README.md

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	✔️	✔️	✔️	✔️