Climate Change/Environmental
In the wake of marine oil exploration and transportation, the accidents of oil spills have occurred
frequently around the world, which leads to the severe pollution of the marine environment and the
huge damage of coastal species [1–6]. On April 20, 2010, the explosion of Deepwater Horizon oil
drilling platform led to a severe leakage. Million barrels of oil polluted the Gulf of Mexico with the
area of about 10,000 square kilometers [7, 8]. Due to this accident, the marine ecosystems, such as fish
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urrently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA), as a benchmark dataset for developing and evaluating Machine Learning (ML) algorithms capable of detecting Marine Debris.
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This dataset consists of 3500 images of beach litter and 3500 corresponding pixel-wise labelled images. Although performing such pixel-by-pixel semantic masking is expensive, it allows us to build machine-learning models that can perform more sophisticated automated visual processing. We believe this dataset may be of significance to the scientific communities concerned with marine pollution and computer vision, as this dataset can be used for benchmarking in the tasks involving the evaluation of marine pollution with various machine learning models.
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Tree planting has the potential to improve the livelihoods of millions of people as well as to support environmental services such as biodiversity conservation. Planting however needs to be executed wisely if benefits are to be achieved. We have developed the GlobalUsefulNativeTrees (GlobUNT) database to directly support the principles advocated by the ‘golden rules for reforestation’, including planting tree mixtures that maximize the benefits to local livelihoods and the diversity of native trees.
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As the harmful effects of climate change on human society increase, the analysis of abnormal weather is becoming an important issue. Therefore, this work provides the Korean weather dataset, including the anomaly score measurements by using seven different methods. In this dataset, seven types of weather data for each day in 64 Korean cities from 2010 to 2020 are provided by Weather Radar Center in Korea Meteorological Administration.
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Learning SAR Dataset for Wet Snow Detection - Full Analysis Version.
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Since meteorological satellites can observe the Earth’s atmosphere from a spatial perspective at a large scale, in this paper, a dust storm database is constructed using multi-channel and dust label data from the Fengyun-4A (FY-4A) geosynchronous orbiting satellite, namely, the Large-Scale Dust Storm database based on Satellite Images and Meteorological Reanalysis data (LSDSSIMR), with a temporal resolution of 15 minutes and a spatial resolution of 4 km from March to May of each year during 2020–2022.
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537 Views
This paper presents a bi-directional Long ShortTerm Memory (LSTM) model for the detection of landslides. Previous uses of machine learning in this setting have demonstrated its general potential, which necessitates the implementation of a suitable algorithm. Landslides are natural disasters that can cause significant destruction and disruption in the affected areas. Early detection is the key to minimizing the impact of landslides, so it is important to develop accurate and efficient models.
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Adverse climatic events like heat stress, floods, unseasonal rainfall, and droughts frequently hinder crop productivity. Long-term crop yield data plays a crucial role in food security planning. This study presents historical wheat yield data at the satellite pixel level from 2001 to 2019 in Uttar Pradesh, India. We use various satellite indicators to develop wheat yield models, including the normalized difference vegetation index and gridded weather data, such as precipitation, temperature, and evapotranspiration.
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550 Views
Social Media Big Dataset for Research, Analytics, Prediction, and Understanding the Global Climate Change Trends is focused on understanding the climate science, trends, and public awareness of climate change. The use of dataset for analytics of climate change trends greatly helps in researching and comprehending global climate change trends.
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