The accelerated change in our planet due to human activities has led to grand societal challenges including health crises, intensified extreme weather events, food security, environmental injustice, etc. Digital twin systems combined with emerging technologies such as artificial intelligence and edge computing provide opportunities to support planning and decision-making to address these challenges. Digital twins for Earth systems (DT4ESs) are defined as the digital representation of the complex integrated Earth system including both natural processes and human activities. They have the potential to enable a diverse range of users to explore what-if scenarios across spatial and temporal scales to improve our understanding, prediction, mitigation, and adaptation to grand societal challenges. The 4th NOAA AI Workshop convened around 100 members who are developing or interested in participating in the development of DT4ES to discuss a shared community vision and path forward on fostering a future ecosystem of interoperable DT4ES. This paper summarizes the workshop discussions around DT4ES. We first defined the foundational features of a viable digital twins for Earth system that can be used to guide the development of various use cases of DT4ES. Finally, we made practical recommendations for the community on different aspects of collaboration in order to enable a future ecosystem of interoperable DT4ES, including equity-centered use case development, community-driven investigation of interoperability for DT4ES, trust-oriented co-development, and developing a community of practice.

Locality Sensitive Hashing (LSH) is an index-based data structure that allows spatial item retrieval over a large dataset. The performance measure, ?, has significant effect on the computational complexity and memory space requirement to create and store items in this data structure respectively. The minimization of ? at a specific approximation factor c, is dependent on the load factor, ?. Over the years,?=4has been used by researchers. In this paper, we demonstratethat the choice of?=4does not guarantee low computational complexity and low memory space of the data structure under the LSH scheme. To guarantee low computational complexity and low memory space, we propose?=5. Experiments on the Defense Meteorological Satellite Program imagery datasethave shown that?=5saves more than 75%on memory space; cuts the computational complexity by more than 70%andanswers query two times faster on the average compared to that of?=4.

The World Data Center for Geophysics in Boulder, Colorado is hosted by the National Geophysical Data Center (NGDC). NGDC's vision is to be the world's leading provider of geophysical and environmental data, information, and products. NGDC's mission is to provide long-term scientific data stewardship for geophysical data, ensuring quality, integrity, and accessibility. Faced with ever expanding data volumes and types of data, NGDC is developing more innovative techniques for science data stewardship based in part on data mining and fuzzy logic. Use of these techniques will allow NGDC to more effectively provide data stewardship for its own scientific data archives and perhaps the broader World Data System.

Locality Sensitive Hashing (LSH) is an index-based data structure that allows spatial item retrieval over a large dataset.The performance measure, ρ, has significant effect on the computational complexity and memory space requirement to create and store items in this data structure respectively.The minimization of ρ at a specific approximation factor c, is dependent on the load factor, α.Over the years, = 4has been used by researchers.In this paper, we demonstratethat the choice of = 4does not guarantee low computational complexity and low memory space of the data structure under the LSH scheme.To guarantee low computational complexity and low memory space, we propose = 5.Experiments on the Defense Meteorological Satellite Program imagery datasethave shown that = 5saves more than 75%on memory space; cuts the computational complexity by more than 70%andanswers query two times faster on the average compared to that of = 4.

In this paper, an evaluation technique based on several image feature attributes along with image classifications is investigated. Furthermore, a semi-supervised technique based on support vector machine (SVM) for image classification and a Locality Sensitive Hashing (LSH) based searching algorithm to search for similarity of satellite imagery is presented. Given a query image, the goal is to retrieve matching images in the database based on the shape features extracted from satellite imagery data. The experimental results demonstrate superior results based on shape features which provide a better classification accuracy using both support vector machine and the semi-supervised hashing search methods.

Data mining is a valuable tool in meteorological applications. Properly selected data mining techniques enable researchers to process and analyze massive amounts of data collected by satellites and other instruments. Large spatial-temporal datasets can be analyzed using different linear and nonlinear methods. The Self-Organizing Map (SOM) is a promising tool for clustering and visualizing high dimensional data and mapping spatial-temporal datasets describing nonlinear phenomena. We present results of the application of the SOM technique in regions of interest within the European re-analysis data set. The possibility of detecting climate change signals through the visualization capability of SOM tools is examined.

In this paper, a semi-supervised technique based on support vector machine (SVM) for image classification and a Locality Sensitive Hashing (LSH) based searching algorithm to search for similarity of satellite imagery is presented. Given a query image, the goal is to retrieve matching images in the database based on the shape features extracted from satellite imagery data. The experimental results demonstrate superior results based on shape features which provide a better classification accuracy using both support vector machine and the semi-supervised hashing search methods.

