mlsanityData to 7 Oct 2026

Enhancing Environmental Monitoring through Multispectral Imaging: The WasteMS Dataset for Semantic Segmentation of Lakeside Waste

arXiv 2407.17028 ↗First posted 24 Jul 2024cs.CV cs.AI cs.MMcited by 3

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Abstract (arXiv)

Environmental monitoring of lakeside green areas is crucial for environmental protection. Compared to manual inspections, computer vision technologies offer a more efficient solution when deployed on-site. Multispectral imaging provides diverse information about objects under different spectrums, aiding in the differentiation between waste and lakeside lawn environments. This study introduces WasteMS, the first multispectral dataset established for the semantic segmentation of lakeside waste. WasteMS includes a diverse range of waste types in lawn environments, captured under various lighting conditions. We implemented a rigorous annotation process to label waste in images. Representative semantic segmentation frameworks were used to evaluate segmentation accuracy using WasteMS. Challenges encountered when using WasteMS for segmenting waste on lakeside lawns were discussed. The WasteMS dataset is available at https://github.com/zhuqinfeng1999/WasteMS.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 28 of its 43 references that are in the map. First posted on arXiv 24 Jul 2024.

Builds on

28 of 43 references.

References · 28

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16 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025Domain adaptation of large language models for geotechnical applicationscited by 1
  2. 2025FusionSort: Enhanced Cluttered Waste Segmentation with Advanced Decoding and Comprehensive Modality Optimizationcited by 1
  3. 2024MSCrackMamba: Leveraging Vision Mamba for Crack Detection in Fused Multispectral Imagerycited by 0