AIMC Topic: Satellite Imagery
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Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China.
International journal of environmental research and public health
Jan 28, 2019
The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artiļ¬cial neural netw...
Integrating camera imagery, crowdsourcing, and deep learning to improve high-frequency automated monitoring of snow at continental-to-global scales.
PloS one
Dec 27, 2018
Snow is important for local to global climate and surface hydrology, but spatial and temporal heterogeneity in the extent of snow cover make accurate, fine-scale mapping and monitoring of snow an enormous challenge. We took 184,453 daily near-surface...
Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks.
Neural networks : the official journal of the International Neural Network Society
Jun 2, 2018
Environmental sustainability research is dependent on accurate land cover information. Even with the increased number of satellite systems and sensors acquiring data with improved spectral, spatial, radiometric and temporal characteristics and the ne...
Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery.
International journal of health geographics
May 9, 2018
BACKGROUND: Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative s...
Mapping of the corals around Hendorabi Island (Persian Gulf), using WorldView-2 standard imagery coupled with field observations.
Marine pollution bulletin
Mar 23, 2018
High spatial resolution WorldView-2 (WV2) satellite imagery coupled with field observations have been utilized for mapping the coral reefs around Hendorabi Island in the northern Persian Gulf. In doing so, three standard multispectral bands (red, gre...
Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States.
Proceedings of the National Academy of Sciences of the United States of America
Nov 28, 2017
The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic facto...
An alternative approach for estimating above ground biomass using Resourcesat-2 satellite data and artificial neural network in Bundelkhand region of India.
Environmental monitoring and assessment
Oct 20, 2017
Determination of above ground biomass (AGB) of any forest is a longstanding scientific endeavor, which helps to estimate net primary productivity, carbon stock and other biophysical parameters of that forest. With advancement of geospatial technology...
A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S.
PloS one
Aug 4, 2017
Shortwave solar radiation is an important component of the surface energy balance and provides the principal source of energy for terrestrial ecosystems. This paper presents a machine learning approach in the form of a random forest (RF) model for es...
Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations.
Computational intelligence and neuroscience
Dec 27, 2015
A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived "ocean color" (OC) data are used in this study becau...
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