# Boundary Loss for Remote Sensing Imagery Semantic Segmentation

arXiv: https://arxiv.org/abs/1905.07852 · HTML: https://mlsanity.com/paper/1905.07852/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2019.

## Built on by (most cited first, 27 of 27)

- [Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions](https://mlsanity.com/paper/2103.08852/) (2021)
- [SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints](https://mlsanity.com/paper/2312.02464/) (2023)
- [Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation](https://mlsanity.com/paper/2202.13377/) (2022)
- [Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art](https://mlsanity.com/paper/2111.03260/) (2021)
- [MaskRange: A Mask-classification Model for Range-view based LiDAR Segmentation](https://mlsanity.com/paper/2206.12073/) (2022)
- [Panoptic Segmentation Meets Remote Sensing](https://mlsanity.com/paper/2111.12126/) (2021)
- [TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation](https://mlsanity.com/paper/2309.07849/) (2023)
- [Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network](https://mlsanity.com/paper/2307.12309/) (2023)
- [Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation](https://mlsanity.com/paper/2306.11734/) (2023)
- [FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2208.00400/) (2022)
- [FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation](https://mlsanity.com/paper/2310.13820/) (2023)
- [Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots](https://mlsanity.com/paper/2403.14056/) (2024)
- [Rethinking Boundary Detection in Deep Learning-Based Medical Image Segmentation](https://mlsanity.com/paper/2505.04652/) (2025)
- [RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images](https://mlsanity.com/paper/2507.11143/) (2025)
- [Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy](https://mlsanity.com/paper/2104.01268/) (2021)
- [KD-SCFNet: Towards More Accurate and Efficient Salient Object Detection via Knowledge Distillation](https://mlsanity.com/paper/2208.02178/) (2022)
- [Boundary Aware U-Net for Glacier Segmentation](https://mlsanity.com/paper/2301.11454/) (2023)
- [MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement](https://mlsanity.com/paper/2309.13817/) (2023)
- [Steerable Pyramid Weighted Loss: Multi-Scale Adaptive Weighting for Semantic Segmentation](https://mlsanity.com/paper/2503.06604/) (2025)
- [HARP-NeXt: High-Speed and Accurate Range-Point Fusion Network for 3D LiDAR Semantic Segmentation](https://mlsanity.com/paper/2510.06876/) (2025)
- [Global and Local Features through Gaussian Mixture Models on Image Semantic Segmentation](https://mlsanity.com/paper/2207.09162/) (2022)
- [Multistep feature aggregation framework for salient object detection](https://mlsanity.com/paper/2211.06697/) (2022)
- [DBAT: Dynamic Backward Attention Transformer for Material Segmentation with Cross-Resolution Patches](https://mlsanity.com/paper/2305.03919/) (2023)
- [Revising deep learning methods in parking lot occupancy detection](https://mlsanity.com/paper/2306.04288/) (2023)
- [LC-SLab -- An object-based deep learning framework for large-scale land cover classification from satellite imagery and sparse in-situ labels](https://mlsanity.com/paper/2509.15868/) (2025)
- [Deep learning for echo sounder data](https://mlsanity.com/paper/2606.10811/) (2026)
- [Perturbation-Aware Diffusion-Guided Hybrid Segmentation for Robust and Annotation-Efficient Plant Stress Phenotyping](https://mlsanity.com/paper/2607.23680/) (2026)

## Abstract (arXiv)

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.