Enhancing Environmental Monitoring through Multispectral Imaging: The WasteMS Dataset for Semantic Segmentation of Lakeside Waste
- URL: http://arxiv.org/abs/2407.17028v2
- Date: Thu, 25 Jul 2024 05:23:24 GMT
- Title: Enhancing Environmental Monitoring through Multispectral Imaging: The WasteMS Dataset for Semantic Segmentation of Lakeside Waste
- Authors: Qinfeng Zhu, Ningxin Weng, Lei Fan, Yuanzhi Cai,
- Abstract summary: This study introduces WasteMS, the first multispectral dataset established for the semantic segmentation of lakeside waste.
We implemented a rigorous annotation process to label waste in images.
Challenges encountered when using WasteMS for segmenting waste on lakeside lawns were discussed.
- Score: 1.4604369887945985
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Plant detection from ultra high resolution remote sensing images: A Semantic Segmentation approach based on fuzzy loss [2.6489824612123716]
We tackle the challenge of identifying plant species from ultra high resolution (UHR) remote sensing images.
Our approach involves introducing an RGB remote sensing dataset, characterized by millimeter-level spatial resolution.
First experimental results obtained on both our UHR dataset and a public dataset are presented, showing the relevance of the proposed methodology.
arXiv Detail & Related papers (2024-08-31T17:40:17Z) - SpectralWaste Dataset: Multimodal Data for Waste Sorting Automation [46.178512739789426]
We present SpectralWaste, the first dataset collected from an operational plastic waste sorting facility.
This dataset contains labels for several categories of objects that commonly appear in sorting plants.
We propose a pipeline employing different object segmentation architectures and evaluate the alternatives on our dataset.
arXiv Detail & Related papers (2024-03-26T18:39:38Z) - Solid Waste Detection, Monitoring and Mapping in Remote Sensing Images: A Survey [0.8499685241219366]
Improperly managed landfills contaminate soil and groundwater via infiltration rainwater, posing threats to both animals and humans.
Traditional landfill identification approaches, such as on-site inspections, are time-consuming and expensive.
Earth Observation (EO) satellites, equipped with an array of sensors and imaging capabilities, have been providing high-resolution data for several decades.
arXiv Detail & Related papers (2024-02-14T10:24:04Z) - Spectral Analysis of Marine Debris in Simulated and Observed
Sentinel-2/MSI Images using Unsupervised Classification [0.0]
This study uses Radiative Transfer Model (RTM) simulated data and data from the Multispectral Instrument (MSI) of the Sentinel-2 mission in combination with machine learning algorithms.
The results indicate that the spectral behavior of pollutants is influenced by factors such as the type of polymer and pixel coverage percentage.
These insights can guide future research in remote sensing applications for detecting marine plastic pollution.
arXiv Detail & Related papers (2023-06-26T18:46:47Z) - VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting [61.52419223232737]
In industrial waste sorting, one of the biggest challenges is the extreme diversity of the input stream.
We present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting.
arXiv Detail & Related papers (2023-03-26T21:38:38Z) - Evaluation of the potential of Near Infrared Hyperspectral Imaging for
monitoring the invasive brown marmorated stink bug [53.682955739083056]
The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops.
The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
arXiv Detail & Related papers (2023-01-19T11:37:20Z) - SatMAE: Pre-training Transformers for Temporal and Multi-Spectral
Satellite Imagery [74.82821342249039]
We present SatMAE, a pre-training framework for temporal or multi-spectral satellite imagery based on Masked Autoencoder (MAE)
To leverage temporal information, we include a temporal embedding along with independently masking image patches across time.
arXiv Detail & Related papers (2022-07-17T01:35:29Z) - A Multi-purpose Real Haze Benchmark with Quantifiable Haze Levels and
Ground Truth [61.90504318229845]
This paper introduces the first paired real image benchmark dataset with hazy and haze-free images, and in-situ haze density measurements.
This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene.
A subset of this dataset has been used for the Object Detection in Haze Track of CVPR UG2 2022 challenge.
arXiv Detail & Related papers (2022-06-13T19:14:06Z) - ZeroWaste Dataset: Towards Automated Waste Recycling [51.053682077915546]
We present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste.
This dataset contains over1800fully segmented video frames collected from a real waste sorting plant.
We show that state-of-the-art segmentation methods struggle to correctly detect and classify target objects.
arXiv Detail & Related papers (2021-06-04T22:17:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.