# Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification

arXiv: https://arxiv.org/abs/2207.07189 · HTML: https://mlsanity.com/paper/2207.07189/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 99 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 30 of 43)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark](https://mlsanity.com/paper/1910.04867/) (2019)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016)
- [Understanding Robustness of Transformers for Image Classification](https://mlsanity.com/paper/2103.14586/) (2021)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [Vision Transformers are Robust Learners](https://mlsanity.com/paper/2105.07581/) (2021)
- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [An Empirical Study of Remote Sensing Pretraining](https://mlsanity.com/paper/2204.02825/) (2022)
- [PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval](https://mlsanity.com/paper/1706.03424/) (2017)
- [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)
- [TorchGeo: Deep Learning With Geospatial Data](https://mlsanity.com/paper/2111.08872/) (2021)

## Built on by (most cited first, 18 of 18)

- [GEO-Bench: Toward Foundation Models for Earth Monitoring](https://mlsanity.com/paper/2306.03831/) (2023)
- [Towards Geospatial Foundation Models via Continual Pretraining](https://mlsanity.com/paper/2302.04476/) (2023)
- [TerraMind: Large-Scale Generative Multimodality for Earth Observation](https://mlsanity.com/paper/2504.11171/) (2025)
- [SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models](https://mlsanity.com/paper/2304.11619/) (2023)
- [Isolated Channel Vision Transformers: From Single-Channel Pretraining to Multi-Channel Finetuning](https://mlsanity.com/paper/2503.09826/) (2025)
- [In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2307.01645/) (2023)
- [Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer](https://mlsanity.com/paper/2410.01092/) (2024)
- [OpenEarthSensing: Large-Scale Fine-Grained Benchmark for Open-World Remote Sensing](https://mlsanity.com/paper/2502.20668/) (2025)
- [Human-annotated label noise and their impact on ConvNets for remote sensing image scene classification](https://mlsanity.com/paper/2305.12106/) (2023)
- [Deep Multimodal Fusion for Semantic Segmentation of Remote Sensing Earth Observation Data](https://mlsanity.com/paper/2410.00469/) (2024)
- [Real-Time Semantic Segmentation of Aerial Images Using an Embedded U-Net: A Comparison of CPU, GPU, and FPGA Workflows](https://mlsanity.com/paper/2503.08700/) (2025)
- [Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning](https://mlsanity.com/paper/2409.03938/) (2024)
- [Geospatial Machine Learning Libraries](https://mlsanity.com/paper/2510.02572/) (2025)
- [Adaptive Gradient Calibration for Single-Positive Multi-Label Learning in Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2510.08269/) (2025)
- [Few-Shot Remote Sensing Image Scene Classification with CLIP and Prompt Learning](https://mlsanity.com/paper/2510.24321/) (2025)
- [The Global-Local loop: what is missing in bridging the gap between geospatial data from numerous communities?](https://mlsanity.com/paper/2603.20305/) (2026)
- [Design Choices That Matter: A Functional ANOVA Analysis for Remote Sensing Multi-Label Classification](https://mlsanity.com/paper/2608.04702/) (2026)
- [A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation](https://mlsanity.com/paper/2608.28247/) (2026)

## Abstract (arXiv)

We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures and compare them to a variety of multi-class and multi-label classification tasks from 22 datasets with different sizes and properties. In addition to models trained entirely on these datasets, we benchmark models trained in the context of transfer learning, leveraging pre-trained model variants, as it is typically performed in practice. All presented approaches are general and can be easily extended to many other remote sensing image classification tasks not considered in this study. To ensure reproducibility and facilitate better usability and further developments, all of the experimental resources including the trained models, model configurations, and processing details of the datasets (with their corresponding splits used for training and evaluating the models) are publicly available on the repository: https://github.com/biasvariancelabs/aitlas-arena