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AiTLAS: Benchmark Arena

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 available on this repository.

For further details, please refer to our paper Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.197, pp 18-35

AiTLAS: Benchmark Arena is part of the AiTLAS ecosystem, an open-source library for exploratory and predictive analysis of satellite imaginary pertaining to different remote-sensing tasks.

Citation

For attribution in academic contexts, please cite this work as

@article{aitlas_arena2022,
      title={{Current Trends in Deep Learning for Earth Observation:An Open-source Benchmark Arena for Image Classification}}, 
      author={Ivica Dimitrovski and Ivan Kitanovski and Dragi Kocev and Nikola Simidjievski},
      journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
      volume = {197},
      pages = {18-35},
      year = {2023},
      issn = {0924-2716},
}

Datasets

You can obtain each dataset (with the respective splits) on the links below. If you use these datasets please cite the original authors accordingly. The references are provided in Tables 1 and 2 in our paper. You can find more details about each dataset in the supplementary material of the paper as well as on our AiTLAS Semantic Data Catalog.

Multi-class datasets

Dataset Data source Data Splits
EuroSAT github train validation test
UC Merced url train validation test
RSSCN7 figshare train validation test
WHU-RS19 zip train validation test
AID baidu train validation test
SIRI-WHU figshare train validation test
RSI-CB256 one drive train validation test
RESISC45 one drive train validation test
PatternNet gdrive train validation test
CLRS baidu train validation test
RSD46-WHU baidu train validation test
SAT6 gdrive train validation test
Optimal31 gdrive train validation test
Brazilian Coffee Scenes zip train validation test
So2Sat url train validation test

Multi-label datasets

Dataset Data source Data Splits
UC Merced (mlc) gdrive train validation test
AID (mlc) github train validation test
DFC15 gdrive train validation test
Planet UAS kaggle train validation test
MLRSNet mendeley train validation test
BigEarthNet 19 url train validation test
BigEarthNet 43 url train validation test

Performance

Pretrained [ImageNet-1K]

Multi-class datasets

*Top-1 Accuracy

Dataset\Model AlexNet VGG16 ResNet50 ResNet152 DenseNet161 EfficientNetB0 ViT MLPMixer ConvNeXt SwinT
WHU-RS19 93.532 99.005 99.502 98.01 100 99.502 99.502 98.507 99.005 99.502
Optimal31 80.914 88.71 92.204 92.473 94.355 91.667 94.624 92.742 93.011 92.473
UC Merced 92.143 95.476 98.571 98.810 98.333 98.571 98.333 98.333 97.857 98.571
SIRI-WHU 92.292 93.958 95 96.25 95.625 95 95.625 95.208 96.25 95.625
RSSCN7 91.964 93.929 95 95 94.821 95.536 95.893 95.179 94.643 95.179
BCS 89.583 90.972 92.014 92.361 92.708 91.319 92.014 93.056 91.493 93.403
AID 92.9 96.1 96.55 97.2 97.25 96.25 97.750 96.7 96.95 97.4
CLRS 84.1 89.9 91.567 91.9 92.2 90.5 93.200 90.1 91.1 92.533
RSI-CB256 99.354 99.051 99.677 99.859 99.737 99.717 99.758 99.657 99.596 99.677
Eurosat 97.574 98.148 98.833 99 98.889 98.907 98.722 98.741 98.778 98.944
PatternNet 99.161 99.424 99.737 99.49 99.737 99.539 99.655 99.704 99.671 99.688
RESISC45 90.492 93.905 96.46 96.54 96.508 94.873 97.079 95.952 96.27 96.587
RSD46-WHU 90.646 92.422 94.158 94.404 94.507 93.387 94.238 93.673 93.627 93.536
So2Sat 59.203 65.375 61.903 65.169 65.756 65.801 68.551 67.066 66.169 65.950
SAT6 99.98 99.993 100 100 100 99.988 99.998 99.995 99.999 99.999

Multi-label datasets

*Mean Average Precision mAP

Dataset\Model AlexNet VGG16 ResNet50 ResNet152 DenseNet161 EfficientNetB0 ViT MLPMixer ConvNeXt SwinT
AID (mlc) 75.906 79.893 80.758 80.942 81.708 78.002 81.539 80.879 82.298 82.254
UC Merced (mlc) 92.638 92.848 95.665 96.01 96.056 95.384 96.699 96.34 96.431 96.831
DFC15 94.057 96.566 97.662 97.6 97.529 96.787 97.617 97.941 97.994 98.111
Planet UAS 64.048 65.584 65.528 64.825 66.339 64.157 66.804 67.330 66.447 67.837
MLRSNet 93.399 94.633 96.272 96.432 96.306 95.391 96.41 95.049 95.807 96.620
BigEarthNet 19 77.147 78.418 79.983 79.776 79.686 80.221 77.31 77.288 80.283 81.384
BigEarthNet 43 58.554 61.205 66.256 64.066 64.229 64.589 58.997 59.648 66.166 67.733

Models

All trained models are available here.

Model list

Model from scratch pretrained [ImageNet1K] config files logs
AlexNet ✔️ ✔️ ✔️ ✔️
VGG16 ✔️ ✔️ ✔️ ✔️
ResNet50 ✔️ ✔️ ✔️ ✔️
ResNet152 ✔️ ✔️ ✔️ ✔️
DenseNet161 ✔️ ✔️ ✔️ ✔️
EfficientNetB0 ✔️ ✔️ ✔️ ✔️
Vision Transformer ✔️ ✔️ ✔️ ✔️
MLPMixer ✔️ ✔️ ✔️ ✔️
ConvNeXt ✔️ ✔️ ✔️ ✔️
Swin Transformer ✔️ ✔️ ✔️ ✔️