Geocoded data structures and their applications to Earth science investigations

A geocoded data structure is a means for digitally representing a geographically referenced map or image. The characteristics of representative cellular, linked, and hybrid geocoded data structures are reviewed. The data processing requirements of Earth science projects at the Goddard Space Flight Center and the basic tools of geographic data processing are described. Specific ways that new geocoded data structures can be used to adapt these tools to scientists' needs are presented. These include: expanding analysis and modeling capabilities; simplifying the merging of data sets from diverse sources; and saving computer storage space

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

Computer-aided processing of LANDSAT MSS data for classification of forestlands

There are no author-identified significant results in this report

Monitoring the defoliation of hardwood forests in Pennsylvania using LANDSAT

An automated system for conducting annual gypsy moth defoliation surveys using LANDSAT MSS data and digital processing techniques is described. A two-step preprocessing procedure was developed that uses multitemporal data sets representing forest canopy conditions before and after defoliation to create a digital image in which all nonforest cover types are eliminated or masked out of a LANDSAT image that exhibits insect defoliation. A temporal window for defoliation assessment was identified and a statewide data base was established. A data management system to interface image analysis software with the statewide data base was developed and a cost benefit analysis of this operational system was conducted

Data Mining Applications in Big Data

Data mining is a process of extracting hidden, unknown, but potentially useful information from massive data. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in data mining and technologies in Big Data. Challenges of data mining and data mining with big data are discussed. Some technology progress of data mining and data mining with big data are also presented

Application of an Artificial Neural Network for the CPT-based Soil Stratigraphy Classification

Subsurface soil profiling is an essential step in a site investigation. The traditional methods for in situ investigations, such as SPT borings and sampling, have been progressively replaced by CPT soundings since they are fast, repeatable, economical and provide continuous parameters of the mechanical behaviour of the soils. However, the derived CPT-based stratigraphy profiles might present noisy thin layers, and its soil type description might not reflect a textural-based classification (i.e. Universal Soil Classification System, USCS). Thus, this paper presents a straightforward artificial neural network (ANN) algorithm, to classify CPT soundings according to the USCS. Data for training the model have been retrieved from SPT-CPT pairs collected after the 2011 Christchurch earthquake in New Zealand. The application of the ANN to case studies show how the method is a cost-effective and time-efficient approach, but more input parameters and data are needed for increasing its performance

Ecology, biodiversity and mining: Science and solving the challenges

Mining has an impact on the ecology and biodiversity of an area, and this impact is required to be mitigated under regulatory requirements. The mitigation is achieved by applying measures, deeply rooted in scientific knowledge, of the functioning of the impacted (and rehabilitated) ecosystems and the biota supported by these ecosystems. This paper focusses on selected issues of the pre-mining ecology and biodiversity surveys and the scientific basis of regulatory requirements. Using Western Australian experience and drawing on comparisons from other countries, this paper addresses two challenges. The first is the classification of vegetation of mining tenements to inform on the variability of vegetation using a series of data-analytical experiments. The paper reveals flaws in the current practise aimed at recognition and description of plant communities in vegetation surveys. Concrete steps serving and enhancing the clarity and plausibility of regulatory tools (guidance manuals) are identified. It also advises on what we should do if we find discrepancies between the regulatory expectations and the level they reflect current scientific knowledge. The second is the comparative and spatial aspects of the identification of communities of conservation interest. The paper further identifies the missing vital information of the vegetation mapping procedures and briefly analyses the need for tools assisting assessment of conservation value of the vegetation classifications, including the building of a centralised vegetation database, formulation of vegetation and habitat classification systems, and construction of scientifically sound and ecologically informative vegetation maps at various scales of complexity