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Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition
This paper describes our approach to the DSTL Satellite Imagery Feature ...
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Measuring Human and Economic Activity from Satellite Imagery to Support City-Scale Decision-Making during COVID-19 Pandemic
The COVID-19 outbreak forced governments worldwide to impose lockdowns a...
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Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico
Mapping the spatial distribution of poverty in developing countries rema...
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Detecting the Presence of Vehicles and Equipment in SAR Imagery Using Image Texture Features
In this work, we present a methodology for monitoring man-made, construc...
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Deep Learning for Recognizing Mobile Targets in Satellite Imagery
There is an increasing demand for software that automatically detects an...
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Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Data
In this paper, the authors aim to combine the latest state of the art mo...
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Using Deep Learning and Satellite Imagery to Quantify the Impact of the Built Environment on Neighborhood Crime Rates
The built environment has been postulated to have an impact on neighborh...
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Using Satellite Imagery for Good: Detecting Communities in Desert and Mapping Vaccination Activities
Deep convolutional neural networks (CNNs) have outperformed existing object recognition and detection algorithms. On the other hand satellite imagery captures scenes that are diverse. This paper describes a deep learning approach that analyzes a geo referenced satellite image and efficiently detects built structures in it. A Fully Convolution Network (FCN) is trained on low resolution Google earth satellite imagery in order to achieve end result. The detected built communities are then correlated with the vaccination activity that has furnished some useful statistics.
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