Image Fusion

Drone based wildfire detection and modeling methods enable high-precision, real-time fire monitoring that is not provided by traditional remote fire monitoring systems, such as satellite imaging. Precise, real-time information enables rapid, effective wildfire intervention and management strategies. Drone systems’ ease of deployment, omnidirectional maneuverability, and robust sensing capabilities make them effective tools for early wildfire detection and evaluation, particularly so in environments that are inconvenient for humans and/or terrestrial vehicles.


A Chinese character gesture dataset for 8 Chinese characters ( “国”,“图”,“木”,“工”,“口”,“中”,“国”,“人” ) .Datasets were collected in three different environments.


The C3I Synthetic Face Depth Dataset consists of 3D virtual human models and 2D rendered RGB and GT depth images in zipped version into two folders for male and female.


The presented dataset is a supplementary material to the paper [1] and it represents the X-Ray Energy Dispersive (EDS)/ Scanning Electron Microscopy (SEM) images of a shungite-mineral particle. Pansharpening is a procedure for enhancing the spatial resolution of a multispectral image, here the EDS individual bands, with a high-spatial panchromatic image, here the SEM image. Pansharpening techniques are usually tested with remote sensed data, but the procedures have been efficient in close-range MS-PAN pairs as well [3].


The Contest: Goals and Organization

The 2022 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee, aims to promote research on semi-supervised learning. The overall objective is to build models that are able to leverage a large amount of unlabelled data while only requiring a small number of annotated training samples. The 2022 Data Fusion Contest will consist of two challenge tracks:

Track SLM:Semi-supervised Land Cover Mapping

Last Updated On: 
Mon, 03/07/2022 - 04:41

Sequential skeleton and average foot pressure data for normal and five pathological gaits (i.e., antalgic, lurching, steppage, stiff-legged, and Trendelenburg) were simultaneously collected. The skeleton data were collected by using Azure Kinect (Microsoft Corp. Redmond, WA, USA). The average foot pressure data were collected by GW1100 (GHIWell, Korea). 12 healthy subjects participated in data collection. They simulated the pathological gaits under strict supervision. A total of 1,440 data instances (12 people x 6 gait types x 20 walkings) were collected.


There is an industry gap for publicly available electric utility infrastructure imagery.  The Electric Power Research Institute (EPRI) is filling this gap to support public and private sector AI innovation.  This dataset consists of ~30,000 images of overhead Distribution infrastructure.  These images have been anonymized, reviewed, and .exif image-data scrubbed.  EPRI intends to label these data to support its own research activities.  As these labels are created, EPRI will periodically update this dataset with those data.

Update: July 2022


This dataset was acquired at the Radboud University Medical Center, Nijmegen, the Netherlands and enriched with landmarks by Fraunhofer MEVIS. It consists of nine datasets of consecutive sections, each containing four slides stained with H&E, CD8, CD45, Ki67, respectively.


We present here an annotated thermal dataset which is linked to the dataset present in

To our knowledge, this is the only public dataset at present, which has multi class annotation on thermal images, comprised of 5 different classes.

This database was hand annotated over a period of 130 work hours.