# LSKNet: A Foundation Lightweight Backbone for Remote Sensing

arXiv: https://arxiv.org/abs/2403.11735 · HTML: https://mlsanity.com/paper/2403.11735/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 113 of its 186 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 30 of 113)

- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)

## Built on by (most cited first, 30 of 44)

- [Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models](https://mlsanity.com/paper/2606.03748/) (2026)
- [SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection](https://mlsanity.com/paper/2412.20665/) (2024)
- [Pick of the Bunch: Detecting Infrared Small Targets Beyond Hit-Miss Trade-Offs via Selective Rank-Aware Attention](https://mlsanity.com/paper/2408.03717/) (2024)
- [Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection](https://mlsanity.com/paper/2501.03775/) (2025)
- [RSAR: Restricted State Angle Resolver and Rotated SAR Benchmark](https://mlsanity.com/paper/2501.04440/) (2025)
- [M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detection](https://mlsanity.com/paper/2505.10931/) (2025)
- [Frequency-Dynamic Attention Modulation for Dense Prediction](https://mlsanity.com/paper/2507.12006/) (2025)
- [Background Semantics Matter: Cross-Task Feature Exchange Network for Clustered Infrared Small Target Detection](https://mlsanity.com/paper/2407.20078/) (2024)
- [Visual Instruction Pretraining for Domain-Specific Foundation Models](https://mlsanity.com/paper/2509.17562/) (2025)
- [EagleVision: Object-level Attribute Multimodal LLM for Remote Sensing](https://mlsanity.com/paper/2503.23330/) (2025)
- [UniChange: Unifying Change Detection with Multimodal Large Language Model](https://mlsanity.com/paper/2511.02607/) (2025)
- [DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing](https://mlsanity.com/paper/2505.19148/) (2025)
- [MHAF-YOLO: Multi-Branch Heterogeneous Auxiliary Fusion YOLO for accurate object detection](https://mlsanity.com/paper/2502.04656/) (2025)
- [A Simple Detector with Frame Dynamics is a Strong Tracker](https://mlsanity.com/paper/2505.04917/) (2025)
- [Fourier Angle Alignment for Oriented Object Detection in Remote Sensing](https://mlsanity.com/paper/2602.23790/) (2026)
- [ACPV-Net: All-Class Polygonal Vectorization for Seamless Vector Map Generation from Aerial Imagery](https://mlsanity.com/paper/2603.16616/) (2026)
- [Active-SAOOD: Active Sparsely Annotated Oriented Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2605.10162/) (2026)
- [MoCoLSK: Modality Conditioned High-Resolution Downscaling for Land Surface Temperature](https://mlsanity.com/paper/2409.19835/) (2024)
- [LightFormer: A lightweight and efficient decoder for remote sensing image segmentation](https://mlsanity.com/paper/2504.10834/) (2025)
- [A Gift from the Integration of Discriminative and Diffusion-based Generative Learning: Boundary Refinement Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2507.01573/) (2025)
- [Beyond Motion Cues and Structural Sparsity: Revisiting Small Moving Target Detection](https://mlsanity.com/paper/2509.07654/) (2025)
- [VK-Det: Visual Knowledge Guided Prototype Learning for Open-Vocabulary Aerial Object Detection](https://mlsanity.com/paper/2511.18075/) (2025)
- [MODA: The First Challenging Benchmark for Multispectral Object Detection in Aerial Images](https://mlsanity.com/paper/2512.09489/) (2025)
- [Parameters as Experts: Adapting Vision Models with Dynamic Parameter Routing](https://mlsanity.com/paper/2602.06862/) (2026)
- [Unifying Heterogeneous Multi-Modal Remote Sensing Detection Via Language-Pivoted Pretraining](https://mlsanity.com/paper/2603.01758/) (2026)
- [RiO-DETR: DETR for Real-time Oriented Object Detection](https://mlsanity.com/paper/2603.09411/) (2026)
- [SLIP-RS: Structured-Attribute Language-Image Pre-Training for Remote Sensing Object Detection](https://mlsanity.com/paper/2605.23144/) (2026)
- [IPDiff: Diffusion-driven ORSI Salient Object Detection with Information Reconstruction and Multi-Prior Guidance](https://mlsanity.com/paper/2607.03696/) (2026)
- [Learning Dynamic Local Context Representations for Infrared Small Target Detection](https://mlsanity.com/paper/2412.17401/) (2024)
- [MobileViM: A Light-weight and Dimension-independent Vision Mamba for 3D Medical Image Analysis](https://mlsanity.com/paper/2502.13524/) (2025)

## Abstract (arXiv)

Remote sensing images pose distinct challenges for downstream tasks due to their inherent complexity. While a considerable amount of research has been dedicated to remote sensing classification, object detection and semantic segmentation, most of these studies have overlooked the valuable prior knowledge embedded within remote sensing scenarios. Such prior knowledge can be useful because remote sensing objects may be mistakenly recognized without referencing a sufficiently long-range context, which can vary for different objects. This paper considers these priors and proposes a lightweight Large Selective Kernel Network (LSKNet) backbone. LSKNet can dynamically adjust its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios. To our knowledge, large and selective kernel mechanisms have not been previously explored in remote sensing images. Without bells and whistles, our lightweight LSKNet sets new state-of-the-art scores on standard remote sensing classification, object detection and semantic segmentation benchmarks. Our comprehensive analysis further validated the significance of the identified priors and the effectiveness of LSKNet. The code is available at https://github.com/zcablii/LSKNet.