Multimodal material segmentation (MCubeS) dataset contains 500 sets of images from 42 street scenes. The dataset provides annotated ground truth labels for both material and semantic segmentation for every pixel.
11 PAPERS • 1 BENCHMARK
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
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The RIT-18 dataset was built for the semantic segmentation of remote sensing imagery.
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The LIB-HSI dataset was created to develop deep learning methods for segmenting building facade materials. The images were captured using a Specim IQ hyperspectral camera.
…corrected (e.g. calibrated) radiographic projections, their tomographic reconstructions (based on 37 projections of 256 detector pixels into a 100×100 pixel CT image per slice) and the corresponding set of segmentation