16 datasets found

Referring Remote Sensing Image Segmentation (RRSIS) is a new challenge that combines computer vision and natural language processing, delineating specific regions in aerial...

Referring Remote Sensing Image Segmentation (RRSIS) is a new challenge that combines computer vision and natural language processing, delineating specific regions in aerial...

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

Semantic segmentation in high resolution remote sensing images

Semantic segmentation in high resolution remote sensing images

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.

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

CITY-OSM dataset includes Berlin and Potsdam in Germany, Chicago in the United States, Paris in France, Zurich in Switzerland and Tokyo in Japan, with a total of 1641 aerial...

A private dataset of UAV-borne remote sensing images with a resolution between 10000×10000 and 20000×20000 is constructed. Each remote sensing image which corresponds to the red...

High-resolution remotely sensed images pose a challenge for commonly used semantic segmentation methods such as Convolutional Neural Network (CNN) and Vision Transformer (ViT).

The RIT-18 image set contains a single aerial training image with a spatial resolution of 0.047 m.

The DSTL image set comprises 25 satellite images covering a region of 1000 m × 1000 m.

The ISPRS Potsdam dataset consists of remotely sensed imagery with a spatial resolution of 5 centimeters, and includes 6 semantic classes.

The LoveDA dataset contains high-spatial-resolution images from three different cities, focusing on improving the generalization capability of model from different urban and...

The ISPRS Vaihingen dataset consists of remotely sensed imagery with a spatial resolution of 9 centimeters, and includes 6 semantic classes.