9 datasets found

The LoveDA dataset consists of 5,987 high-quality optical remote sensing images with a resolution of 0.3 meters per pixel.

The ISPRS Vaihingen dataset contains 33 high-quality images with topographical information, each with an average resolution of 2494×2064 pixels.

Four widely-used remote sensing datasets are considered for evaluating the efficacy of the proposed approach. Sample images of these datasets are provided in Fig. 5.

The dataset used in the paper is the ResNet18 dataset, which is a convolutional neural network dataset.

A rice disease and pest detection dataset collected in real life scenario

Four remote sensing datasets were used for classification with both small and large training samples.

Urban change detection for multispectral earth observation using convolutional neural networks.

Rayleigh wave dispersion curves have been widely used in near-surface studies, and are primarily inverted for the shear wave (S-wave) velocity profiles. However, the inverse...

The dataset used in this paper is the VGG16 dataset, a deep neural network trained on the CIFAR-10 and CIFAR-100 datasets.