Christopher A. Ramezan

1. Remote sensing and Earth observation have become key components of contemporary geospatial science and decision support across public, private, and research sectors, with applications in environ...

As global fraud increases, the need for forensic accountants is evident. To clarify contemporary employer demand, we analyze 238 job postings collected from 31 countries using a hybrid approach combining manual analysis of position qualifications with three machine learning-based natural language processing (NLP) techniques—term frequency (TF)-inverse document frequency (IDF), latent Dirichlet allocation (LDA), and named entity recognition (NER)—to analyze position responsibilities. Results indicate that credentials such as certified fraud examiner (CFE) and certified public accountant (CPA), possession of a bachelor’s degree, and prior professional experience are dominant and foundational requirements. Analytical software expertise is desirable, while nearly a fourth of positions include work travel. Soft communication skills are highly valued. Responsibilities highlight the dual nature of the role, which combines rigorous analytical work with client-facing communication tasks to effectively write, translate, and present reports; provide expert testimony; and assist clients with investigative, legal, and business development tasks. Data Availability: Data are available upon request. JEL Classifications: J010; J240; M400.

As the demand for geospatial analytics continues to grow, geographic information systems (GIS) professionals are needed to build, operate, and maintain GIS technologies, data, and software to provide geospatial insights for modern industries and organizations. To best train the next generation of GIS professionals, an understanding of qualifications and requirements of GIS positions is needed. Thus, this work analyzes 508 GIS positions, grouped by position type (analysts, developers, educators, managers, specialists, technicians) to provide insights on key pre‐requisite requirements, such as education, experience, certifications, soft communication skills, programming skills, and knowledge of GIS or IT. In general, possession of a bachelor's degree in GIS, geography, or computer science, prior professional experience, and knowledge of GIS and IT software were common pre‐requisites for most GIS roles. Soft communication skills were also frequently desired for GIS roles. We also found that some position requirements tended to vary by position type, such as manager and developer roles requiring on average 5 years or higher prior experience, while analyst, specialist, and technician roles had much lower experience and education requirements. Higher education institutions and GIS training programs should note the desired requirements for GIS position types and continue to refine programs and develop pathways for success for aspiring GIS professionals.

A trained workforce is needed to protect operational technology (OT) and industrial control systems (ICS) within national critical infrastructure and critical industries. However, what knowledge, skills, and credentials are employers looking for in OT cybersecurity professionals? To best train the next generation of OT cybersecurity professionals, an understanding of current OT cybersecurity position requirements is needed. Thus, this work analyzes 100 OT cybersecurity positions to provide insights on key prerequisite requirements such as prior professional experience, education, industry certifications, security clearances, programming expertise, soft verbal and written communication skills, knowledge of OT frameworks, standards, and network communication protocols, and position travel. We found that OT cybersecurity roles are typically non-entry level, as experience was the most common requirement, and was required on 95% of analyzed positions. Possession of a bachelor’s degree or higher was required for 82% of positions, while industry certifications such as the Certified Information Systems Security Professional (CISSP) or the Global Information Assurance Certification (GIAC) Global Industrial Cyber Security Professional (GICSP) were listed on 64% of positions. Knowledge of OT or IT frameworks and standards and strong communication skills were listed on 48% of positions, while programming expertise, possession of the United States security clearance, and knowledge of OT or IT networking protocols were required for 18%, 24%, and 27% of positions, respectively. A work travel requirement was listed on 29% of positions. Individuals seeking to enter the OT cybersecurity field, and educational programs focusing on training OT cybersecurity professionals should prioritize obtaining experience, education, and certification, possessing strong communication skills, and knowledge of relevant OT and IT industry standards and frameworks.

Remote sensing analyses frequently use feature selection methods to remove non-beneficial feature variables from the input data, which often improve classification accuracy and reduce the computational complexity of the classification. Many remote sensing analyses report the results of the feature selection process to provide insights on important feature variable for future analyses. Are these feature selection results generalizable to other classification models, or are they specific to the input dataset and classification model they were derived from? To investigate this, a series of radial basis function (RBF) support vector machines (SVM) supervised machine learning land cover classifications of Sentinel-2A Multispectral Instrument (MSI) imagery were conducted to assess the transferability of recursive feature elimination (RFE)-derived feature sets between different classification models using different training sets acquired from the same remotely sensed image, and to classification models of other similar remotely sensed imagery. Feature selection results for various training sets acquired from the same image and different images widely varied on small training sets (n = 108). Variability in feature selection results between training sets acquired from different images was reduced as training set size increased; however, each RFE-derived feature set was unique, even when training sample size was increased over 10-fold (n = 1895). The transferability of an RFE-derived feature set from a high performing classification model was, on average, slightly more accurate in comparison to other classification models of the same image, but provided, on average, slightly lower accuracies when generalized to classification models of other, similar remotely sensed imagery. However, the effects of feature set transferability on classification accuracy were inconsistent and varied per classification model. Specific feature selection results in other classification models or remote sensing analyses, while useful for providing general insights on feature variables, may not always generalize to provide comparable accuracies for other classification models of the same dataset, or other, similar remotely sensed datasets. Thus, feature selection should be individually conducted for each training set within an analysis to determine the optimal feature set for the classification model.

Many issues can reduce the reproducibility and replicability of deep learning (DL) research and application in remote sensing, including the complexity and customizability of architectures, variable model training and assessment processes and practice, inability to fully control random components of the modeling workflow, data leakage, computational demands, and the inherent nature of the process, which is complex, difficult to perform systematically, and challenging to fully document. This communication discusses key issues associated with convolutional neural network (CNN)-based DL in remote sensing for undertaking semantic segmentation, object detection, and instance segmentation tasks and offers suggestions for best practices for enhancing reproducibility and replicability and the subsequent utility of research results, proposed workflows, and generated data. We also highlight lingering issues and challenges facing researchers as they attempt to improve the reproducibility and replicability of their experiments.

The size of the training data set is a major determinant of classification accuracy. Nevertheless, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algorithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data within the context of a geographic object-based image analysis (GEOBIA) approach. GEOBIA, in which adjacent similar pixels are grouped into image-objects that form the unit of the classification, offers the potential benefit of allowing multiple additional variables, such as measures of object geometry and texture, thus increasing the dimensionality of the classification input data. The six supervised machine-learning algorithms are support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), single-layer perceptron neural networks (NEU), learning vector quantization (LVQ), and gradient-boosted trees (GBM). RF, the algorithm with the highest overall accuracy, was notable for its negligible decrease in overall accuracy, 1.0%, when training sample size decreased from 10,000 to 315 samples. GBM provided similar overall accuracy to RF; however, the algorithm was very expensive in terms of training time and computational resources, especially with large training sets. In contrast to RF and GBM, NEU, and SVM were particularly sensitive to decreasing sample size, with NEU classifications generally producing overall accuracies that were on average slightly higher than SVM classifications for larger sample sizes, but lower than SVM for the smallest sample sizes. NEU however required a longer processing time. The k-NN classifier saw less of a drop in overall accuracy than NEU and SVM as training set size decreased; however, the overall accuracies of k-NN were typically less than RF, NEU, and SVM classifiers. LVQ generally had the lowest overall accuracy of all six methods, but was relatively insensitive to sample size, down to the smallest sample sizes. Overall, due to its relatively high accuracy with small training sample sets, and minimal variations in overall accuracy between very large and small sample sets, as well as relatively short processing time, RF was a good classifier for large-area land-cover classifications of HR remotely sensed data, especially when training data are scarce. However, as performance of different supervised classifiers varies in response to training set size, investigating multiple classification algorithms is recommended to achieve optimal accuracy for a project.