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Electric utilities are struggling to manage increasing wildfire risk in ahotter and drier climate. Utility transmission and distribution lines regularlyignite destructive fires when they make contact with surrounding vegetation.Trimming vegetation to maintain the separation from utility assets is ascritical to safety as it is difficult. Each utility has tens of thousands oflinear miles to manage, poor knowledge of where those assets are located, andno way to prioritize trimming. Feature-enhanced convolutional neural networks(CNNs) have proven effective in this problem space. Histograms of orientedgradients (HOG) and Hough transforms are used to increase the salience of thelinear structures like power lines and poles. Data is frequently taken fromdrone or satellite footage, but Google Street View offers an even more scalableand lower cost solution. This paper uses $1,320$ images scraped from StreetView, transfer learning on popular CNNs, and feature engineering to placeimages in one of three classes: (1) no utility systems, (2) utility systemswith no overgrown vegetation, or (3) utility systems with overgrown vegetation.The CNN output thus yields a prioritized vegetation management system andcreates a geotagged map of utility assets as a byproduct. Test set accuracywith reached $80.15\%$ using VGG11 with a trained first layer and classifier,and a model ensemble correctly classified $88.88\%$ of images with riskyvegetation overgrowth.

Slash or burn: Power line and vegetation classification for wildfire prevention