Climate Change/Environmental
An automatic waste classification system embedded with higher accuracy and precision of convolution neural network (CNN) model can significantly the reduce manual labor involved in recycling. The ConvNeXt architecture has gained remarkable improvements in image recognition. A larger dataset, called TrashNeXt, comprising 23,625 images across nine categories has been introduced in this study by combining and thoroughly analyzing various pre-existing datasets.
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208 Views
This dataset accompanies the IEEE IoT Journal paper titled "A Dual System IoT Strategy for Hyperlocal Spatial-Temporal Microclimate Monitoring in Urban Environments Using LoRa." It is intended for validating bespoke sensors against commercial sensors. The data were collected using two different types of sensors deployed at eight locations in East London, starting on August 1, 2023, and covering a period of one year.
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33 Views
CO2 Emissions Data Visualization Project – I Hug Trees
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81 Views
Monitoring glacier calving fronts is essential for understanding ice dynamics and their response to climate change. The present ice frontdatasets are limited to temporal resolution and precision, making it difficult to efficiently capture the rapid detailed changes in calving processes. In this study, we proposed a novel approach of calving front extraction derived from Sentinel-2 satellite images, integrated with samgeo and geemap modules based on Segment Anything Model (SAM) and Google Earth Engine (GEE) framework.
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32 Views
We introduce a dataset comprised of energy consumption data from smart meters in French households, capturing detailed, disaggregated time series for various home appliances. This dataset covers a six-month period with a one-minute sampling rate across five different households. The objective of this dataset is to support the development of models that learn disentangled representations of time series energy data, which can significantly enhance model generalization across both in-distribution and out-of-distribution scenarios.
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168 Views
A new small aerial flame dataset, called the Aerial Fire and Smoke Essential (AFSE) dataset, is created which is comprised of screenshots from different YouTube wildfire videos as well as images from FLAME2. Two object categories are included in this dataset: smoke and fire. The collection of images is made to mostly contain pictures utilizing aerial viewpoints. It contains a total of 282 images with no augmentations and has a combination of images with only smoke, fire and smoke, and no fire nor smoke.
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665 Views
Due to climate change, the Northwesterner Gilgit Baltistan's, Ghizer district is highly susceptible to glacial lake outburst floods (GLOFs). Nearly 24 GLOFs have occurred in this area in the last ∼200 years, demonstrating the growing recurrent nature of these incidents. Taking this into account, the assessment of risks associated with GLOFs was investigated in this study. All regional glacial lakes were identified in the first phase, and changes between 2000 and 2023 were mapped using moderate-resolution satellite images (Landsat).
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108 Views
Beijing, Hangzhou, Shenzhen, and Chongqing. For each city, a total of 4337 hourly AQI records from January 1, 2024, to June 29, 2024, were selected. In Beijing, the primary observation point was the Tiantan observatory, with auxiliary observation points at Wanshouxigong, Guanyuan, and Olympic Sports Center. Similarly, in Hangzhou, the main observation point was the City Government Building, and auxiliary observation points were Binjiang, Xisha, and Yunqi. In Shenzhen, the main observation point was Longgang, with auxiliary observation points at Overseas Chinese Town, Guanlan, and Xixiang.
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41 Views