scholarly journals The rise of big data and supporting technologies in keeping watch on the world's forests

2020 ◽  
Vol 22 (1) ◽  
pp. 129-141
Author(s):  
R. Taylor ◽  
C. Davis ◽  
J. Brandt ◽  
M. Parker ◽  
T. Stäuble ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Language Processing ◽  
Lower Cost ◽  
Forest Policy ◽  
Forest Products ◽  
Comprehensive Analysis ◽  
Geospatial Data ◽  
User Friendly ◽  
Analysis Of Social Networks

Technology-driven advances in the gathering, processing and delivery of big data are making it easier to monitor forests and make informed decisions over their use and management. This paper first describes how innovations in remote sensing and cloud computing are enabling generation of geospatial data more often, at lower cost and in more user-friendly formats. Second, it describes the evolution of systems and technologies to trace forest products, and agricultural commodities linked to deforestation, from source to final use. Third, it reviews the potential for emerging data mining technologies such as natural language processing, web scraping and computer vision to support forest policy analysis and augment geospatial data gathered through remote sensing. The paper gives examples of how these technologies are being used and may be used in the future to monitor and respond to deforestation, fire and natural disasters, improve governance by enabling faster and more comprehensive analysis of social networks, policies and regulations, and increase traceability and transparency within supply chains.

scholarly journals Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study

10.2196/16862 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e16862
Author(s):  
Curtis Lee Petersen ◽  
Ryan Halter ◽  
David Kotz ◽  
Lorie Loeb ◽  
Summer Cook ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Older Adults ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Design Process ◽  
Language Processing ◽  
User Centered Design ◽  
The Mean ◽  
User Friendly ◽  
User Centered

Background Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process. Objective This study aims to develop a mobile app for a novel device through a user-centered design process with both older adults and clinicians while exploring whether data collected through this process can be used in NLP and sentiment analysis Methods Through a user-centered design process, we conducted semistructured interviews during the development of a geriatric-friendly Bluetooth-connected resistance exercise band app. We interviewed patients and clinicians at weeks 0, 5, and 10 of the app development. Each semistructured interview consisted of heuristic evaluations, cognitive walkthroughs, and observations. We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. Sentiment was defined as the sum of positive and negative words (each word with a +1 or −1 value). To assess utility, we used quantitative assessment questionnaires—System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). Finally, we used multivariate linear models—adjusting for age, sex, subject group (clinician vs patient), and development—to explore the association between sentiment analysis and SUS and USE outcomes. Results The mean age of the 22 participants was 68 (SD 14) years, and 17 (77%) were female. The overall mean SUS and USE scores were 66.4 (SD 13.6) and 41.3 (SD 15.2), respectively. Both patients and clinicians provided valuable insights into the needs of older adults when designing and building an app. The mean positive-negative sentiment per sentence was 0.19 (SD 0.21) and 0.47 (SD 0.21) for patient and clinician interviews, respectively. We found a positive association with positive sentiment in an interview and SUS score (ß=1.38; 95% CI 0.37 to 2.39; P=.01). There was no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics. Conclusions Involving patients and clinicians allowed us to design and build an app that is user friendly for older adults while supporting compliance. This is the first analysis using NLP and usability questionnaires in the quantification of user-centered design of technology for older adults.

scholarly journals PROCESSING BIG REMOTE SENSING DATA FOR FAST FLOOD DETECTION IN A DISTRIBUTED COMPUTING ENVIRONMENT

2017 ◽  
Vol XLII-4/W2 ◽  
pp. 137-138
Author(s):  
A. Olasz ◽  
D. Kristóf ◽  
B. Nguyen Thai ◽  
M. Belényesi ◽  
R. Giachetta
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Remote Sensing Data ◽  
Development Project ◽  
Geospatial Data ◽  
Computing Environment ◽  
Sensing Data ◽  
Space Industry ◽  
Spatio Temporal ◽  
Flood Detection

