# Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

arXiv: https://arxiv.org/abs/2005.01094 · HTML: https://mlsanity.com/paper/2005.01094/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2020.

## Built on by (most cited first, 30 of 71)

- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [An Empirical Study of Remote Sensing Pretraining](https://mlsanity.com/paper/2204.02825/) (2022)
- [BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval](https://mlsanity.com/paper/2105.07921/) (2021)
- [Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks](https://mlsanity.com/paper/2208.04441/) (2022)
- [Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation](https://mlsanity.com/paper/2205.11319/) (2022)
- [CHOICE: Benchmarking the Remote Sensing Capabilities of Large Vision-Language Models](https://mlsanity.com/paper/2411.18145/) (2024)
- [Prompt-based Adaptation in Large-scale Vision Models: A Survey](https://mlsanity.com/paper/2510.13219/) (2025)
- [Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification](https://mlsanity.com/paper/2207.07189/) (2022)
- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [Universal Domain Adaptation for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2301.11387/) (2023)
- [MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels](https://mlsanity.com/paper/2103.10368/) (2021)
- [GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates](https://mlsanity.com/paper/2304.07750/) (2023)
- [Harnessing Massive Satellite Imagery with Efficient Masked Image Modeling](https://mlsanity.com/paper/2406.11933/) (2024)
- [RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation Model](https://mlsanity.com/paper/2411.17984/) (2024)
- [Efficient High-Resolution Deep Learning: A Survey](https://mlsanity.com/paper/2207.13050/) (2022)
- [SUNDIAL: 3D Satellite Understanding through Direct, Ambient, and Complex Lighting Decomposition](https://mlsanity.com/paper/2312.16215/) (2023)
- [Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling](https://mlsanity.com/paper/2201.01953/) (2022)
- [False: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image](https://mlsanity.com/paper/2211.07928/) (2022)
- [SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation](https://mlsanity.com/paper/2507.12857/) (2025)
- [MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images](https://mlsanity.com/paper/2104.02846/) (2021)
- [Mapping industrial poultry operations at scale with deep learning and aerial imagery](https://mlsanity.com/paper/2112.10988/) (2021)
- [AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning](https://mlsanity.com/paper/2202.00067/) (2022)
- [Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2204.09120/) (2022)
- [All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification](https://mlsanity.com/paper/2205.03371/) (2022)
- [Remote Sensing Image Classification with the SEN12MS Dataset](https://mlsanity.com/paper/2104.00704/) (2021)
- [Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks](https://mlsanity.com/paper/2104.11200/) (2021)
- [A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferability](https://mlsanity.com/paper/2202.03342/) (2022)
- [RingMo-lite: A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework](https://mlsanity.com/paper/2309.09003/) (2023)
- [FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2](https://mlsanity.com/paper/2311.03339/) (2023)
- [Rainy: Unlocking Satellite Calibration for Deep Learning in Precipitation](https://mlsanity.com/paper/2504.10776/) (2025)

## Abstract (arXiv)

Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering the rapid evolution of this field, this paper provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers. To be specific, we discuss the main challenges of remote sensing image scene classification and survey (1) Autoencoder-based remote sensing image scene classification methods, (2) Convolutional Neural Network-based remote sensing image scene classification methods, and (3) Generative Adversarial Network-based remote sensing image scene classification methods. In addition, we introduce the benchmarks used for remote sensing image scene classification and summarize the performance of more than two dozen of representative algorithms on three commonly-used benchmark data sets. Finally, we discuss the promising opportunities for further research.