Introduction

The purpose of this post is to explain how the data was annotated. We hope that this would help you better understand the problem and give you insights for your solutions.

Problem Description

We have a set of GPS coordinates corresponding to the centers of maize fields. There were some issues in the data collection process which sometimes resulted in GPS coordinates that do not precisely coincide with their correct maize field centers. The errors in the GPS coordinates are due to recording on the edges of the field rather than the center, or in the house which owns the farm, or under the shade of a nearby tree, or even on the main road leading to the farm.

The problem we are trying to solve is to correctly identify the original field centers given the GPS coordinates.

Annotation Process

The GPS coordinates are projected on the map as shown in the figures below. Two target icons are drawn on the map; black indicates original position and red indicates corrected position. The area of these icons is calculated from the “plot size” variable but may not exactly match its size. Note that the icons are always circular but the field itself may be an elongated rectangular stretch or even triangular. Also, the corrected (red) field location may be somewhat far from the original (black) location.

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Challenges

The figures above demonstrate some of the challenges in this problem.

The image resolution in the competition data is lower than these figures, making it harder to identify the fields. We couldn’t use the same high resolution images due to licensing issues.

Furthermore, sometimes there are multiple possible candidate target fields and the annotator has to make a best guess based on size and proximity of the field. This is captured by the “Quality” variable in the data.

Moreover, some field workers record multiple data points in the same spot making plot size the only distinguishing variable.