This paper presents the use of the Low Memory Locality Sensitive Hashing (LMLSH) technique operating in Euclidean space to build a data structure for the Defense Meteorological Satellite Program (DMSP) satellite imagery database. The LMLSH technique finds satellite image matches in sublinear search time. The texture feature vectors of the images are extracted using pyramid-structured wavelet transform coupled with Gaussian central moment technique. These feature vectors and families of hash functions, drawn randomly and independently from a Gaussian distribution, are used to build hash tables. Given a query, the hash tables are used to pull out the best matches to that query and this is done in a sublinear search time complexity. When tested, our algorithm has proven to be approximately twenty six times faster than the Linear Search (LS) algorithm. In addition, the LMLSH algorithm searches about two percent of the entire database randomly to find the possible matches to any given query without loss of accuracy compared to the absolute best matches returned by its LS counterpart.

Shape descriptors have been used frequently as features to characterize an image for classification and image retrieval tasks. The problem of recognizing classes of objects in images is important for annotation and indexing of Satellite image databases. In this paper, a comparison between shape and texture features for classification is presented. The classification is based on Support Vector Machine (SVM) learning. SVM classifier can be learned from training data of relevance images and irrelevance images marked by users. Using the classifier, the system can retrieve more images relevant to the query in the database efficiently. The goal is to build an accurate and fast query-by-example using content based image retrieval based on the information extracted from satellite image data. We have investigated and described various feature extraction methods relevant to our work in this paper. The experimental results demonstrate that using the shape features give a better classification accuracy than that of the texture features.1 2

This paper demonstrates the use of the Locality Sensitive Hashing technique operating in Euclidean metric space to build a data structure for Defense Meteorological Satellite Program (DMSP) satellite imagery database. Due to the high dimensionality of these images, their texture feature vectors are used. These features are extracted using pyramidal wavelet decomposition coupled with Gaussian central moments. Families of hash functions are drawn randomly and independently from a Gaussian distribution to create hash tables for these texture feature vectors of the images. The hash tables and the families of hash functions are then used to find similar satellite image matches to any query image in sublinear search time. When tested, our algorithm has proven to be about thirty three times faster than the linear search algorithm. In addition, our algorithm searches less than two percent of the entire database on the average to find the possible similar image matches to any given query without loss of accuracy. 1 2

This paper demonstrates the use of modified Locality Sensitive Hashing (mLSH) technique with Euclidean distance space to build a data structure for Defense Meteorological Satellite Program (DMSP) satellite imagery database that can be used to find similar satellite image matches in sublinear search time. Given the texture feature vectors of the images extracted using Gaussian central moments of wavelet edges after multi-resolution decomposition, a one-time linked-list hash table is created. A family of hash functions is drawn randomly and independently from a Gaussian distribution with mean zero and a standard deviation, d (i.e. dimensionality of the image feature vectors) to create the hash table. When tested, our algorithm has proved to be at least twenty times faster than the linear search algorithm. In addition, the algorithm ensures that the percentage of the entire database searched to find possible matches to any given query falls below ten percent.(1) (2)

The recent Heliophysics Virtual Observatory (VxO) effort involves the development of separate observatories with a low overlap in physical domain or area of scientific specialization and a high degree of overlap in metadata management needs. VxOware is a content and metadata management system. While it is intended for use by a VxO specifically, it can also be used by any entity that manages structured metadata. VxOware has many features of a content management system and extensively uses the W3C recommendations for XML (Extensible Markup Language), XQuery (XML Query), and XSLT (Extensible Style Sheet Language Transformations). VxOware has features such as system and user administration, search, user-editable content, version tracking, and a wiki. Besides virtual observatories, the intended user-base of VxOware includes a group or an instrument team that has developed a directory structure of data files and would like to make this data, and its associated metadata, available in the virtual observatory network. One of the most powerful features of VxOware is the ability to link any type of object in the observatory to other objects and the ability for every object to be tagged.

A mixture of feature extraction (FE) and a Locality Sensitive Hashing (LSH) based searching algorithm to search for similarity in satellite imagery is presented. The goal is to build an accurate and fast query-by-example using content based image retrieval based on the information extracted from satellite image data. We have investigated and described various feature extraction methods relevant to our work in this paper. The experimental results demonstrate satisfactory retrieval efficiency based on the proposed model. The results show the effectiveness of our approach.

The area lit by anthropogenic visible-near infrared emissions (i.e., lights) has been estimated for 21 countries using night-time data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). The area lit is highly correlated to gross domestic product and electric power consumption. Significant outliers exist in the relation between area lit and population. The results indicate that the local level of economic development must be factored into the apportionment of population across the land surface based on DMSP-OLS observed lights.