Robustness of Limited Training Data for Building Footprint Identification: Part 1

Daniel Hogan
The DownLinQ
Published in
5 min readJul 9, 2019

It’s a question that gets asked over and over again: How much data do I need to train my neural network? In this blog post, we will explore that question and answer it in the context of a specific case from the field of remote sensing imagery analysis. We’ll show that small amounts of data can perform surprisingly well. Subsequent blog posts will look at whether the answer is affected by geography and model architecture.

Motivation: The Utility of Limited Data

Daniel Hogan
The DownLinQ

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