The Earth observation (EO) missions of the space agencies and space industry (ESA, NASA, national and commercial companies) are evolving as never before. These missions aim to develop and launch next-generation series of satellites and sensors and often provide huge amounts of data, even free of charge, to enable novel monitoring services. The wide geospatial sector is targeted to handle new challenges to store, process and visualize these geospatial data, reaching the level of Big Data by their volume, variety, velocity, along with the need of multi-source spatio-temporal geospatial data processing. Handling and analysis of remote sensing data has always been a cumbersome task due to the ever-increasing size and frequency of collected information. This paper presents the achievements of the IQmulus EU FP7 research and development project with respect to processing and analysis of geospatial big data in the context of flood and waterlogging detection.

scholarly journals Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
Curtis Lee Petersen ◽  
Ryan Halter ◽  
David Kotz ◽  
Lorie Loeb ◽  
Summer Cook ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Older Adults ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Design Process ◽  
Language Processing ◽  
User Centered Design ◽  
The Mean ◽  
User Friendly ◽  
User Centered

BACKGROUND Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process. OBJECTIVE This study aims to develop a mobile app for a novel device through a user-centered design process with both older adults and clinicians while exploring whether data collected through this process can be used in NLP and sentiment analysis METHODS Through a user-centered design process, we conducted semistructured interviews during the development of a geriatric-friendly Bluetooth-connected resistance exercise band app. We interviewed patients and clinicians at weeks 0, 5, and 10 of the app development. Each semistructured interview consisted of heuristic evaluations, cognitive walkthroughs, and observations. We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. Sentiment was defined as the sum of positive and negative words (each word with a +1 or −1 value). To assess utility, we used quantitative assessment questionnaires—System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). Finally, we used multivariate linear models—adjusting for age, sex, subject group (clinician vs patient), and development—to explore the association between sentiment analysis and SUS and USE outcomes. RESULTS The mean age of the 22 participants was 68 (SD 14) years, and 17 (77%) were female. The overall mean SUS and USE scores were 66.4 (SD 13.6) and 41.3 (SD 15.2), respectively. Both patients and clinicians provided valuable insights into the needs of older adults when designing and building an app. The mean positive-negative sentiment per sentence was 0.19 (SD 0.21) and 0.47 (SD 0.21) for patient and clinician interviews, respectively. We found a positive association with positive sentiment in an interview and SUS score (ß=1.38; 95% CI 0.37 to 2.39; <i>P</i>=.01). There was no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics. CONCLUSIONS Involving patients and clinicians allowed us to design and build an app that is user friendly for older adults while supporting compliance. This is the first analysis using NLP and usability questionnaires in the quantification of user-centered design of technology for older adults.

scholarly journals Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China

2021 ◽  
Vol 13 (20) ◽  
pp. 4086
Author(s):  
Guoqing Zhi ◽  
Bin Meng ◽  
Juan Wang ◽  
Siyu Chen ◽  
Bin Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Spatial Analysis ◽  
Urban Planning ◽  
Language Processing ◽  
Temporal Trends ◽  
New Paradigm ◽  
Geographic Space ◽  
Low Sensitivity ◽  
The Relationship

Urban heatwaves increase residential health risks. Identifying urban residential sensitivity to heatwave risks is an important prerequisite for mitigating the risks through urban planning practices. This research proposes a new paradigm for urban residential sensitivity to heatwave risks based on social media Big Data, and describes empirical research in five megacities in China, namely, Beijing, Nanjing, Wuhan, Xi’an and Guangzhou, which explores the application of this paradigm to real-world environments. Specifically, a method to identify urban residential sensitive to heatwave risks was developed by using natural language processing (NLP) technology. Then, based on remote sensing images and Weibo data, from the perspective of the relationship between people (group perception) and the ground (meteorological temperature), the relationship between high temperature and crowd sensitivity in geographic space was studied. Spatial patterns of the residential sensitivity to heatwaves over the study area were characterized at fine scales, using the information extracted from remote sensing information, spatial analysis, and time series analysis. The results showed that the observed residential sensitivity to urban heatwave events (HWEs), extracted from Weibo data (Chinese Twitter), best matched the temporal trends of HWEs in geographic space. At the same time, the spatial distribution of observed residential sensitivity to HWEs in the cities had similar characteristics, with low sensitivity in the urban center but higher sensitivity in the countryside. This research illustrates the benefits of applying multi-source Big Data and intelligent analysis technologies to the understand of impacts of heatwave events on residential life, and provide decision-making data for urban planning and management.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

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