# Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images

arXiv: https://arxiv.org/abs/1905.06368 · HTML: https://mlsanity.com/paper/1905.06368/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 30 of 51)

- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery](https://mlsanity.com/paper/2312.10115/) (2023)
- [High-Resolution Image Harmonization via Collaborative Dual Transformations](https://mlsanity.com/paper/2109.06671/) (2021)
- [Progressive Semantic Segmentation](https://mlsanity.com/paper/2104.03778/) (2021)
- [Global-Local Transformer for Brain Age Estimation](https://mlsanity.com/paper/2109.01663/) (2021)
- [High Quality Segmentation for Ultra High-resolution Images](https://mlsanity.com/paper/2111.14482/) (2021)
- [CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2410.22629/) (2024)
- [SkySense V2: A Unified Foundation Model for Multi-modal Remote Sensing](https://mlsanity.com/paper/2507.13812/) (2025)
- [MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling](https://mlsanity.com/paper/2303.09373/) (2023)
- [Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel Benchmark](https://mlsanity.com/paper/2305.10899/) (2023)
- [Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image Segmentation](https://mlsanity.com/paper/2307.14052/) (2023)
- [Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery](https://mlsanity.com/paper/2312.02481/) (2023)
- [Guided Patch-Grouping Wavelet Transformer with Spatial Congruence for Ultra-High Resolution Segmentation](https://mlsanity.com/paper/2307.00711/) (2023)
- [Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing](https://mlsanity.com/paper/2310.12452/) (2023)
- [Structural and Statistical Texture Knowledge Distillation and Learning for Segmentation](https://mlsanity.com/paper/2503.08043/) (2025)
- [Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition](https://mlsanity.com/paper/2103.00259/) (2021)
- [DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis](https://mlsanity.com/paper/2109.05788/) (2021)
- [CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks](https://mlsanity.com/paper/2208.10761/) (2022)
- [PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization](https://mlsanity.com/paper/2211.06560/) (2022)
- [Automatic High Resolution Wire Segmentation and Removal](https://mlsanity.com/paper/2304.00221/) (2023)
- [Long-Range Correlation Supervision for Land-Cover Classification from Remote Sensing Images](https://mlsanity.com/paper/2309.04225/) (2023)
- [Boosting General Trimap-free Matting in the Real-World Image](https://mlsanity.com/paper/2405.17916/) (2024)
- [Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images](https://mlsanity.com/paper/2405.18078/) (2024)
- [MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification with Zoom-Free Remote Sensing Imagery](https://mlsanity.com/paper/2503.11219/) (2025)
- [F2Net: A Frequency-Fused Network for Ultra-High Resolution Remote Sensing Segmentation](https://mlsanity.com/paper/2506.07847/) (2025)
- [Ultra-high Resolution Image Segmentation via Locality-aware Context Fusion and Alternating Local Enhancement](https://mlsanity.com/paper/2109.02580/) (2021)
- [Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2211.12425/) (2022)
- [Memory-Constrained Semantic Segmentation for Ultra-High Resolution UAV Imagery](https://mlsanity.com/paper/2310.04721/) (2023)
- [The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention](https://mlsanity.com/paper/2405.17776/) (2024)
- [Multiplane Prior Guided Few-Shot Aerial Scene Rendering](https://mlsanity.com/paper/2406.04961/) (2024)

## Abstract (arXiv)

Segmentation of ultra-high resolution images is increasingly demanded, yet poses significant challenges for algorithm efficiency, in particular considering the (GPU) memory limits. Current approaches either downsample an ultra-high resolution image or crop it into small patches for separate processing. In either way, the loss of local fine details or global contextual information results in limited segmentation accuracy. We propose collaborative Global-Local Networks (GLNet) to effectively preserve both global and local information in a highly memory-efficient manner. GLNet is composed of a global branch and a local branch, taking the downsampled entire image and its cropped local patches as respective inputs. For segmentation, GLNet deeply fuses feature maps from two branches, capturing both the high-resolution fine structures from zoomed-in local patches and the contextual dependency from the downsampled input. To further resolve the potential class imbalance problem between background and foreground regions, we present a coarse-to-fine variant of GLNet, also being memory-efficient. Extensive experiments and analyses have been performed on three real-world ultra-high aerial and medical image datasets (resolution up to 30 million pixels). With only one single 1080Ti GPU and less than 2GB memory used, our GLNet yields high-quality segmentation results and achieves much more competitive accuracy-memory usage trade-offs compared to state-of-the-arts.