Earth Science Deep Learning: Applications and Lessons Learned

Maskey, Manil; Ramachandran, Rahul; Miller, Jeffrey J.; Zhang, Jia; Gurung, Iksha

doi:10.1109/igarss.2018.8517346

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Using a convolutional neural network optimized by parameters has turned out to be a promising prediction method due to the higher accuracy rates. The CNN specified in Maskey et al [20] uses a training data set consisting of previous instances of hailstorms, along with their respective Next Generation Weather Radar (NEXRAD) images. The CNN classified the images where hail was present.…”

Section: Cnn Models Using Image Processing For Hailstorm Predictionmentioning

confidence: 99%

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Using Deep Learning and Machine Learning Methods to Diagnose Hailstorms in Large-Scale Thermodynamic Environments

Pulukool

Liu

2020

Sustainability

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Hailstorms have caused damages in billions of dollars to industrial, electronic, and mechanical properties such as automobiles, buildings, roads, and aircrafts, as well as life threats to crop and cattle populations, due to their hazardous nature. Hence, the relevance of predicting hailstorms in the future has significant scientific, economic, and societal benefits. However, climate models do not have adequate resolutions to explicitly resolve these subscale phenomena. One solution is to estimate the probability of these storms by using large-scale atmospheric thermodynamic environment variables from climate model outputs, but the existing methods only carried out experiments on small datasets limited to a region, country, or location and a large number of input features. Using one year of Tropical Rainfall Measuring Mission (TRMM) observations and European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim (ERA-Interim) reanalysis on a global scale, this paper develops two deep-learning-based models (an autoencoder and convolutional neural network (CNN)) as well as a machine learning approach (random forest) for hailstorm prediction by using only four attributes—convective potential energy, convective inhibition, 1–3 km wind shear, and warm cloud depth. In the experiments, the random forest approach produces the best hailstorm prediction performance compared to the other two methods.

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Section: Cnn Models Using Image Processing For Hailstorm Predictionmentioning

confidence: 99%

“…It was also pointed out that most of the hailstorm prediction techniques require efficient preprocessing of the data, which can be complicated. Hence, Maskey et al [20] utilized a CNN optimized by parameters that has high accuracy compared to the existing CNN models.…”

Section: Cnn Models Using Image Processing For Hailstorm Predictionmentioning

confidence: 99%

Using Deep Learning and Machine Learning Methods to Diagnose Hailstorms in Large-Scale Thermodynamic Environments

Pulukool

Liu

2020

Sustainability

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“…Rolnick et al [30] outline the wide range of machine learning applications to climate action, from enhancing efficiency in transport and infrastructure, to advancing the energy transition by improving renewable energy technologies. Other examples include using ML models for more accurate weather and climate forecasts [20] and applying deep learning to improve climate models [29] or advance earth science more broadly [24]. Meanwhile, the number of companies using AI to offer 'climate services' has surged for example through monitoring environmental risks (eg Ecometrica [16]), predicting extreme weather events (eg Jupiter [21]), or providing data to assess general climate risks (eg Acclimatise [4]).…”

Section: Ai For Climate Change and Food Securitymentioning

confidence: 99%

AI Ethics for Systemic Issues: A Structural Approach

van der Loeff,

Bassi,

Kapila

et al. 2019

Preprint

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The debate on AI ethics largely focuses on technical improvements and stronger regulation to prevent accidents or misuse of AI, with solutions relying on holding individual actors accountable for responsible AI development. While useful and necessary, we argue that this "agency" approach disregards more indirect and complex risks resulting from AI's interaction with the socio-economic and political context. This paper calls for a "structural" approach to assessing AI's effects in order to understand and prevent such systemic risks where no individual can be held accountable for the broader negative impacts. This is particularly relevant for AI applied to systemic issues such as climate change and food security which require political solutions and global cooperation. To properly address the wide range of AI risks and ensure 'AI for social good', agency-focused policies must be complemented by policies informed by a structural approach.

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“…These satellites capture large volumes of images and are used for various purposes. Processing and analysis of these images are useful for many applications such as the prediction of landslides, forest fires, rescue operations on post-disaster, post-disaster damage assessment, land use and land cover (LULC) classification, and many more [3]- [9]. With the advancement in space technology small satellites such as CubeSats, nanosatellites, and miniature satellites are launched which are less costly, less power consumable, and easy to manufacture with less weight and less time.…”

Section: Introductionmentioning

confidence: 99%

Comparative study on satellite based image encryption methods: a survey

Vasudevaiah,

Shivaswamy

2024

IJECE

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The availability of high-resolution satellite images increases with advancements in remote sensing technology. These satellite images are used in various earth observation applications such as disaster management, military applications, weather forecasting, land use and cover, and many more. Satellite images have large volumes stored in memory devices. These satellite images are transmitted to the ground station for processing and analysis. In these cases, images are vulnerable to privacy issues. As technology advances, onboard processing of satellite images using intelligent systems processes the images faster. A model such as field programmable gate arrays (FPGA) is used in onboard processing to process satellite images. However, images are susceptible to faults induced by harsh radiation environments in space. Encryption is one of the most assured methods to provide privacy to satellite images. Hence, encryption of satellite images during processing, storage, and transmission is the present rising demand. There are various encryption methods implemented using algorithms such as advanced encryption standard (AES), homomorphic, advanced encryption standard-counter (AES-CTR), and chaotic maps. Concurrent processing and encryption of images using MapReduce with Hadoop Framework perform the task faster. The focus of this paper is a comparative study of the various encryption methods used in recent years.

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Earth Science Deep Learning: Applications and Lessons Learned

Cited by 7 publications

References 9 publications

Using Deep Learning and Machine Learning Methods to Diagnose Hailstorms in Large-Scale Thermodynamic Environments

Using Deep Learning and Machine Learning Methods to Diagnose Hailstorms in Large-Scale Thermodynamic Environments

AI Ethics for Systemic Issues: A Structural Approach

Comparative study on satellite based image encryption methods: a survey

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