Raster Vision Archives | Page 2 of 6

Screenshot of mapped building footprints in Cambodia.

Building Footprint Comparison in Cambodia: Orbital Insight and OpenStreetMap

By Ross Bernet and Andrew Fink-Miller on January 23rd, 2020

We created a map that shows the difference between two building footprint datasets of Cambodia: OSM and Orbital Insights' AI generated dataset.

Common vector of disease overlaid by maps.

Company

How Geospatial Technology and AI are Transforming Medicine

By Niki LaGrone on January 17th, 2020

From predicting outbreaks of infectious disease to predicting the likelihood of an asthma attack, Geospatial AI and medicine are transforming healthcare.

Ground truth and model prediction outlines overlaying the original geospatial chip.

Company

Join the OpenCities AI Challenge and Detect Building Footprints from Aerial Imagery

By Simon Kassel on January 16th, 2020

We partnered with Driven Data and the World Bank to develop the Open Cities AI Challenge. This challenge uses machine learning to extract building footprints in unmapped areas to promote disaster resilience.

Company

How to Find the Most Recent Satellite Imagery Anywhere on Earth

By Joe Morrison on January 2nd, 2020

Every project with satellite imagery begins with a data source. Here's a rundown of the best options, considering cost, usability, and resolution.

Slider for testing seams of labeled machine learning data.

Software Engineering

3 Ways to Analyze the Results of a Supervised Machine Learning Model

By Joe Morrison on December 13th, 2019

How accurate are our supervised machine learning models and what are they really doing? We offer 3 tips to help you better understand these models.

Azavea team team leading GIS training in Guyana.

Software Engineering

Ethical Machine Learning for Disaster Relief: Rage for Machine Learning

By Niki LaGrone on December 10th, 2019

ML's predictive powers are driving a rage for deep learning in the crisis management and disaster relief industries. How can these powers be harnessed for ethical machine learning?