Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

A Quantitative Study of Java Software Buildability

Researchers, students and practitioners often encounter a situation when the build process of a third-party software system fails. In this paper, we aim to confirm this observation present mainly as anecdotal evidence so far. Using a virtual environment simulating a programmer's one, we try to fully automatically build target archives from the source code of over 7,200 open source Java projects. We found that more than 38% of builds ended in failure. Build log analysis reveals the largest portion of errors are dependency-related. We also conduct an association study of factors affecting build success

Pornography and Committed Relationships: How Pre-existing Factors within a Dyad Change the Effect of Pornography on Heterosexual and Homosexual Couples

Pornography and its effects have been the topic of debate for decades now. Much of the pornography debate centers on whether or not male pornography consumption is detrimental to men’s perception of, communication with, and treatment of women. As Charlotte Witt claims, “feminist debates over pornography originate in fundamental philosophical disagreement” (165). Many feminists and feminist groups critique pornography for its degradation of and violence towards women. Andrea Dworkin, a feminist against pornography, states that “the fact that pornography is widely believed to be ‘sexual representation’ or ‘depictions of sex’ emphasizes only that the valuation of women as low whores is widespread and that the sexuality of women is perceived as low and whorish in itself” (201). However, some couple therapists support pornography and prescribe its use to aid couples struggling with intimacy. It is primarily used as a way to bring the couple together through the intimacy created when viewing pornography together as well as to help the couple regain their sexual stimulation

Research Trends & Emerging Technologies for Genealogists

This study examines current research methods utilized by genealogists, and seeks to discover the impact of emerging tools and technologies on their information seeking needs and behaviors. When it became clear that there is a shortage of scholarly studies identifying the use of newer technologies (i.e. blogs, social media, and apps), an original survey for genealogists was created. Over four hundred genealogists were surveyed regarding their use of both traditional research methods (methods that have existed for many decades) and Internet/electronic resources, in order to demonstrate which new trends are emerging. The data from the survey might lessen the gap in current scholarly research. Technology is constantly changing, and the findings show which trends are currently being utilized the most by genealogists. The results indicate that genealogists are definitely using more technology to research their family trees. In fact, they adapt fairly quickly to new methods, relying heavily on technology and the Internet to conduct research and share information. Due to the ease of using technology, fewer and fewer genealogists rely on in-house visits to repositories to access original documents. The research concludes with a discussion on where the use of technology for genealogical research is headed, and what genealogists hope to accomplish by using new tools and technologies

Earth observing system. Data and information system. Volume 2A: Report of the EOS Data Panel

The purpose of this report is to provide NASA with a rationale and recommendations for planning, implementing, and operating an Earth Observing System data and information system that can evolve to meet the Earth Observing System's needs in the 1990s. The Earth Observing System (Eos), defined by the Eos Science and Mission Requirements Working Group, consists of a suite of instruments in low Earth orbit acquiring measurements of the Earth's atmosphere, surface, and interior; an information system to support scientific research; and a vigorous program of scientific research, stressing study of global-scale processes that shape and influence the Earth as a system. The Eos data and information system is conceived as a complete research information system that would transcend the traditional mission data system, and include additional capabilties such as maintaining long-term, time-series data bases and providing access by Eos researchers to relevant non-Eos data. The Working Group recommends that the Eos data and information system be initiated now, with existing data, and that the system evolve into one that can meet the intensive research and data needs that will exist when Eos spacecraft are returning data in the 1990s

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Dermatology residency selection criteria with an emphasis on program characteristics: a national program director survey.

Background. Dermatology residency programs are relatively diverse in their resident selection process. The authors investigated the importance of 25 dermatology residency selection criteria focusing on differences in program directors' (PDs') perception based on specific program demographics. Methods. This cross-sectional nationwide observational survey utilized a 41-item questionnaire that was developed by literature search, brainstorming sessions, and online expert reviews. The data were analyzed utilizing the reliability test, two-step clustering, and K-means methods as well as other methods. The main purpose of this study was to investigate the differences in PDs' perception regarding the importance of the selection criteria based on program demographics. Results. Ninety-five out of 114 PDs (83.3%) responded to the survey. The top five criteria for dermatology residency selection were interview, letters of recommendation, United States Medical Licensing Examination Step I scores, medical school transcripts, and clinical rotations. The following criteria were preferentially ranked based on different program characteristics: "advanced degrees," "interest in academics," "reputation of undergraduate and medical school," "prior unsuccessful attempts to match," and "number of publications." Conclusions. Our survey provides up-to-date factual data on dermatology PDs' perception in this regard. Dermatology residency programs may find the reported data useful in further optimizing their residency selection process

Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval