# CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation

arXiv: https://arxiv.org/abs/2501.15389 · HTML: https://mlsanity.com/paper/2501.15389/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 57 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching](https://mlsanity.com/paper/2410.12261/) (2024)
- [TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting](https://mlsanity.com/paper/2410.11802/) (2024)
- [AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generation](https://mlsanity.com/paper/2411.15497/) (2024)
- [EasyTime: Time Series Forecasting Made Easy](https://mlsanity.com/paper/2412.17603/) (2024)
- [AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation](https://mlsanity.com/paper/2306.06842/) (2023)
- [Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark](https://mlsanity.com/paper/2112.00570/) (2021)
- [MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast](https://mlsanity.com/paper/2410.15997/) (2024)
- [CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term Renewable Energy Forecasting](https://mlsanity.com/paper/2501.08620/) (2025)
- [Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering](https://mlsanity.com/paper/2312.15447/) (2023)
- [AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation](https://mlsanity.com/paper/2409.02580/) (2024)
- [AirShot: Efficient Few-Shot Detection for Autonomous Exploration](https://mlsanity.com/paper/2404.05069/) (2024)
- [Bird Distribution Modelling using Remote Sensing and Citizen Science data](https://mlsanity.com/paper/2305.01079/) (2023)
- [DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs](https://mlsanity.com/paper/2204.06454/) (2022)
- [AbHE: All Attention-based Homography Estimation](https://mlsanity.com/paper/2212.03029/) (2022)
- [RapVerse: Coherent Vocals and Whole-Body Motions Generations from Text](https://mlsanity.com/paper/2405.20336/) (2024)
- [CPDR: Towards Highly-Efficient Salient Object Detection via Crossed Post-decoder Refinement](https://mlsanity.com/paper/2501.06441/) (2025)
- [Stereo Matching Based on Visual Sensitive Information](https://mlsanity.com/paper/2105.10831/) (2021)
- [Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space](https://mlsanity.com/paper/2108.13902/) (2021)
- [Real-Time Localization and Bimodal Point Pattern Analysis of Palms Using UAV Imagery](https://mlsanity.com/paper/2410.11124/) (2024)
- [UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation](https://mlsanity.com/paper/2501.06440/) (2025)
- [Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning](https://mlsanity.com/paper/2107.04983/) (2021)

## Built on by (most cited first, 3 of 3)

- [K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2505.23017/) (2025)
- [TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization](https://mlsanity.com/paper/2503.03276/) (2025)
- [Segregation and Context Aggregation Network for Real-time Cloud Segmentation](https://mlsanity.com/paper/2504.14178/) (2025)

## Abstract (arXiv)

Remote sensing image segmentation is pivotal for earth observation, underpinning applications such as environmental monitoring and urban planning. Due to the limited annotation data available in remote sensing images, numerous studies have focused on data augmentation as a means to alleviate overfitting in deep learning networks. However, some existing data augmentation strategies rely on simple transformations that may not sufficiently enhance data diversity or model generalization capabilities. This paper proposes a novel augmentation strategy, Clustered-Patch-Mixed Mosaic (CP2M), designed to address these limitations. CP2M integrates a Mosaic augmentation phase with a clustered patch mix phase. The former stage constructs a new sample from four random samples, while the latter phase uses the connected component labeling algorithm to ensure the augmented data maintains spatial coherence and avoids introducing irrelevant semantics when pasting random patches. Our experiments on the ISPRS Potsdam dataset demonstrate that CP2M substantially mitigates overfitting, setting new benchmarks for segmentation accuracy and model robustness in remote sensing tasks.