Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asia

• URL: http://arxiv.org/abs/2112.12966v1
• Date: Fri, 24 Dec 2021 06:44:55 GMT
• Title: Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asia
• Authors: Manmeet Singh, Bipin Kumar, Rajib Chattopadhyay, K Amarjyothi, Anup K Sutar, Sukanta Roy, Suryachandra A Rao, Ravi S. Nanjundiah
• Abstract summary: This survey focuses on the current problems in Earth systems science where machine learning algorithms can be applied. It provides an overview of previous work, ongoing work at the Ministry of Earth Sciences, Gov. of India, and future applications of ML algorithms to some significant earth science problems.
• Score: 0.12647816797166164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This survey focuses on the current problems in Earth systems science where machine learning algorithms can be applied. It provides an overview of previous work, ongoing work at the Ministry of Earth Sciences, Gov. of India, and future applications of ML algorithms to some significant earth science problems. We provide a comparison of previous work with this survey, a mind map of multidimensional areas related to machine learning and a Gartner's hype cycle for machine learning in Earth system science (ESS). We mainly focus on the critical components in Earth Sciences, including atmospheric, Ocean, Seismology, and biosphere, and cover AI/ML applications to statistical downscaling and forecasting problems.

Related papers

• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Ontologies are widely used for representing domain knowledge and meta data. One straightforward solution is to integrate statistical analysis and machine learning. Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• GeoAI in Social Science [0.9527350779226282]
  GeoAI, or geospatial artificial intelligence, is an exciting new area that leverages artificial intelligence (AI), geospatial big data, and massive computing power to solve problems with high automation and intelligence. This paper reviews the progress of AI in social science research, highlighting important advancements in using GeoAI to fill critical data and knowledge gaps.
  arXiv  Detail & Related papers  (2023-12-19T20:23:18Z)
• Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward [60.43248801101935]
  This article gives a bird's eye view of the essential scientific tools and approaches informing and supporting the transition from raw EO data to usable EO-based information. We cover the impact of (i) Computer vision; (ii) Machine learning; (iii) Advanced processing and computing; (iv) Knowledge-based AI; (v) Explainable AI and causal inference; (vi) Physics-aware models; (vii) User-centric approaches; and (viii) the much-needed discussion of ethical and societal issues related to the massive use of ML technologies in EO.
  arXiv  Detail & Related papers  (2023-05-15T07:47:24Z)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• Physical Computing for Materials Acceleration Platforms [81.09376948478891]
  We argue that the same simulation and AI tools that will accelerate the search for new materials, as part of the MAPs research program, also make possible the design of fundamentally new computing mediums. We outline a simulation-based MAP program to design computers that use physics itself to solve optimization problems. We expect to introduce a new era of innovative collaboration between materials researchers and computer scientists.
  arXiv  Detail & Related papers  (2022-08-17T23:03:54Z)
• Towards a Collective Agenda on AI for Earth Science Data Analysis [39.78763440312085]
  We aim to inspire researchers, especially the younger generations, to tackle these challenges for a real advance of remote sensing and the geosciences. In our declared agenda for AI on Earth sciences, we aim to inspire researchers, especially the younger generations, to tackle these challenges for a real advance of remote sensing and the geosciences.
  arXiv  Detail & Related papers  (2021-04-11T20:54:44Z)
• Applications of physics-informed scientific machine learning in subsurface science: A survey [64.0476282000118]
  Geosystems are geological formations altered by humans activities such as fossil energy exploration, waste disposal, geologic carbon sequestration, and renewable energy generation. The responsible use and exploration of geosystems are thus critical to the geosystem governance, which in turn depends on the efficient monitoring, risk assessment, and decision support tools for practical implementation. Fast advances in machine learning algorithms and novel sensing technologies in recent years have presented new opportunities for the subsurface research community to improve the efficacy and transparency of geosystem governance.
  arXiv  Detail & Related papers  (2021-04-10T13:40:22Z)
• Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods [1.9491825010518622]
  The AI/ML ThetaRay system is trained initially with Kepler exoplanetary data and validated with confirmed exoplanets. By the application of ThetaRay to 10,803 light curves of threshold crossing events (TCEs) produced by the TESS mission, we uncover 39 new exoplanetary candidates.
  arXiv  Detail & Related papers  (2021-02-20T12:28:39Z)
• Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources [0.0]
  This paper reviews the most important information fusion algorithms based on Machine Learning (ML) techniques for problems in Earth observation. Data-driven approaches, and ML techniques in particular, are the natural choice to extract significant information from this data deluge.
  arXiv  Detail & Related papers  (2020-12-07T13:35:08Z)
• A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence [48.67755344239951]
  We provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning. A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping. It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities.
  arXiv  Detail & Related papers  (2020-06-22T19:01:21Z)
• Incorporating Physical Knowledge into Machine Learning for Planetary Space Physics [0.0]
  We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetosphere. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine learning methods.
  arXiv  Detail & Related papers  (2020-06-02T20:31:29Z)

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.