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Abstract:
We'll discuss our work at Esri to reconstruct 3D building models from aerial LiDAR data with the help of deep neural networks. The value of accurate 3D building models for cities is hard to overestimate, but collecting and maintaining this data is labor-intensive, error-prone, and expensive. We teamed up with Miami-Dade County and NVIDIA to see if we could streamline this data-acquisition workflow or at least, make it more cost-effective. We used a Mask R-CNN model trained to detect and report instances of roof segments of various types. Our talk will cover data preparation and Mask R-CNN training and achieved precision. We'll also outline the inference architecture, the integration of TensorFlow and ArcGIS Pro 2.3, and the steps we used to reconstruct 3D building models from the predictions.
We'll discuss our work at Esri to reconstruct 3D building models from aerial LiDAR data with the help of deep neural networks. The value of accurate 3D building models for cities is hard to overestimate, but collecting and maintaining this data is labor-intensive, error-prone, and expensive. We teamed up with Miami-Dade County and NVIDIA to see if we could streamline this data-acquisition workflow or at least, make it more cost-effective. We used a Mask R-CNN model trained to detect and report instances of roof segments of various types. Our talk will cover data preparation and Mask R-CNN training and achieved precision. We'll also outline the inference architecture, the integration of TensorFlow and ArcGIS Pro 2.3, and the steps we used to reconstruct 3D building models from the predictions.  Back
 
Topics:
AI Application Deployment and Inference, Seismic and Geosciences, Product & Building Design
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9255
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Abstract:
During this presentation we will review a deep neural network architecture and its training approaches used for producing high volume of estimations of travel times on a road graph with historical routes and traffic. This includes initial and continuous online training, finding various sources to produce training data, challenges of quality control, and, of course, the invaluable role of GPU's for computation during both training and inference.
During this presentation we will review a deep neural network architecture and its training approaches used for producing high volume of estimations of travel times on a road graph with historical routes and traffic. This includes initial and continuous online training, finding various sources to produce training data, challenges of quality control, and, of course, the invaluable role of GPU's for computation during both training and inference.  Back
 
Topics:
AI and DL Research, Product & Building Design, Intelligent Video Analytics and Smart Cities, GIS, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8156
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