Sage

We propose incorporating both spatial and temporal information via a combined CNN-LSTM classification model. We theorize that the inclusion of temporal information may reduce the number of false positives and improve generalizability to new environments. The model is trained and tested on images of landscapes with and without smoke from the HPWREN tower network in southern California, part of the SAGE remote-sensing infrastructure. We use traditional CNN-based classifiers leveraged in past smoke detection literature as baselines to evaluate our model's performance.

Influenced by the advances in data and computing, the scientific practice increasingly involves machine learning and artificial intelligence driven methods which requires specialized capabilities at the system-, science- and service-level in addition to the conventional large-capacity supercomputing approaches. The latest distributed architectures built around the composability of data-centric applications led to the emergence of a new ecosystem for container coordination and integration.