Aurora-CCPM is being utilized by Boeing in the Final Assembly of the B787 Dreamliner. Aurora-CCPM provides Boeing with Critical Chain capabilities not available in other Critical Chain software. Aurora-CCPM prioritizes factory production tasks by balancing resource capacities with manufacturing requirements and constraints via the Critical Chain method of buffer management. The result is a dynamic assembly schedule that adapts to real-time production variability and allows Boeing to execute the plan as efficiently as possible. Video showing production & painting of B787.
The original project was written in tensorflow based on keras-retinanet. When tensorflow updated to 2.0 there were breaking changes and the authors of keras-retinanet decided to not attempt to recover the project.The rapid development of open machine learning community resources means that tensorflow 1.14.0, which is required for deepforest, will rapidly become out of date. Fear not, starting in 1.0.0, the model now depends on the pytorch and torchvision and will be stable for the forseeable future.
Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. Individual crown delineation has been a long-standing challenge in remote sensing and available algorithms produce mixed results. DeepForest is the first open source implementation of a deep learning model for crown detection. Deep learning has made enormous strides in a range of computer vision tasks but requires significant amounts of training data. By including a trained model, we hope to simplify the process of retraining deep learning models for a range of forests, sensors, and spatial resolutions.
This capability of software is the source for many specific dilemmas around the pros and cons of every policy. The policy and its ramifications would determine whether the software, forcing that policy, is a blessing or a curse. 2b1af7f3a8