The DownLinQ
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The DownLinQ

Robustness of Limited Training Data: Part 4

When it comes to the relationship between geospatial neural network performance and the amount of training data, do geographic differences matter? In a previous post, we examined this question by training the same building footprint model using various amounts of data from four different cities: Las Vegas, Paris, Shanghai, and Khartoum. That led to a plot (Figure 1) of performance for each city, either using a model trained on the city in question or using a model trained on the combined data of all four cities. In this post, we’ll take a closer look at two questions that went…




As of March 2021, CosmiQ Works has been folded into IQT Labs. An archive will remain here to showcase historical work from CosmiQ Works that took place July 2016 — March 2021.

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Daniel Hogan

Daniel Hogan

Daniel Hogan, PhD, is a data scientist at CosmiQ Works, an IQT Lab.

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