Pith. sign in

REVIEW 1 cited by

Hyperspectral City V1.0 Dataset and Benchmark

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1907.10270 v4 pith:LO6V77OI submitted 2019-07-24 cs.CV

Shaodi You , Erqi Huang , Shuaizhe Liang , Yongrong Zheng , Yunxiang Li , Fan Wang , Sen Lin , Qiu Shen
show 9 more authors
Xun Cao Diming Zhang Yuanjiang Li Yu Li Ying Fu Boxin Shi Feng Lu Yinqiang Zheng Robby T. Tan
This is my paper · ORCID
classification cs.CV
keywords datasetcityhyperspectralbackgroundbenchmarkmethodusagebriefly
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This document introduces the background and the usage of the Hyperspectral City Dataset and the benchmark. The documentation first starts with the background and motivation of the dataset. Follow it, we briefly describe the method of collecting the dataset and the processing method from raw dataset to the final release dataset, specifically, the version 1.0. We also provide the detailed usage of the dataset and the evaluation metric for submitted the result for the 2019 Hyperspectral City Challenge.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Optimization of DNN-based HSI Segmentation FPGA-based SoC for ADS: A Practical Approach

    cs.CV 2025-07 conditional novelty 6.0 of 10

    An iterative pruning plus quantization co-design compresses a U-Net for hyperspectral driving-scene segmentation to 1% of its parameters, enabling a 2.86x inference speed-up on an FPGA SoC with negligible accuracy loss.

Pith tools