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20 results for “landfill”
A Construction Waste Landfill Dataset of Two Districts in Beijing, China from High Resolution Satellite Images
<p>CWLD_model project shows scripts and instructions on how to use this dataset to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
Semantic segmentation model of construction waste landfill based on high-resolution satellite images
<p>CWLD_model project shows scripts and instructions on how to use this dataset (<a href="../records/10686118">https://zenodo.org/records/10686118</a>) to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
Geophysical investigation of the Heferlbach landfill in Lower Austria
<p>This repository contains seismic electrical raw data collected at the Heferlbach landfill as well as the obtained joint inversion results along with python scripts for reading and plotting the resolved models.</p>
Landfill: An open dataset of code smells with public evaluation
<p>Code smells are symptoms of poor design and implementation choices that may hinder code comprehension and possibly increase the change- and fault-proneness of source code. Several techniques have been proposed in the literature for detecting code smells. These techniques are generally evaluated by comparing their accuracy on a set of detected candidate code smells against a manually-produced oracle. Unfortunately, such comprehensive sets of annotated code smells are not available in the literature, with only a few exceptions. This dataset provides 243 instances of five types of code smells identified from 20 open-source software projects. In particular, it contains a SQL file with the information concerning such instances and a zip file with their source code.</p>
Fig.2 in Forest Phytocenotic Changes In Impact Zone Of Municipal Vilnius County (Lithuania) Waste Landfill
Fig.2. Undergrowth density at different distances from the landfill.
Fig. 1 in Forest Phytocenotic Changes In Impact Zone Of Municipal Vilnius County (Lithuania) Waste Landfill
Fig. 1. The share of healthy trees in relation with distance from the landfill.
Supplementary Figures Chapter 5 - Peat capping: Natural capping of wet landfills by peat formation
<p>Supplementary Figures to Chapter 5 "Peat capping: Natural capping of wet landfills by peat formation" of PhD thesis from Ciska Overbeek, "Peat formation on a former landfill - Production and decomposition of aquatic pioneer vegetation". </p> <p>Published by Harpenslager et al in 2018 in Ecological Engineering 114: 146-153. <a href="https://doi.org/10.1016/j.ecoleng.2017.04.040">https://doi.org/10.1016/j.ecoleng.2017.04.040</a>. </p>
Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility testing and human health risks - Supplementary data
<p>This is a supplementary dataset to the paper:</p> <p>Hiller E., Faragó T., Kolesár M., Filová L., Mihaljevič M., Jurovič L., Demko R., Mchlica A., Štefánek J., Vítková M. (2024): Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility and human health risks. <em>Chemosphere</em> <strong>362</strong>, 142677. DOI: 10.1016/j.chemosphere.2024.142677</p> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks. The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
Data for: Of the first five US states with food waste bans, Massachusetts alone has reduced landfill waste
Open the record for dataset details and reuse information.
Seasonal landfill methane emissions driven by temperature and pressure
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Pilot-Scale Continuous Foam Fractionation for the Removal of Per- and Polyfluoroalkyl Substances (PFAS) from Landfill Leachate
<p>This is the supplementary data for the publication '<span>Pilot-Scale Continuous Foam Fractionation for the Removal of Per- and Polyfluoroalkyl Substances (PFAS) from Landfill Leachate' (</span>https://pubs.acs.org/doi/full/10.1021/acsestwater.2c00032)</p>
Eubank Landfill 2020
Eubank Landfill is a closed Municipal Waste Landfill in Albuquerque, NM Flown using a DJI Phantom 4 Pro V2.0 in November 2020 7122 Images Mean RMS Error of 0.022 ft. Processed in PIX4D Source: Objaverse 1.0 / Sketchfab
Geology and the Potential of Soil Contamination Due to Heavy Metals Around Banyuroto Landfill, Kulon Progo
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Alteration and progressive degradation of plastic waste in a polish operational landfill analysed over 60 years
<p>The dataset is presented as an Excel file, which includes both raw and synthetic data collected, processed, or used within the investigation. It supports the tables and images published in the manuscript. The dataset also includes data intended for machine learning (ML) modeling to estimate the total degradation time.</p>
Landfills
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Dataset for Unlocking the Mitigation Potential of Landfills as A Cost-Effective Solution for Climate Change
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Functional environmental genomics of a municipal landfill soil
GEO Series GSE37154. Folsomia candida. 32 samples. Type: Expression profiling by array.
Reintegrating Children Living at Landfills in Burundi Into Society by Means of a Comprehensive Family Program Addressing Maltreatment and Mental Health Symptoms
ClinicalTrials.gov study NCT03846154. IPD Sharing: NO. Countries: 1. Publications: 0.
Data underlying findings reported in "Potentially Toxic Elements (PTEs) and Ecological Risk at Waste Disposal Sites: An Analysis of Sanitary Landfills" (Plos One article)
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Metagenomic analysis revealed higher microbial and functional gene diversities in deep landfill
GEO Series GSE68712. Bacteria. 15 samples. Type: Other.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.