Validation of an integrated pedal desk and electronic behavior tracking platform

Background This study tested the validity of revolutions per minute (RPM) measurements from the Pennington Pedal Desk™. Forty-four participants (73 % female; 39 ± 11.4 years-old; BMI 25.8 ± 5.5 kg/m2 [mean ± SD]) completed a standardized trial consisting of guided computer tasks while using a pedal desk for approximately 20 min. Measures of RPM were concurrently collected by the pedal desk and the Garmin Vector power meter. After establishing the validity of RPM measurements with the Garmin Vector, we performed equivalence tests, quantified mean absolute percent error (MAPE), and constructed Bland–Altman plots to assess agreement between RPM measures from the pedal desk and the Garmin Vector (criterion) at the minute-by-minute and trial level (i.e., over the approximate 20 min trial period). Results The average (mean ± SD) duration of the pedal desk trial was 20.5 ± 2.5 min. Measures of RPM (mean ± SE) at the minute-by-minute (Garmin Vector: 54.8 ± 0.4 RPM; pedal desk: 55.8 ± 0.4 RPM) and trial level (Garmin Vector: 55.0 ± 1.7 RPM; pedal desk: 56.0 ± 1.7 RPM) were deemed equivalent. MAPE values for RPM measured by the pedal desk were small (minute-by-minute: 2.1 ± 0.1 %; trial: 1.8 ± 0.1 %) and no systematic relationships in error variance were evident by Bland–Altman plots. Conclusion The Pennington Pedal Desk™ provides a valid count of RPM, providing an accurate metric to promote usage

The role of citizen science in addressing grand challenges in food and agriculture research

The power of citizen science to contribute to both science and society is gaining increased recognition, particularly in physics and biology. Although there is a long history of public engagement in agriculture and food science, the term ‘citizen science’ has rarely been applied to these efforts. Similarly, in the emerging field of citizen science, most new citizen science projects do not focus on food or agriculture. Here, we convened thought leaders from a broad range of fields related to citizen science, agriculture, and food science to highlight key opportunities for bridging these overlapping yet disconnected communities/fields and identify ways to leverage their respective strengths. Specifically, we show that (i) citizen science projects are addressing many grand challenges facing our food systems, as outlined by the United States National Institute of Food and Agriculture, as well as broader Sustainable Development Goals set by the United Nations Development Programme, (ii) there exist emerging opportunities and unique challenges for citizen science in agriculture/food research, and (iii) the greatest opportunities for the development of citizen science projects in agriculture and food science will be gained by using the existing infrastructure and tools of Extension programmes and through the engagement of urban communities. Further, we argue there is no better time to foster greater collaboration between these fields given the trend of shrinking Extension programmes, the increasing need to apply innovative solutions to address rising demands on agricultural systems, and the exponential growth of the field of citizen science.This working group was partially funded from the NCSU Plant Sciences Initiative, College of Agriculture and Life Sciences ‘Big Ideas’ grant, National Science Foundation grant to R.R.D. (NSF no. 1319293), and a United States Department of Food and Agriculture-National Institute of Food and Agriculture grant to S.F.R., USDA-NIFA Post Doctoral Fellowships grant no. 2017-67012-26999.http://rspb.royalsocietypublishing.orghj2018Forestry and Agricultural Biotechnology Institute (FABI

Preface

[No abstract available

The role of citizen science in addressing grand challenges in food and agriculture research

The power of citizen science to contribute to both science and society is gaining increased recognition, particularly in physics and biology. Although there is a long history of public engagement in agriculture and food science, the term ‘citizen science’ has rarely been applied to these efforts. Similarly, in the emerging field of citizen science, most new citizen science projects do not focus on food or agriculture. Here, we convened thought leaders from a broad range of fields related to citizen science, agriculture, and food science to highlight key opportunities for bridging these overlapping yet disconnected communities/fields and identify ways to leverage their respective strengths. Specifically, we show that (i) citizen science projects are addressing many grand challenges facing our food systems, as outlined by the United States National Institute of Food and Agriculture, as well as broader Sustainable Development Goals set by the United Nations Development Programme, (ii) there exist emerging opportunities and unique challenges for citizen science in agriculture/food research, and (iii) the greatest opportunities for the development of citizen science projects in agriculture and food science will be gained by using the existing infrastructure and tools of Extension programmes and through the engagement of urban communities. Further, we argue there is no better time to foster greater collaboration between these fields given the trend of shrinking Extension programmes, the increasing need to apply innovative solutions to address rising demands on agricultural systems, and the exponential growth of the field of citizen science.This working group was partially funded from the NCSU Plant Sciences Initiative, College of Agriculture and Life Sciences ‘Big Ideas’ grant, National Science Foundation grant to R.R.D. (NSF no. 1319293), and a United States Department of Food and Agriculture-National Institute of Food and Agriculture grant to S.F.R., USDA-NIFA Post Doctoral Fellowships grant no. 2017-67012-26999.http://rspb.royalsocietypublishing.orghj2018Forestry and Agricultural Biotechnology Institute (FABI

