Abstract: A method comprising receiving a first set of aerial images of a geographic area, receiving locations of assets, determining a likely location of at least one asset within each image, creating one or more bounding boxes, encompassing the likely location of one or more assets within each of the images, providing any number of the images to a convolutional neural network to classify pixels, the classification of each of the pixels indicating if the pixels are part of one or more obstructions or are part of a different classification, determining at least one zone, the zone encompassing the at least one asset, determining a distance between at least one pixel part of an obstruction and the zone, generating a criticality score based on the distance, comparing the criticality score to a threshold, and providing an alert of a future hazardous condition based on the criticality score.

Abstract: A method comprising receiving a first set of aerial images of a geographic area, receiving locations of assets, determining a likely location of at least one asset within each image, creating one or more bounding boxes, encompassing the likely location of one or more assets within each of the images, providing any number of the images to a convolutional neural network to classify pixels, the classification of each of the pixels indicating if the pixels are part of one or more obstructions or are part of a different classification, determining at least one zone, the zone encompassing the at least one asset, determining a distance between at least one pixel part of an obstruction and the zone, generating a criticality score based on the distance, comparing the criticality score to a threshold, and providing an alert of a future hazardous condition based on the criticality score.

Abstract: A method comprising receiving a first set of aerial images of a geographic area, receiving locations of assets, determining a likely location of at least one asset within each image, creating one or more bounding boxes, encompassing the likely location of one or more assets within each of the images, providing any number of the images to a convolutional neural network to classify pixels, the classification of each of the pixels indicating if the pixels are part of one or more obstructions or are part of a different classification, determining at least one zone, the zone encompassing the at least one asset, determining a distance between at least one pixel part of an obstruction and the zone, generating a criticality score based on the distance, comparing the criticality score to a threshold, and providing an alert of a future hazardous condition based on the criticality score.