45 machine learning datasets
45 dataset results
This dataset contains the data for the paper 'Using Multiple Instance Learning for Explainable Solar Flare Prediction'.
We present datasets containing urban traffic and rural road scenes recorded using hyperspectral snap-shot sensors mounted on a moving car. The novel hyperspectral cameras used can capture whole spectral cubes at up to 15 Hz. This emerging new sensor modality enables hyperspectral scene analysis for autonomous driving tasks. Up to the best of the author’s knowledge no such dataset has been published so far. The datasets contain synchronized 3-D laser, spectrometer and hyperspectral data. Dense ground truth annotations are provided as semantic labels, material and traversability. The hyperspectral data ranges from visible to near infrared wavelengths. We explain our recoding platform and method, the associated data format along with a code library for easy data consumption. The datasets are publicly available for download.
HSI-Drive is the hyperspectral image (HSI) dataset created by the Digital Electronics Design Group (GDED) of the University of the Basque Country (UPV/EHU). This database is intended to contribute to the research into the use of hyperspectral imaging for the development of advanced driver assistance systems (ADAS) and autonomous driving systems (ADS). The dataset contains a diverse set of images recorded with a small-size 25-band VNIR snapshot camera mounted on a moving automobile. The recordings have been made in different seasons of the year, at different day times, under different weather conditions and on different types of roads. The dataset contains images and videos classified and tagged accordingly to provide rich and diverse data.
The Multi-Spectral Imaging via Computed Tomography (MUSIC) dataset is a two-part (2D- and 3D spectral) open access dataset for advanced image analysis of spectral radiographic (x-ray) scans, their tomographic reconstruction and the detection of specific materials within such scans. The scans operate at a photon energy range of around 20 keV up to 160 keV.
The dataset of Thermal Bridges on Building Rooftops (TBBR dataset) consists of annotated combined RGB and thermal drone images with a height map. All images were converted to a uniform format of 3000$\times$4000 pixels, aligned, and cropped to 2400$\times$3400 to remove empty borders.
SSL4EO-S12 is a large-scale, global, multimodal, and multi-seasonal corpus of satellite imagery from the ESA Sentinel-1 & -2 satellite missions.
MaNGA is a component of the Fourth-Generation Sloan Digital Sky Survey whose goal is to map the detailed composition and kinematic structure of nearby galaxies. MaNGA uses integral field unit (IFU) spectroscopy to measure spectra for hundreds of points within each galaxy. MaNGA’s goal is to understand the “life history” of present-day galaxies from imprinted clues of their birth and assembly, through their ongoing growth via star formation and merging, to their death from quenching at late times.
The ARPA-E funded TERRA-REF project is generating open-access reference datasets for the study of plant sensing, genomics, and phenomics. Sensor data were generated by a field scanner sensing platform that captures color, thermal, hyperspectral, and active flourescence imagery as well as three dimensional structure and associated environmental measurements. This dataset is provided alongside data collected using traditional field methods in order to support calibration and validation of algorithms used to extract plot level phenotypes from these datasets.
A high-resolution multi-sensor remote sensing scene classification dataset, appropriate for training and evaluating image classification models in the remote sensing domain.
This data set contains weekly scans of cauliflower and broccoli covering a ten week growth cycle from transplant to harvest. The data set includes ground-truth, physical characteristics of the crop; environmental data collected by a weather station and a soil-senor network; and scans of the crop performed by an autonomous agricultural robot, which include stereo colour, thermal and hyperspectral imagery. The crop were planted at Lansdowne Farm, a University of Sydney agricultural research and teaching facility. Lansdowne Farm is located in Cobbitty, a suburb 70km south-west of Sydney in New South Wales (NSW), Australia. Four 80 metre raised crop beds were prepared with a North-South orientation. Approximately 144 Brassica were planted in each bed. Cauliflower were planted in the first and third bed (from west to east). Broccoli were planted in the second and fourth beds.
