33 datasets found

The PCD dataset consists of panoramic image pairs, each 1024 × 224, taken at two different time points, t0 and t1, and the change mask, Ic.

This paper presents a novel method for detecting scene changes from a pair of images with a difference of camera viewpoints using a dense optical flow based change detection...

Change detection (CD) methods have been applied to optical data for decades, while the use of hyperspectral data with a fine spectral resolution has been rarely explored. CD is...

The CDD dataset contains 11 pairs of bi-temporal images obtained from Google Earth in different seasons with a spatial resolution ranging from 3 to 100 cm per pixel.

Change detection is a crucial but extremely challenging task in remote sensing image analysis, and much progress has been made with the rapid development of deep learning....

Change detection in high-resolution remote sensing images using a bitemporal image transformer (BIT)

The Change Detection Dataset contains realistic, diverse set of videos with pixel-wise labeling of moving objects.

The Vaihingen dataset consists of 1440 scenes with a size of 250×250 pixels. Each scene is a colour-infrared (CIR) true orthophoto and a height grid (digital surface model; DSM)...

The CNAM-CD dataset is a multi-class change detection dataset with a resolution of 0.5 meter.

The SECOND dataset is a semantic change detection dataset with 7 land cover class annotations.

A large-scale dataset containing 1689 training, 202 validation, and 392 testing image pairs of rural areas around the world, with the size of 1024 × 1024 and spatial resolution...

A large-scale dataset containing 5 million RS images with English descriptions by filtering the image-text pair dataset and generating captions for RS images.

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

The proposed SMDNet model is used for change detection in high-resolution remote sensing images.

The CDD dataset is a collection of remote sensing images with seasonal changes and different lighting conditions.

The Sardinia dataset consists of bi-temporal SAR images, acquired by the RADARSAT at the area of Sardinia in September 1995 and July 1996, respectively.

The Yellow River dataset consists of bi-temporal SAR images, acquired by Radarsat-2 at the region of Yellow River Estuary in China in June 2008 and June 2009, respectively.

The Bern dataset consists of bi-temporal SAR images, acquired by the European Remote Sensing 2 satellite SAR sensor at the region of Bern, Switzerland, in April and May 1999,...

The Ottawa dataset consists of bi-temporal SAR images, acquired by the RADARSAT at the area of Ottawa in May 1997 and August 1997, respectively.

Change Detection (CD) is an important problem in remote sensing in which our goal is to identify relevant changes in bi-temporal remote sensing images that can help in various...