Abstract
Abstract. Deep learning (DL) methods are used for identifying objects in aerial and ground-based images. Detecting vehicles, roads, buildings, and crops are examples of object identification applications using DL methods. Identifying complex natural and man-made features continues to be a challenge. Oil pads are an example of complex built features due to their shape, size, and presence of other structures like sheds. This work applies Faster Region-based Convolutional Neural Network (R-CNN), a DL-based object recognition method, for identifying oil pads in high spatial resolution (1m), true-color aerial images. Faster R-CNN is a region-based object identification method, consisting of Regional Proposal Network (RPN) that helps to find the area where the target can be possibly present in the images. If the target is present in the images, the Faster R-CNN algorithm will identify the area in an image as foreground and the rest as background. The algorithm was trained with oil pad locations that were manually annotated from orthorectified imagery acquired in 2017. Eighty percent of the annotated images were used for training and the number of epochs was increased from 100 to 1000 in increments of 100 with a fixed length of 1000. After determining the optimal number of epochs the performance of the algorithm was evaluated with an independent set of validation images consisting of frames with and without oil pads. Results indicate that the Faster R-CNN algorithm can be used for identifying oil pads in aerial images.
Highlights
Recent advances in Artificial Intelligence (AI) techniques have led to relatively accurate and rapid detection of features or objects in aerial photographs
Faster Region-based Convolutional Neural Network (R-CNN) achieved an accuracy of 94.9% and recall of 95.4% while identifying self-blast glass insulator’s location in aerial images (Ling et al, 2019)
In aerial images, it is very tough to manually differentiate the storing buildings, parking areas, oil pads containing more than one oil mining unit with oil pads containing single mining units
Summary
Recent advances in Artificial Intelligence (AI) techniques have led to relatively accurate and rapid detection of features or objects in aerial photographs. Oil pads are examples of complex features in aerial images. The area of land is cleared for a drilling unit and there is an access road connecting the oil pad to the main road. Xia et al, (2018) used Faster R-CNN to identify 15 objects in 2806 images and achieved an average accuracy of 60.5%. Faster R-CNN was used to identify objects in complex battlefield environments and achieved an average accuracy of 94.2% (Xu et al, 2020). Faster R-CNN achieved an accuracy of 94.9% and recall of 95.4% while identifying self-blast glass insulator’s location in aerial images (Ling et al, 2019). Ho et al, 2019 successfully identified watermelons present with other complex objects in aerial images with Faster R-CNN. We tested Faster Faster R-CNN to identify oil pads in aerial images
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