The dataset contains full-spectral autofluorescence lifetime microscopic images (FS-FLIM) acquired on unstained ex-vivo human lung tissue, where 100 4D hypercubes of 256x256 (spatial resolution) x 32 (time bins) x 512 (spectral channels from 500nm to 780nm). This dataset associates with our paper "Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence Lifetime Microscopic Images with H&E-Stained Histology Images" (https://arxiv.org/abs/2202.07755) and "Full spectrum fluorescence lifetime imaging with 0.5 nm spectral and 50 ps temporal resolution" (https://doi.org/10.1038/s41467-021-26837-0). The FS-FLIM images provide transformative insights into human lung cancer with extra-dimensional information. This will enable visual and precise detection of early lung cancer. With the methodology in our co-registration paper, FS-FLIM images can be registered with H&E-stained histology images, allowing characterisation of tumour and surrounding cells at a celluar level with abs
This dataset inclue multi-spectral acquisition of vegetation for the conception of new DeepIndices. The images were acquired with the Airphen (Hyphen, Avignon, France) six-band multi-spectral camera configured using the 450/570/675/710/730/850 nm bands with a 10 nm FWHM. The dataset were acquired on the site of INRAe in Montoldre (Allier, France, at 46°20'30.3"N 3°26'03.6"E) within the framework of the “RoSE challenge” founded by the French National Research Agency (ANR) and in Dijon (Burgundy, France, at 47°18'32.5"N 5°04'01.8"E) within the site of AgroSup Dijon. Images of bean and corn, containing various natural weeds (yarrows, amaranth, geranium, plantago, etc) and sowed ones (mustards, goosefoots, mayweed and ryegrass) with very distinct characteristics in terms of illumination (shadow, morning, evening, full sun, cloudy, rain, ...) were acquired in top-down view at 1.8 meter from the ground. (2020-05-01)
We capture some hyperspectral images in our lab using the multishot DD-CASSI architecture. The algorithm can be found on GitHub
The LIB-HSI dataset contains hyperspectral reflectance images and their corresponding RGB images of building façades in a light industrial environment. The dataset also contains pixel-level annotated images for each hyperspectral/RGB image. The LIB-HSI dataset was created to develop deep learning methods for segmenting building facade materials.
This dataset contains the raw images for the dataset of Thermal Bridges on Building Rooftops (TBBR) dataset.
The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2D image data, which is insufficient for 3D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multi-sensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for non-structured 3D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent
The ROAD dataset is made up of observations from the Low Frequency Array (LOFAR) telescope. LOFAR is comprised of 52 stations across Europe, where each station is an array of 96 dual polarisation low-band antennas (LBA) in the 10–90 MHz range and 48 or 96 dual polarisation high-band antenna antennas (HBA) in the 110–250 MHz range. The data are four dimensional, with the dimensions corresponding to time, frequency, polarisation, and station. dictate the array configuration (i.e. the number of stations used), the number of frequency channels (Nf), the time sampling, as well as the overall integration time (Nt) of the observing session. Furthermore, the dual-polarisation of the antennas results in a correlation product (Npol) of size 4. The ROAD dataset contains ten classes that describe various system-wide phenomena and anomalies from data obtained by the LOFAR telescope. These classes are categorised into four groups: data processing system failures, electronic anomalies, environmental
Dataset of paired thermal and RGB images comprising ten diverse scenes—six indoor and four outdoor scenes— for 3D scene reconstruction and novel view synthesis (e.g. with NeRF).
Tecnalia Hyperspectral Dataset contains different non-ferreous fractions of Waste from Electric and Electronic Equipment (WEEE) of Copper, Brass, Aluminum, Stainless Steel and White Copper. Images were captured by a hyperspectral Specim PHF Fast10 camera that is able to capture wavelengths in the range 400 to 1000 nm with a spectral resolution of less than 1 nm. The PHF Fast10 camera is equipped with a CMOS sensor (1024 × 1024 resolution), a Camera Link interface and a special Fore objective OL10. The provided dataset contains 76 uniformly distributed wave-lengths in the spectral range [415.05 nm, 1008.10 nm]. Illumination setup, as described in \cite{picon2012real}, was specifically designed to reduce the specular reflections generated by the surface of the non-ferrous materials and to provide a homogeneous and even illumination that covers the wavelengths sensitive to the hyperspectral camera. The illumination system consists of a parabolic surface that uniformly distributes the lig
We introduce HSIRS, a large scale dataset of hyper-spectral images along with corresponding manually annotated segmentation maps for material characterization and classification based on spectral signature. Such data can be used to simulate any type of spectrometer and to train DNNs end-to-end for spectral reconstruction and image segmentation tasks. HSIRS features scenes containing real and fake (made of polyester, plastic or ceramic) food items with different backgrounds and scene layouts, some scenes contain also color checkers. Spectral bands are sequentially captured using a VariSpecTM tunable color filter and the scene is illuminated with 4 Halogen light sources.