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state‐of‐the‐art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water and natural hazard management are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined

A Spatial Semi-supervised Learning Method for Mining Multi-spectral Remote Sensing Imagery

Supervised learning, which is often used in land cover (thematic) classification of remote sensing imagery, has two limitations: first these techniques require large amounts of accurate training data to accurately estimate underlying statistical model parameters and secondly, the independent and identically distributed (i.i.d) assumptions made by these techniques do not hold true in the case of high-resolution satellite images. Recently, semi-supervised learning techniques that utilize large unlabeled training samples in conjunction with small labeled training data are becoming popular in machine learning, especially in text data mining. These techniques provide a viable solution to small training dataset problems; however, the techniques do not exploit spatial context. In this paper we explore methods that utilize unlabeled samples in supervised learning for classification of multi-spectral remote sensing imagery, while also taking into account the spatial context in the learning process. We extended the classical Expectation-Maximization (EM) technique to model spatial context via Markov Random Fields (MRF). We have conducted several experiments on real data sets and our classification procedure shows an improvement of 10% in overall classification accuracy. Further studies are necessary to assess the true potential and usefulness of this technique in varying geographic settings. Keywords: MAP, MLE, EM, Spatial Context, Auto-correlation, MRF, semi-supervised learning, mixture model

Semantics-Enabled Framework for Spatial Image Information Mining of Linked Earth Observation Data

Recent developments in sensor technology are contributing toward the tremendous growth of remote sensing (RS) archives (currently, at the petabyte scale). However, this data largely remain unexploited due to the current limitations in the data discovery, querying, and retrieval capabilities. This issue becomes exacerbated in disaster situations, where there is a need for rapid processing and retrieval of the affected areas. Furthermore, the retrieval of images based on the spatial configurations of affected regions [land use/cover (LULC) classes] in an image is important in disaster situations such as floods and earthquakes. The majority of existing Earth observation (EO) image information mining (IIM) systems does not consider the spatial relations among image regions during image retrieval (aka spatial semantic gap). In this work, we have specifically addressed two issues, i.e., explicit modeling of topological and directional relationships between image regions, and development of a resource description framework (RDF)-based spatial semantic graphs (SSGs). This enables more intuitive querying and reasoning on the archived data. A spatial IIM (SIIM) framework is proposed, which integrates a logic-based reasoning mechanism to extract the hidden spatial relationships (both topological and directional) and enables image retrieval based on spatial relationships. The system is tested using several spatial relations-based queries on the RS image repository of flood-affected areas to check its applicability in post flood scenario. Precision, recall, and F-measure metrics were used to evaluate the performance of the SIIM system, which showed good potential for spatial relations-based image retrieval

GX-Means: A model-based divide and merge algorithm for geospatial image clustering

AbstractOne of the practical issues in clustering is the specification of the appropriate number of clusters, which is not obvious when analyzing geospatial datasets, partly because they are huge (both in size and spatial extent) and high dimensional. In this paper we present a computationally effcient model-based split and merge clustering algorithm that incrementally finds model parameters and the number of clusters. Additionally, we attempt to provide insights into this problem and other data mining challenges that are encountered when clustering geospatial data. The basic algorithm we present is similar to the G-means and X-means algorithms; however, our proposed approach avoids certain limitations of these well-known clustering algorithms that are pertinent when dealing with geospatial data. We compare the performance of our approach with the G-means and X-means algorithms. Experimental evaluation on simulated data and on multispectral and hyperspectral remotely sensed image data demonstrates the effectiveness of our algorithm