104 datasets found

SatlasPretrain: A large-scale dataset for remote sensing image understanding

SAMRS: Scaling-up remote sensing segmentation dataset with segment anything model

The dataset contains twelve land cover types, namely, Fallow land, Built-up-area, Dense Vegetation, Fenugreek, Fodder, Gram, Mustard, Oat, Pea, Sparse Vegetation, Spinach and...

Remote sensing image scene classification dataset

Semantic segmentation in high resolution remote sensing images

Semantic segmentation in high resolution remote sensing images

Hyperspectral image super-resolution (HSI SR) task is critical and meaningful to enhance the image quality to better serve the subsequent high-level computer vision tasks.

UC Merced Land Use Dataset (Yang & Newsam, 2010) contains remote sensing satellite images of 21 classes, with 100 images in each class.

The DSIFN-CD dataset is a collection of high-resolution images with seasonal changes in different cities.

BigEarthNet-S2 is a large-scale benchmark archive for remote sensing image classification and retrieval.

GEDI is a full-waveform LiDAR sensor onboard the International Space Station (ISS) that has been in operation between latitudes of 51.6° N and 51.6° S since 2019.

Land cover classification is a key application for remote sensing and Earth observation (EO) data, as it provides essential information for various domains, such as urban...

Implicit ray-transformers for multi-view remote sensing image segmentation

Multi-view segmentation in Remote Sensing (RS) seeks to segment images from diverse perspectives within a scene.

Remote sensing of Chlorophyll-a is vital in monitoring climate change. Chlorphyll-a measurements give us an idea of the algae concentrations in the ocean, which lets us monitor...

Machine learning in remote sensing has matured alongside an increase in the availability and resolution of satellite imagery, enabling advances in such tasks as land use...

The Gaofen dataset is a high-quality PolSAR semantic segmentation dataset.

The Canadian Cropland Dataset is a multitemporal patch-based dataset of Canadian croplands, enriched with labels retrieved from the Canadian Annual Crop Inventory.

Hyperspectral images have attracted increasing interest from researchers worldwide. They provide more detailed information about an observed area and allow an accurate target...

Imagery collected by satellites and airborne platforms can enable a higher frequency of consistent observations and effectively observe spatiotemporal changes in benthos...