…A total of 787 satellite images of size 256 × 256 are collected at a high resolution (HR) of 1.193 meters per pixel and hand tagged for built-up region segmentation using an online tool Label-Box.
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5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan Focus on different geographical environments between Urban and Rural Advance both semantic segmentation and domain adaptation tasks Three considerable challenges: Multi-scale objects Complex background samples Inconsistent class distributions Two contests are held on the Codalab: <b>LoveDA Semantic Segmentation
45 PAPERS • 1 BENCHMARK
…Each reconstruction has clean dense geometry, high resolution and high dynamic range textures, glass and mirror surface information, planar segmentation as well as semantic class and instance segmentation
280 PAPERS • 3 BENCHMARKS
We introduce ACDC, the Adverse Conditions Dataset with Correspondences for training and testing semantic segmentation methods on adverse visual conditions. ACDC supports two tasks: 1. standard semantic segmentation 2. uncertainty-aware semantic segmentation
23 PAPERS • 4 BENCHMARKS
We design an all-day semantic segmentation benchmark all-day CityScapes. It is the first semantic segmentation benchmark that contains samples from all-day scenarios, i.e., from dawn to night.
3 PAPERS • 1 BENCHMARK
REFUGE Challenge provides a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
13 PAPERS • 5 BENCHMARKS
SemanticUSL is a dataset for domain adaptation for LiDAR point cloud semantic segmentation. The dataset has the same data format and ontology as SemanticKITTI.
IDD is a dataset for road scene understanding in unstructured environments used for semantic segmentation and object detection for autonomous driving.
86 PAPERS • NO BENCHMARKS YET
…To gather the simulated dataset, we captured before and after flood pairs from 2000 viewpoints with the following modalities: non-flooded RGB image, depth map, segmentation map flooded RGB image, binary mask of the flooded area, segmentation map The camera was placed about 1.5 above ground, and has a field of view of 120 degree, and the resolution of the images is 1200 x 900.
2 PAPERS • 1 BENCHMARK
…Notably, deep learning methods are employed to obtain a semantic segmentation of aerial images.
1 PAPER • 1 BENCHMARK
…Activity labels Annotations of the experimental task activity the subject performed throughout the session, including instruction, rest, and active experiment segments. We label each segment of the active experiment as one of four possible n-back working memory intensity levels (0-back, 1-back, 2-back, or 3-back).
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