# Boundary Loss for Remote Sensing Imagery Semantic Segmentation

## TL;DR

This paper introduces a new boundary-aware loss function for remote sensing image segmentation, improving boundary accuracy and overall segmentation quality in neural network models.

## Contribution

The paper proposes a novel differentiable boundary loss function that enhances boundary delineation in remote sensing image segmentation tasks.

## Key findings

- Models trained with the new loss outperform baselines in IoU scores.
- The loss improves boundary accuracy in synthetic and real-world datasets.
- Enhanced boundary detection leads to better segmentation quality.

## Abstract

In response to the growing importance of geospatial data, its analysis including semantic segmentation becomes an increasingly popular task in computer vision today. Convolutional neural networks are powerful visual models that yield hierarchies of features and practitioners widely use them to process remote sensing data. When performing remote sensing image segmentation, multiple instances of one class with precisely defined boundaries are often the case, and it is crucial to extract those boundaries accurately. The accuracy of segments boundaries delineation influences the quality of the whole segmented areas explicitly. However, widely-used segmentation loss functions such as BCE, IoU loss or Dice loss do not penalize misalignment of boundaries sufficiently. In this paper, we propose a novel loss function, namely a differentiable surrogate of a metric accounting accuracy of boundary detection. We can use the loss function with any neural network for binary segmentation. We performed validation of our loss function with various modifications of UNet on a synthetic dataset, as well as using real-world data (ISPRS Potsdam, INRIA AIL). Trained with the proposed loss function, models outperform baseline methods in terms of IoU score.