89,420 research outputs found
Deep Learning Techniques for Geospatial Data Analysis
Consumer electronic devices such as mobile handsets, goods tagged with RFID
labels, location and position sensors are continuously generating a vast amount
of location enriched data called geospatial data. Conventionally such
geospatial data is used for military applications. In recent times, many useful
civilian applications have been designed and deployed around such geospatial
data. For example, a recommendation system to suggest restaurants or places of
attraction to a tourist visiting a particular locality. At the same time, civic
bodies are harnessing geospatial data generated through remote sensing devices
to provide better services to citizens such as traffic monitoring, pothole
identification, and weather reporting. Typically such applications are
leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes
Classifiers, Support Vector Machines, and decision trees. Recent advances in
the field of deep-learning showed that Neural Network-based techniques
outperform conventional techniques and provide effective solutions for many
geospatial data analysis tasks such as object recognition, image
classification, and scene understanding. The chapter presents a survey on the
current state of the applications of deep learning techniques for analyzing
geospatial data.
The chapter is organized as below: (i) A brief overview of deep learning
algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii)
Deep-learning techniques for Remote Sensing data analytics tasks (iv)
Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques
for RFID data analytics.Comment: This is a pre-print of the following chapter: Arvind W. Kiwelekar,
Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam, {\em Deep Learning
Techniques for Geospatial Data Analysis}, published in {\bf Machine Learning
Paradigms}, edited by George A. TsihrintzisLakhmi C. Jain, 2020, publisher
Springer, Cham reproduced with permission of publisher Springer, Cha
Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion
This is the publisher’s final pdf. The published article is copyrighted by the Public Library of Science and can be found at: http://www.plosone.org/home.action.Background: Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. \ud
\ud
Methodology and Principal Findings: A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. \ud
\ud
Conclusion and Significance: Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level
Njord: a fishing trawler dataset
Fish is one of the main sources of food worldwide. The commercial
fishing industry has a lot of different aspects to consider, ranging
from sustainability to reporting. The complexity of the domain also
attracts a lot of research from different fields like marine biology,
fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote sensing and
classification of fish from images or videos using machine learning
or other analysis methods attracts growing attention. Surprisingly,
little work has been done that considers what is happening on
board the fishing vessels. On the deck of the boats, a lot of data and
important information are generated with potential applications,
such as automatic detection of accidents or automatic reporting of
fish caught. This paper presents Njord, a fishing trawler dataset
consisting of surveillance videos from a modern off-shore fishing
trawler at sea. The main goal of this dataset is to show the potential
and possibilities that analysis of such data can provide. In addition to the data, we provide a baseline analysis and discuss several
possible research questions this dataset could help answer
- …