Abstract: Example systems, methods, and non-transitory computer readable media are directed to obtaining a first image and a second image, wherein the second image is an image to be evaluated with respect to the first image for alignment; generating tiles for the first image and the second image; measuring one or more respective alignment offsets between a plurality of tiles associated with the second image and corresponding tiles associated with the first image; classifying the plurality of tiles as aligned or misaligned based on the respective alignment offsets measured; determining that the classified plurality of tiles associated with the second image satisfy a tile threshold; and co-registering the second image with the first image based at least in part on the determination that the classified plurality of tiles associated with the second image satisfy the tile threshold.

Abstract: Example systems, methods, and non-transitory computer readable media are directed to determining a geographic location to be assessed; obtaining information associated with the geographic location, the information including at least one data point of the geographic location; classifying one or more habitats within the geographic location based on at least one machine learning model that processes the at least one data point of the geographic location; determining at least one respective metric for the one or more classified habitats based at least in part on the at least one data point of the geographic location; and providing an interface that includes at least a map of the geographic location and the at least one respective metric for the one or more classified habitats, the one or more classified habitats are visually segmented in the map by habitat type.

Abstract: Example systems, methods, and non-transitory computer readable media are directed to determining a geographic location to be assessed; obtaining information associated with the geographic location, the information including at least one data point of the geographic location; classifying one or more habitats within the geographic location based on at least one machine learning model that processes the at least one data point of the geographic location; determining at least one respective metric for the one or more classified habitats based at least in part on the at least one data point of the geographic location; and providing an interface that includes at least a map of the geographic location and the at least one respective metric for the one or more classified habitats, the one or more classified habitats are visually segmented in the map by habitat type.

Abstract: An example method includes receiving georeferenced satellite images of a geographic region that includes electrical assets. Segmentation maps are generated by providing the georeferenced satellite images to fully convolutional networks to classify pixels of the georeferenced satellite images as either trees or non-trees. Rasters of the geographic region are generated based on the segmentation maps and vectors are generated based on the rasters. A vector includes one or more polygons, a polygon representing a tree and having a set of coordinates defining the polygon. Canopy height models are generated based on received digital surface models. The canopy height models include heights of trees in the geographic region. Heights of trees are associated with polygons. A height of a polygon is compared to a distance between the polygon and an electrical asset to identify a tree as a potential hazard. Notifications thereof are provided.

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: Example systems, methods, and non-transitory computer readable media are directed to determining a geographic location to be assessed; obtaining information associated with the geographic location, the information including at least one data point of the geographic location; classifying one or more habitats within the geographic location based on at least one machine learning model that processes the at least one data point of the geographic location; determining at least one respective metric for the one or more classified habitats based at least in part on the at least one data point of the geographic location; and providing an interface that includes at least a map of the geographic location and the at least one respective metric for the one or more classified habitats, the one or more classified habitats are visually segmented in the map by habitat type.

Abstract: Example systems, methods, and non-transitory computer readable media are directed to obtaining a first image and a second image, wherein the second image is an image to be evaluated with respect to the first image for alignment; generating tiles for the first image and the second image; measuring one or more respective alignment offsets between a plurality of tiles associated with the second image and corresponding tiles associated with the first image; classifying the plurality of tiles as aligned or misaligned based on the respective alignment offsets measured; determining that the classified plurality of tiles associated with the second image satisfy a tile threshold; and co-registering the second image with the first image based at least in part on the determination that the classified plurality of tiles associated with the second image satisfy the tile threshold.

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: Example systems, methods, and non-transitory computer readable media are directed to determining a geographic location to be assessed; obtaining information associated with the geographic location, the information including at least one data point of the geographic location; classifying one or more habitats within the geographic location based on at least one machine learning model that processes the at least one data point of the geographic location; determining at least one respective metric for the one or more classified habitats based at least in part on the at least one data point of the geographic location; and providing an interface that includes at least a map of the geographic location and the at least one respective metric for the one or more classified habitats, the one or more classified habitats are visually segmented in the map by habitat type.