# Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation

arXiv: https://arxiv.org/abs/2402.16164 · HTML: https://mlsanity.com/paper/2402.16164/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 25 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)
- [Learning Aerial Image Segmentation from Online Maps](https://mlsanity.com/paper/1707.06879/) (2017)
- [AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning](https://mlsanity.com/paper/2202.00067/) (2022)
- [Map Generation from Large Scale Incomplete and Inaccurate Data Labels](https://mlsanity.com/paper/2005.10053/) (2020)

## Built on by (most cited first, 4 of 4)

- [CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation](https://mlsanity.com/paper/2405.01217/) (2024)
- [Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images](https://mlsanity.com/paper/2505.01884/) (2025)
- [SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation](https://mlsanity.com/paper/2503.12404/) (2025)
- [LandSegmenter: Towards a Flexible Foundation Model for Land Use and Land Cover Mapping](https://mlsanity.com/paper/2511.08156/) (2025)

## Abstract (arXiv)

Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. While image-level information for unsupervised pretraining efficiently works for various classification downstream tasks, the performance on pixel-level semantic segmentation lags behind in terms of model accuracy. On the contrary, many easily available label sources (e.g., automatic labeling tools and land cover land use products) exist, which can provide a large amount of noisy labels for segmentation model training. In this work, we propose to exploit noisy semantic segmentation maps for model pretraining. Our experiments provide insights on robustness per network layer. The transfer learning settings test the cases when the pretrained encoders are fine-tuned for different label classes and decoders. The results from two datasets indicate the effectiveness of task-specific supervised pretraining with noisy labels. Our findings pave new avenues to improved model accuracy and novel pretraining strategies for efficient remote sensing image segmentation.