Abstract We present AiTLAS: Benchmark Arena–an open-source benchmark suite for
evaluating state-of-the-art deep learning approaches for image classification in Earth …

Deep learning methods have become an integral part of computer vision and machine
learning research by providing significant improvement performed in many tasks such as …

Self-supervised pretraining bears the potential to generate expressive representations from
large-scale Earth observation (EO) data without human annotation. However, most existing …

Supervised deep learning models depend on massive labeled data. Unfortunately, it is time-
consuming and labor-intensive to collect and annotate bitemporal samples containing …

Research in self-supervised learning (SSL) with natural images has progressed rapidly in
recent years and is now increasingly being applied to and benchmarked with datasets …

In the training of deep learning models, how the model parameters are initialized greatly
affects the model performance, sample efficiency, and convergence speed. Recently …

Due to the scarcity of labeled data, using supervised models pre-trained on ImageNet is a
de facto standard in remote sensing scene classification. Recently, the availability of larger …

Current AutoML systems have been benchmarked with traditional natural image datasets.
Differences between satellite images and natural images (eg, bit-wise resolution, the …

Transferring the ImageNet pre-trained weights to the various remote sensing tasks has
produced acceptable results and reduced the need for labeled samples. However, the …

Supervised deep learning requires a huge amount of reference data, which is often difficult
and expensive to obtain. Domain adaptation helps with this problem—labelled data from …