Most deep-learning-based object detection approaches today repurpose image classifiers by applying them to a sliding window across an input image. DNNs address almost all of the aforementioned challenges as they have the capacity to learn more complex representations of objects in images than shallow networks and eliminate the reliance on hand-engineered features. The proliferation of powerful GPUs and availability of large datasets have made training deep neural networks practical for object detection.
Historically, object detection systems depended on feature-based techniques which included creating manually engineered features for each region, and then training of a shallow classifier such as SVM or logistic regression. Real-world images can contain a few instances of objects or a very large number, this can have an effect on the accuracy and computational efficiency of an object detection system. A good object detection system has to be robust to the presence (or absence) of objects in arbitrary scenes, be invariant to object scale, viewpoint, and orientation, and be able to detect partially occluded objects. We will discuss how human-level accuracy can be achieved in vector data collection from commercial imagery at a far greater speed. Machines are now able to learn at a speed, accuracy, and scale that are driving true artificial intelligence and Artificial Intelligence Computing. Today’s advanced deep neural networks (DNN) use algorithms, big data, and the computational power of the GPU to change this dynamic. Object detection is a particularly challenging task in computer vision. Often times, this feature extraction work is performed by GIS analysts whose time would be better spent performing analysis and producing actionable reports for decision makers, rather than collecting data. Building footprint extraction from imagery provides an even more complex challenge due to shadows, tree overhang, and the complexity of roofs.
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Digitizing features from imagery or scanned maps is a manual process that is costly, requiring significant human resources to accomplish.
Vector data collection is the most tedious task in a GIS workflow.
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