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Video: Using Convolutional Neural Networks to Automatically Analyze Aerial and Satellite Imagery
In this recording of our most recent Technical Staff Meeting, we walk through our team’s work on Raster Vision, a set of open source tools for automatically analyzing aerial and satellite imagery using convolutional neural networks. As part of Raster Vision, we have implemented approaches to tagging (predicts a set of tags for each image)…
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Video: Using Convolutional Neural Networks to Automatically Analyze Aerial and Satellite Imagery
In this recording of our most recent Technical Staff Meeting, we walk through our team’s work on Raster Vision, a set of open source tools for automatically analyzing aerial and satellite imagery using convolutional neural networks. As part of Raster Vision, we have implemented approaches to tagging (predicts a set of tags for each image)…
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Emerging Hot Spot Analysis: Finding Patterns over Space and Time
This post is part of a series of articles written by 2017 Summer of Maps Fellows. Azavea’s Summer of Maps Fellowship Program provides impactful pro bono spatial analysis for nonprofits, while fellows benefit from Azavea mentors’ expertise. To see more blog posts about Summer of Maps, click here. Analyzing data over space and time can…
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Sankey Diagrams: Six Tools for Visualizing Flow Data
This post is part of a series of articles written by 2017 Summer of Maps Fellows. Azavea’s Summer of Maps Fellowship Program provides impactful pro bono spatial analysis for nonprofits, while fellows benefit from Azavea mentors’ expertise. To see more blog posts about Summer of Maps, click here. One of the non-profits I am working…
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How to Calculate Location Accessibility with AccessMod 5
This post is part of a series of articles written by 2017 Summer of Maps Fellows. Azavea’s Summer of Maps Fellowship Program is run by the Data Analytics Team and provides impactful Data Analysis Services Grants for nonprofits and mentorship to fellows. To see more blog posts about Summer of Maps, click here. Mapping location…
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The Road Ahead for DistrictBuilder and Public Redistricting
Seven years ago Azavea began working with the Public Mapping Project to create DistrictBuilder, an open source, web software tool that would enable people to draw their own legislative district plans. We worked with leading redistricting experts: Michael McDonald (Associate Professor at the University of Florida and head of the Elections Project) and Micah Altman…
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Leveraging Node.js, D3.js, and HTML Canvas for Scalable Infographics
In Part 3 of our 3-part series on Azavea’s redistricting and gerrymandering work, we look at the technical process behind developing infographics for efficiency gap reports. We made strategic use of JavaScript visual tools and Node.js as a scripting language in our design and production of 50 infographics about the efficiency gap, automatically creating one for…
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The Most Gerrymandered States Ranked by Efficiency Gap and Seat Advantage
Learn about our methodology for ranking the efficiency gap for each Congressional delegation in all 50 states and visualize the results.
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The Evolution of Our Redistricting and Gerrymandering Work
In part 1 of our series on Azavea’s redistricting and gerrymandering work, we look at the history of our involvement in this space and what we’re currently working on. Gerrymandering, compactness, contiguity, the efficiency gap. These are the kinds of words that make a lot of redistricting nerds excited these days. Rarely does the application…
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Reading and Visualizing NetCDF Climate Data with GeoTrellis
This blog post contains an example project that demonstrates how to read NetCDF climate projection data from S3 or a local filesystem into a Spark/Scala program using NetCDF Java and how to manipulate the data using GeoTrellis. We are interested in reading datasets stored as NetCDF because it is a common format for storing large, global climate…
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Deep Learning for Semantic Segmentation of Aerial and Satellite Imagery
This post describes how to use deep learning to do semantic segmentation on aerial and satellite imagery, experiments on the ISPRS Potsdam dataset, and how to visualize model predictions on a map.
