Element 84 has developed near real-time edge processing of drone and aerial imagery for human identification that leverages machine learning and AWS Snowcone edge capabilities during austere operations for search and rescue applications.
We outline recent projects tackling complex challenges through the lens of Machine Learning and discuss how our past experience will shape future work.
Reviewing model architectures for building footprint extraction including naive approaches, model improvement strategies, and recent research.
In the second part of our Automated Building Footprint Extraction series, we review some evaluation metrics for building footprint extraction.
In the first installment of this three-part blog series, we summarize some of the latest research on automated building footprint extraction.
In this blog we demonstrate how an active learning approach can boost machine learning model performance with the human-in-the-loop workflow.
Benchmarking Zarr and Parquet Data Retrieval using the National Water Model (NWM) in a Cloud-native environment
In order to benchmark efficiency, we take a deep dive into Zarr and Parquet data retrieval to compare performance on various time scales.
As one of seven pilot programs to address environmental issues in Africa, Azavea trained student workers to label satellite imagery using GroundWork and created a machine learning model to identify tree canopy.