These are images generated with Gigapan technology Through the service requesting information at each point, you can navigate on the high resolution photo thanks to the link View high resolution image as well as open the photo with Google Earth with the link View on Google Earth and navigate through the image located in its real position. Gigapan images have resolutions ranging from 0.5 to 2.5 Gigapixels (500 and 2500 Megapixels) and are generated by merging 200 to 400 individual photographs taken from the same location using a robotised head that moves the camera and automatically shoots. The images have also been published on the website https://www.grafcan.es/fotos/ where the user can see the photos that exist so far made with this technology, navigate on each of them and also from here open them in Google Earth.

Date of data:
Each image has its date associated with requesting information from the server.

Communication platform Discord is a VoIP and social media that was launched in 2015 and became popular among gamer communities first. As of August 2025, the most popular science and technology server on Discord was Midjourney, a community created around AI-powered text-to-image tool, with over **** million members. The Discord server LimeWire ranked second, gathering around *** million members, while Leonardo.Ai used to create art and realistic images ranked third with almost *** million members.

This document describes the WEBUPV dataset compiled for the ImageCLEF
2012 Scalable image annotation task. The data mentioned here
indicates what is ready for download. However, upon request or
depending on feedback from the participants, additional data can be
released. For debugging purposes, thumbnails of the images in the
dataset can be obtained from a web server using '{IID}' the image
identifier:

http://risenet.prhlt.upv.es/db/img/{IID}.jpg

The following is the directory structure of the collection, and bellow
there is a brief description of what each compressed file
contains. The corresponding MD5 checksums of the files shown (for
verifying a correct download) can be found in the md5sums.txt.

Directory structure
-------------------

.
|
|--- README.txt
|--- md5sums.txt
|--- webupv_train_lists.zip
|--- webupv_devel_lists.zip
|--- webupv_test_lists.zip
|--- baseline.zip
|
|--- feats_textual/
| |
| |--- webupv_train_textual.rawfeat.gz
| |--- webupv_train_textual.scofeat.gz
| |--- webupv_train_textual.keywords.gz
|
|--- feats_visual/
|
|--- webupv_{train|devel|test}_visual_gist.feat.gz
|--- webupv_{train|devel|test}_visual_sift_*.feat.gz
|--- webupv_{train|devel|test}_visual_csift_*.feat.gz
|--- webupv_{train|devel|test}_visual_rgbsift_*.feat.gz
|--- webupv_{train|devel|test}_visual_opponentsift_*.feat.gz
|--- webupv_{train|devel|test}_visual_colorhist.feat.gz

Contents of files
-----------------

* webupv_train_lists.zip
-> train_iids.txt : IDs of the images in the training set (250000).
-> train_rids.txt : IDs of the webpages in the training set.
-> train_rimgsrc.txt : The URLs of the images as referenced in each
of the webpages. This can also be useful as a
textual feature.

* webupv_devel_lists.zip
-> devel_iids.txt : IDs of the images in the development set (1000).
-> devel_concepts.txt : List concepts for the development set.
-> devel_gnd.txt : Ground truth concepts for the development set
images.

* webupv_test_lists.zip
-> test_iids.txt : IDs of the images in the test set (2000).
-> test_concepts.txt : List concepts for the test set.
-> test_gnd.txt : Ground truth concepts for the test set images.

* baseline.zip

An archive that includes code for computing the evaluation measures
for two baseline techniques for the "Scalable concept image
annotation" subtask. See the included README.txt for details.

* feats_textual/webupv_train_textual.rawfeat.gz

The raw text extracted from the webpages near where the images
appeared. Each line starts with the image and webpage IDs followed
by the text extracted. The position of the image within the text is
indicated by the special word '{X}'. The extracted text is somewhat
filtered (e.g. there are no HTML tags), although removed words and
tags have been replaced by full stops '.' to preserve word
distances. The title of the webpage is always included, and it is
the first sentence of the text. In total the file has 275749 lines
since the images can appear in more than one webpage.

* feats_textual/webupv_train_textual.scofeat.gz

The processed text extracted from the webpages near where the images
appeared. Each line corresponds to one image, having the same order
as the train_iids.txt list. The lines start with the image ID,
followed by the number of extracted unique words and the
corresponding word-score pairs. The scores were derived taking into
account 1) the term frequency (TF), 2) the document object model
(DOM) attributes, and 3) the word distance to the image. The scores
are all integers and for each image the sum of scores is always
<=100000 (i.e. it is normalized).

* feats_textual/webupv_train_textual.keywords.gz

The words used to find the images when querying image search
engines. Each line corresponds to an image (in the same order as
in train_iids.txt). The lines are composed of triplets:

[keyword] [rank] [search_engine]

where [keyword] is the word used to find the image, [rank] is the
position given to the image in the query, and [search_engine] is a
single character indicating in which search engine it was found
('g':google, 'b':bing, 'y':yahoo).

* feats_visual/webupv_*.feat.gz

The visual features in a simple ASCII text sparse format. The first
line of the file indicates the number of vectors (N) and the
dimensionality (DIMS). Then each line corresponds to one vector,
starting with the number of non-zero elements and followed by pairs
of dimension-value, being the first dimension 0. In summary the file
format is:

N DIMS
nz1 Dim(1,1) Val(1,1) ... Dim(1,nz1) Val(1,nz1)
nz2 Dim(2,1) Val(2,1) ... Dim(2,nz2) Val(2,nz2)
...
nzN Dim(N,1) Val(N,1) ... Dim(N,nzN) Val(N,nzN)

The order of the features is the same as in the lists
devel_iids.txt, test_iids.txt and train_iids.txt.

The procedure to extract the SIFT based features in this
subdirectory was conducted as follows. Using the ImageMagick
software, the images were first rescaled to having a maximum of 240
pixels, of both width and height, while preserving the original
aspect ratio, employing the command:

convert {IMGIN}.jpg -resize '240>x240>' {IMGOUT}.jpg

Then the SIFT features where extracted using the ColorDescriptor
software from Koen van de Sande
(http://koen.me/research/colordescriptors). As configuration we
used, 'densesampling' detector with default parameters, and a hard
assignment codebook using a spatial pyramid as
'pyramid-1x1-2x2'. The number in the file name indicates the size of
the codebook. All of the vectors of the spatial pyramid are given in
the same line, thus keeping only the first 1/5th of the dimensions
would be like not using the spatial pyramid. The codebook was
generated using 1.25 million randomly selected features and the
k-means algorithm.

Contact
-------

For further questions, please contact:
Mauricio Villegas

Google searches interested in the AI-powered text-to-image "Midjourney" spiked in the first 3 weeks of June 2025. On June 15, the keyword hit a popularity score of 100 index points, before starting to decrease to 75 index points to its latest measured week. Midjourney was first launched on Discord with a dedicated server where users can produce and share the images created with the help of the integrated Discord bot. As of April 2025, Midjourney was the most popular server on the platform, with approximately 20 million Discord user members.

Transforma imágenes estáticas en videos animados cautivadores, sin necesidad de habilidades de edición. Perfecto para especialistas en marketing, creadores y empresas.