GTC Silicon Valley-2019: Reconstruction of 3D Building Models from Aerial LiDAR with AI
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GTC Silicon Valley-2019 ID:S9255:Reconstruction of 3D Building Models from Aerial LiDAR with AI
Dmitry Kudinov(Esri)
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.