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BH-Pools/Watertanks Datasets
BH-POOLS & BH-WATERTANKS Two separate datasets were assembled: BH-Pools and BH-WaterTanks, with annotated swimming pools and water tanks respectively. Both datasets consist of imagery from… Read More »BH-Pools/Watertanks Datasets
Multi-View Datasets
AiRound AiRound is composed of 11,753 images distributed among 11 classes, including: airport, bridge, church, forest, lake, river, skyscraper, stadium, statue, tower, and urban park.… Read More »Multi-View Datasets
Fully Convolutional Open Set Segmentation
In semantic segmentation knowing about all existing classes is essential to yield effective results with the majority of existing approaches. However, these methods trained in… Read More »Fully Convolutional Open Set Segmentation
BrazilDam Dataset
BrazilDAM Dataset BrazilDAM is a multi sensor and multitemporal dataset that consists of multispectral images of ore tailings dams throughout Brazil. Landsat 8 and Sentinel… Read More »BrazilDam Dataset
Bridge Dataset
Bridge Dataset This dataset is composed of 500 images each containing at least one bridge. This dataset has samples collected from different regions around the world,… Read More »Bridge Dataset
Fashion Dataset
Fashion Dataset This dataset is a composition of fashion images and associated tags and comments crawled from two fashion-related social networks, namely pose.com and chictopia.com.… Read More »Fashion Dataset
Region-based Annotated Child Pornography Dataset
This dataset is a private database that belongs to the Brazilian Federal Police. The paper “A Benchmark Methodology for Child Pornography Detection” describes the structure of the dataset. The aim of the dataset is to assess and compare the performance of child pornography detection methods.
Deep Semantic Segmentation of Mammographic Images
MIAS and INbreast are mammographic datasets for the detection and diagnosis of breast cancer. With the dawn of digital mammograms, one important preprocessing step for the… Read More »Deep Semantic Segmentation of Mammographic Images
Brazilian Cerrado-Savanna Scenes Dataset
The dataset is composed of 1,311 multi-spectral scenes extracted from images acquired by the RapidEye satellite sensors over the Serra do Cipó region, a mountainous and highly biodiverse and heterogenous landscape in southern-central Brazil mainly constituted of Cerrado-Savanna Vegetation.
From the 5 bands (blue, green, red, red edge and near infrared) that the images acquired by the RapidEye satellite sensors have, we have selected three (near-infrared, green, and red bands), which are the most useful and representative ones for discriminating vegetation areas.
It is a very challenging dataset given its high intraclass variance, caused by different spatial configurations and densities of the same vegetation type, as well as its high interclass similarity, given similar appearance of different types of vegetation species.