# PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation

arXiv: https://arxiv.org/abs/2103.06564 · HTML: https://mlsanity.com/paper/2103.06564/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 70 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 30 of 58)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [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)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection](https://mlsanity.com/paper/1904.07392/) (2019)
- [Context Encoding for Semantic Segmentation](https://mlsanity.com/paper/1803.08904/) (2018)

## Built on by (most cited first, 29 of 29)

- [SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://mlsanity.com/paper/2209.08575/) (2022)
- [DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation](https://mlsanity.com/paper/2203.12560/) (2022)
- [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (2023)
- [SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation](https://mlsanity.com/paper/2403.16605/) (2024)
- [EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow](https://mlsanity.com/paper/2109.09406/) (2021)
- [CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2410.22629/) (2024)
- [AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation](https://mlsanity.com/paper/2306.06842/) (2023)
- [Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2212.10236/) (2022)
- [SFNet: Faster and Accurate Semantic Segmentation via Semantic Flow](https://mlsanity.com/paper/2207.04415/) (2022)
- [PPTFormer: Pseudo Multi-Perspective Transformer for UAV Segmentation](https://mlsanity.com/paper/2406.19632/) (2024)
- [Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images](https://mlsanity.com/paper/2302.12491/) (2023)
- [Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation](https://mlsanity.com/paper/2306.11734/) (2023)
- [LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagery](https://mlsanity.com/paper/2406.16502/) (2024)
- [FSDENet: A Frequency and Spatial Domains based Detail Enhancement Network for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2510.00059/) (2025)
- [ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2108.11535/) (2021)
- [Conditional Permutation Invariant Flows](https://mlsanity.com/paper/2206.09021/) (2022)
- [BuildSeg: A General Framework for the Segmentation of Buildings](https://mlsanity.com/paper/2301.06190/) (2023)
- [Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos](https://mlsanity.com/paper/2105.07209/) (2021)
- [SSA-Seg: Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation](https://mlsanity.com/paper/2405.06525/) (2024)
- [LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images](https://mlsanity.com/paper/2303.07747/) (2023)
- [Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation](https://mlsanity.com/paper/2403.06621/) (2024)
- [A feature refinement module for light-weight semantic segmentation network](https://mlsanity.com/paper/2412.08670/) (2024)
- [SpecDM: Hyperspectral Dataset Synthesis with Pixel-level Semantic Annotations](https://mlsanity.com/paper/2502.17056/) (2025)
- [BuildMamba: A Visual State-Space Based Model for Multi-Task Building Segmentation and Height Estimation from Satellite Images](https://mlsanity.com/paper/2603.08523/) (2026)
- [Deep Level Set for Box-supervised Instance Segmentation in Aerial Images](https://mlsanity.com/paper/2112.03451/) (2021)
- [AF2: Adaptive Focus Framework for Aerial Imagery Segmentation](https://mlsanity.com/paper/2202.10322/) (2022)
- [SegForestNet: Spatial-Partitioning-Based Aerial Image Segmentation](https://mlsanity.com/paper/2302.01585/) (2023)
- [Seeing Beyond the Patch: Scale-Adaptive Semantic Segmentation of High-resolution Remote Sensing Imagery based on Reinforcement Learning](https://mlsanity.com/paper/2309.15372/) (2023)
- [SAIP-Net: Enhancing Remote Sensing Image Segmentation via Spectral Adaptive Information Propagation](https://mlsanity.com/paper/2504.16564/) (2025)

## Abstract (arXiv)

Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two critical issues: The one is an extremely foreground-background imbalanced distribution, and the other is multiple small objects along with the complex background. Such problems make the recent dense affinity context modeling perform poorly even compared with baselines due to over-introduced background context. To handle these problems, we propose a point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse affinity map is generated upon selected points between the adjacent features, which reduces the noise introduced by the background while keeping efficiency. In particular, we design a dual point matcher to select points from the salient area and object boundaries, respectively. Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods. Especially, our methods achieve the best speed and accuracy trade-off on three aerial benchmarks. Further experiments on three general semantic segmentation datasets prove the generality of our method. Code will be provided in (https: //github.com/lxtGH/PFSegNets).