The SpaceNet 6 Baseline

Preface: SpaceNet LLC is a nonprofit organization dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping (i.e. building footprint & road network detection). SpaceNet is run in collaboration with CosmiQ Works, Maxar Technologies, Intel AI, Amazon Web Services (AWS), Capella Space, Topcoder, and IEEE GRSS.

Introduction

The SpaceNet 6 Challenge is the first multimodal SpaceNet challenge, inviting participants to work with optical imagery as well as synthetic aperture radar imagery to design a foundational mapping algorithm. This post describes a newly-released baseline algorithm, which could be used as an example or starting point for SpaceNet 6 participants.

The goal of the challenge is to produce an algorithm that takes a synthetic aperture radar (SAR) image and returns a list of building footprints (i.e., outlines), expressed as vector polygons instead of just pixel maps. To enable supervised learning methods such as deep learning, training data has been provided. The training data includes tiles of SAR imagery, along with the corresponding building footprints for each tile. The training data also includes optical imagery of those same tiles. Although it’s possible to train a SAR→footprint algorithm without using the optical data at all, having it available builds intuition and opens up additional possibilities for how to approach this problem.

SpaceNet 6 baseline example. Top row shows optical and SAR views of the same tile. This tile includes the edge of a SAR strip, which is why the imagery is cut off instead of being a perfect square. The bottom left image is inferred building footprints based on only the SAR image. (The model gets to see optical imagery during training but not inference.) The bottom right image is the ground truth labels.

The baseline algorithm presented here is built using Solaris, an end-to-end Python framework for geospatial deep learning. The code for this baseline can be found on CosmiQ’s Github under “CosmiQ_SN6_Baseline.” In some ways this baseline draws on the baseline and winning submission from SpaceNet 4. However, it also has features that are specific to the new dataset. SAR imagery is affected by the direction from which the data was collected (through effects such as layover). Therefore, the baseline rotates tiles so the model is trained on SAR images of matching directionality. Also, the baseline takes advantage of the optical imagery through transfer learning. Training the model on the optical imagery first…