mlsanityData to 7 Oct 2026

TAS-NIR: A VIS+NIR Dataset for Fine-grained Semantic Segmentation in Unstructured Outdoor Environments

arXiv 2212.09368 ↗First posted 19 Dec 2022cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

Vegetation Indices based on paired images of the visible color spectrum (VIS) and near infrared spectrum (NIR) have been widely used in remote sensing applications. These vegetation indices are extended for their application in autonomous driving in unstructured outdoor environments. In this domain we can combine traditional vegetation indices like the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) with Convolutional Neural Networks (CNNs) pre-trained on available VIS datasets. By laying a focus on learning calibrated CNN outputs, we can provide an approach to fuse known hand-crafted image features with CNN predictions for different domains as well. The method is evaluated on a VIS+NIR dataset of semantically annotated images in unstructured outdoor environments. The dataset is available at mucar3.de/iros2022-ppniv-tas-nir.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 10 of its 35 references that are in the map. First posted on arXiv 19 Dec 2022.

Builds on

10 of 35 references.

References · 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 0; 2026: 1.

5 papers.

  1. 2023What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentationcited by 24
  2. 2023Open-sourced Data Ecosystem in Autonomous Driving: the Present and Futurecited by 22
  3. 2024Excavating in the Wild: The GOOSE-Ex Dataset for Semantic Segmentationcited by 12
  4. 2024Pix2Next: Leveraging Vision Foundation Models for RGB to NIR Image Translationcited by 2
  5. 2026Texture-Shape Bias Balancing for Robust Synthetic-to-Real Semantic Segmentation in Automotive NIR Imagerycited by 0