatial resolution. Thus is proposed a computer algorithm that automatically detects IOT solar PV arrays in high resolution colour (RGB) imagery data. Algorithm developed and validated on a very large collection of aerial imagery using dunes. Human annotators manually scanned and annotated IOT solar PV locations to provide ground truth for evaluating performance. Performance measured in a pixel-based and object-based manner using PR curves. Most of the true PV pixels detected while removing the vast majority of the non-PV pixels. Color Image (3 Channels) ~-I(1)Feature1 )etrtion j Feature Image Extraction (M Channels) (2)Random Confidence Map Forest (1 Channel) Classifier i( 3) Post- Enhanced processing (1 Channel) Object map (list of detected objects) 1 - ~jObject I i mm Detection Fig. A flowchart of the PV detection algorithm Color Image Featurevector (3 Channel) at location po Fig Illustration of pixel-based feature extraction at a single pixel location, po.