# MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding

arXiv: https://arxiv.org/abs/2010.00243 · HTML: https://mlsanity.com/paper/2010.00243/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 30 of 59)

- [RemoteCLIP: A Vision Language Foundation Model for Remote Sensing](https://mlsanity.com/paper/2306.11029/) (2023)
- [Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning](https://mlsanity.com/paper/2212.14532/) (2022)
- [Towards Geospatial Foundation Models via Continual Pretraining](https://mlsanity.com/paper/2302.04476/) (2023)
- [Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding](https://mlsanity.com/paper/2104.07070/) (2021)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIP](https://mlsanity.com/paper/2304.05995/) (2023)
- [Domain-Controlled Prompt Learning](https://mlsanity.com/paper/2310.07730/) (2023)
- [Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification](https://mlsanity.com/paper/2207.07189/) (2022)
- [Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives](https://mlsanity.com/paper/2505.14361/) (2025)
- [Domain Prompt Learning with Quaternion Networks](https://mlsanity.com/paper/2312.08878/) (2023)
- [Generic Knowledge Boosted Pre-training For Remote Sensing Images](https://mlsanity.com/paper/2401.04614/) (2024)
- [SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models](https://mlsanity.com/paper/2304.11619/) (2023)
- [Federated Continual Learning: Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2502.07059/) (2025)
- [How to decay your learning rate](https://mlsanity.com/paper/2103.12682/) (2021)
- [Self-supervised visual learning in the low-data regime: a comparative evaluation](https://mlsanity.com/paper/2404.17202/) (2024)
- [EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision](https://mlsanity.com/paper/2501.08111/) (2025)
- [A diverse large-scale building dataset and a novel plug-and-play domain generalization method for building extraction](https://mlsanity.com/paper/2208.10004/) (2022)
- [Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling](https://mlsanity.com/paper/2201.01953/) (2022)
- [Enhancing Remote Sensing Vision-Language Models for Zero-Shot Scene Classification](https://mlsanity.com/paper/2409.00698/) (2024)
- [GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Data](https://mlsanity.com/paper/2509.26016/) (2025)
- [NTIRE 2021 Multi-modal Aerial View Object Classification Challenge](https://mlsanity.com/paper/2107.01189/) (2021)
- [AiTLAS: Artificial Intelligence Toolbox for Earth Observation](https://mlsanity.com/paper/2201.08789/) (2022)
- [SeasoNet: A Seasonal Scene Classification, segmentation and Retrieval dataset for satellite Imagery over Germany](https://mlsanity.com/paper/2207.09507/) (2022)
- [C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing](https://mlsanity.com/paper/2311.15812/) (2023)
- [Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport](https://mlsanity.com/paper/2503.15337/) (2025)
- [SOTA: Self-adaptive Optimal Transport for Zero-Shot Classification with Multiple Foundation Models](https://mlsanity.com/paper/2506.13723/) (2025)
- [Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems](https://mlsanity.com/paper/2601.01891/) (2026)
- [SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification](https://mlsanity.com/paper/2110.04494/) (2021)
- [Do we still need ImageNet pre-training in remote sensing scene classification?](https://mlsanity.com/paper/2111.03690/) (2021)
- [MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification with Zoom-Free Remote Sensing Imagery](https://mlsanity.com/paper/2503.11219/) (2025)

## Abstract (arXiv)

To better understand scene images in the field of remote sensing, multi-label annotation of scene images is necessary. Moreover, to enhance the performance of deep learning models for dealing with semantic scene understanding tasks, it is vital to train them on large-scale annotated data. However, most existing datasets are annotated by a single label, which cannot describe the complex remote sensing images well because scene images might have multiple land cover classes. Few multi-label high spatial resolution remote sensing datasets have been developed to train deep learning models for multi-label based tasks, such as scene classification and image retrieval. To address this issue, in this paper, we construct a multi-label high spatial resolution remote sensing dataset named MLRSNet for semantic scene understanding with deep learning from the overhead perspective. It is composed of high-resolution optical satellite or aerial images. MLRSNet contains a total of 109,161 samples within 46 scene categories, and each image has at least one of 60 predefined labels. We have designed visual recognition tasks, including multi-label based image classification and image retrieval, in which a wide variety of deep learning approaches are evaluated with MLRSNet. The experimental results demonstrate that MLRSNet is a significant benchmark for future research, and it complements the current widely used datasets such as ImageNet, which fills gaps in multi-label image research. Furthermore, we will continue to expand the MLRSNet. MLRSNet and all related materials have been made publicly available at https://data.mendeley.com/datasets/7j9bv9vwsx/2 and https://github.com/cugbrs/MLRSNet.git.