The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation

by   Fanjie Kong, et al.

Recently deep learning - namely convolutional neural networks (CNNs) - have yielded impressive performance for the task of building segmentation on large overhead (e.g., satellite) imagery benchmarks. However, these benchmark datasets only capture a small fraction of the variability present in real-world overhead imagery, limiting the ability to properly train, or evaluate, models for real-world application. Unfortunately, developing a dataset that captures even a small fraction of real-world variability is typically infeasible due to the cost of imagery, and manual pixel-wise labeling of the imagery. In this work we develop an approach to rapidly and cheaply generate large and diverse virtual environments from which we can capture synthetic overhead imagery for training segmentation CNNs. Using this approach, generate and publicly-release a collection of synthetic overhead imagery - termed Synthinel-1 with full pixel-wise building labels. We use several benchmark dataset to demonstrate that Synthinel-1 is consistently beneficial when used to augment real-world training imagery, especially when CNNs are tested on novel geographic locations or conditions.


page 1

page 4

page 6


SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems

Recently deep neural networks (DNNs) have achieved tremendous success fo...

Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery

Modern deep neural networks (DNNs) achieve highly accurate results for m...

Estimating Displaced Populations from Overhead

We introduce a deep learning approach to perform fine-grained population...

Lane Boundary Geometry Extraction from Satellite Imagery

Autonomous driving car is becoming more of a reality, as a key component...

GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery

Energy system information valuable for electricity access planning such ...

Meta-simulation for the Automated Design of Synthetic Overhead Imagery

The use of synthetic (or simulated) data for training machine learning m...

What Goes Where: Predicting Object Distributions from Above

In this work, we propose a cross-view learning approach, in which images...

Code Repositories


Repository for Synthinel dataset. It is presented in WACV 2020

view repo